electrical energy demand in mechanical machining processes

Electrical Energy Demand in Mechanical Machining Processes

A thesis submitted to The University of Manchester for the degree of

Doctor of Philosophy (PhD)

in the Faculty of Engineering and Physical Sciences

2014

Vincent Aizebeoje Balogun

School of Mechanical, Aerospace and Civil Engineering

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(Intentionally left blank)

iii

Table of Contents

List of Figures ix

List of Tables xiii

List of Nomenclature xiv

List of Abbreviations xviii

Abstract xx

List of Publications xxi

Declaration xxii

Copyright Statements xxiii

Dedication xxiv

Acknowledgements xxv

CHAPTER 1 1

Introduction 1

1.1 Manufacturing Sustainability and Resource efficiency 1

1.2 Global Energy Consumption in Manufacturing 2

1.3 Energy Demand in Machining processes 6

1.4 Aim and Objectives 7

1.5 Thesis Outline 8

CHAPTER 2 11

Literature Review 11

2.1 Energy Demand of Machine Tools 11

2.2 Use Phase energy consumption approach 14

2.2.1 Direct energy demand 14

iv

2.2.2 Energy footprint- Direct and embodied energy 22

2.3 Specific cutting energy approach 23

2.4 Online monitoring approach 29

CHAPTER 3 36

Experimental Strategy 36

3.1 Introduction 36

3.2 Machine tool 36

3.2.1 The MHP lathe 36

3.2.2 Takisawa milling machine 37

3.2.3 Mikron HSM 400 high speed machining centre 39

3.3 Measuring Equipments 40

3.3.1 Fluke 345 Clamp meter 40

3.3.2 ProgRes® microscope camera 41

3.3.3 Leica DM2500M Microscope 42

3.4 Workpiece materials 44

3.5 Cutting tools 45

3.6 Measurement of tool wear 45

3.7 Experimental setup for machining tests 46

CHAPTER 4 48

Modelling of direct energy requirements in mechanical machining processes 48

4.1 Abstract 48

4.2 Introduction 49

v

4.3 Machine Tool States and Proposed Improvements 49

4.4 Research Motivation 56

4.4.1 New Improved Model for Direct Electrical Energy Requirement in

Machining 57

4.4.2 Experimental Investigation 57

4.5 Results and Discussions 58

4.5.1 Energy consumption for machine modules and auxiliary units 58

4.5.2 Tool Change and Spindle speed- power characteristics 63

4.5.3 Effect of spindle speed on energy required by a DC motor driven

MAC-V2 Takisawa Milling Machine 64

4.5.4 Development of an improved and new energy model for milling processes 70

4.6 Validation of Direct Energy Model during Milling processes 71

4.7 Conclusion 72

CHAPTER 5 75

Impact of un-deformed chip thickness on specific energy in mechanical

machining processes 75

5.1 Abstract 75

5.2 Introduction 76

5.3 The Wider Importance of Specific Energy Data 79

5.4 Size effect in machining 80

5.5 Aim and Objective 82

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5.6 Modelling and Experimental setup 82

5.6.1 Research Methodology 82

5.6.2 Cutting Test Details 83

5.6.3 Influence of varying cutting parameters on power demand during

mechanical machining processes 86


5.7.1 Specific energy and size effect 101

5.8 Conclusions 103

CHAPTER 6 106

Improving the integrity of specific cutting energy coefficients for energy

demand modelling 106

6.1 Abstract 106

6.2 Introduction 107

6.2.1 Research aim and motivation 109

6.3 Research Strategy and Experimental Details 109

6.3.1 Research Strategy and Procedure 109

6.3.2 Experimental Details – Milling Tests 110

6.3.3 Experimental Details – Turning Tests 111


6.4.1 The effect of chip thickness on specific cutting energy 111

6.4.2 The effect of nose radius on specific cutting energy 115

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6.4.3 The effect of cutting environment on specific cutting energy 117

6.4.4 The effects of tool wear on specific cutting energy 119

6.5 Conclusions 122

CHAPTER 7 125

Specific energy based evaluation of machining efficiency 125

7.1 Abstract 125

7.2 Introduction 126

7.2.1 The Total Specific Energy and Chip Morphology 126

7.2.2 Process mechanisms in mechanical machining operations 128

7.3 Research aim and Objective 131

7.4 Experimental strategy and set up 131

7.4.1 Swept angle optimisation and their influence on specific ploughing in

milling processes 131

7.4.2 Estimation of the specific ploughing energy 140

7.4.3 Proposed analysis of the Specific Ploughing Energy 144

7.5 Conclusion 150

CHAPTER 8 151

Direct electrical energy demand in Fused Deposition Modelling 151

8.1 Abstract 151

8.2 Introduction – Layered Manufacturing Technologies 151

8.2.1 Fused Deposition Modelling 153

8.2.2 Research Aim 154

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8.3 Energy Demand in Fused Deposition Modelling 155

8.3.1 Energy States of Fused Deposition Modelling Machine 155

8.3.2 New Framework for direct energy requirements in FDM 158

8.3.3 Benchmarking of 3 different FDM technologies 159

8.4 Energy Demand for 3D printing versus Machining 164

8.5 Conclusions 168

CHAPTER 9 170

Conclusions and recommendations for future work 170

9.1 Conclusions 170

9.2 Major Research Contributions to Knowledge 175

9.3 Recomendations 176

REFERENCES 178

APPENDIX A Workpiece materials properties 178

APPENDIX B Cutting tool geometry of SOMT 060204 insert 190

APPENDIX C Process window (recommended machining parameters) on

somt 060204-hq 191

APPENDIX D Taguchi design of experiments for milling AISI 1045 steel alloy 192

APPENDIX E Experimental design to analyze specific energy for AISI 1045

steel alloy 193

APPENDIX F Tool Path View on DepoCAM Software for test piece 241

APPENDIX G ISO NC code blocks for surface cleaning generated on

Depocam software 198

ix

List of Figures

Figure 1.1: Sustainability Pillars (adapted from) [3] 1

Figure 1.2: World electricity consumption by sectors [6] 3

Figure 1.3: UK final energy consumption by main industrial groups [7] 4

Figure 1.4: World total energy consumption [6] 5

Figure 2.1: Energy used as a function of production rate for an automobile

production machining line adapted from Gutowski et al. [14] 15

Figure 2.2: Energy consumption of the machining process on PL700 machining

centre [36]. 19

Figure 2.3: Software architecture for temporal analysis of energy used in

manufacturing adapted from Vijayaraghavan and Dornfeld [22]. 30

Figure 2.4: Architecture of OEEM system adapted from Hu et al. [38]. 32

Figure 2.5: Basic event graph model for an energy consumption cycle adapted

from He et al. [9] 33

Figure 3.1: MHP Lathe 37

Figure 3.2: Takisawa Milling Machine 38

Figure 3.3: Mikron HSM 400 machining centre. 39

Figure 3.4: Fluke 345 Clamp Meter 40

Figure 3.5: ProgRes® microscope workstation 42

Figure 3.6: Leica DM2500M Microscope 43

Figure 3.7: Cutting edge radius measurement under Leica DM2500M Microscope 44

Figure 3.8: Sample flank wear observed under optical microscope 46

Figure 4.1: Machine tool electrical energy consumption estimation model 50

Figure 4.2: Basic and ready states power relationship 60

Figure 4.3: Non-cutting power consumption distribution of the MHP MDSI CNC

Open Lathe machine 61

Figure 4.4: MAC-V2 Takisawa Milling Machine auxiliary units power demand 62

Figure 4.5: Mikron HSM 400 high speed machining, auxiliary units power demand 63

Figure 4.6: Power-Speed Characteristics of a MAC-V2 Takisawa Milling Machine

tool and 3 zones for energy profile 66

Figure 4.7: MAC-V2 Takisawa Milling Machine no load power- spindle

speed characteristic in Zone A to 1500 rpm 67

x


speed characteristic in Zone B to 5000 rpm 68


speed characteristic in Zone C to 5500 rpm 69

Figure 4.10: Total Power Consumption Trend for Machining Tool paths 70

Figure 5.1: Key Process variable ranking for power demand in machining of

AISI 1045 steel alloy 88

Figure 5.2: Evaluation of specific cutting energy coefficient at 0.01 mm/tooth

for aluminium AW6082-T6 alloy 91

Figure 5.3: Evaluation of specific cutting energy coefficient at 0.01 mm/tooth for



for titanium 6Al-4V alloy 92










for AISI 1045 steel alloy 96



Figure 5.11: Specific cutting energy model of aluminium AW6082-T6 alloy 98

Figure 5.12: Specific cutting energy model of AISI 1045 steel alloy 98

Figure 5.13: Specific cutting energy model of titanium 6Al-4V alloy 99

Figure 5.14: Specific energy comparison for aluminium AW6082-T6 alloy,

AISI 1045 steel alloy and titanium 6Al-4V alloy 100

Figure 5.15: Specific energy size effect in machining of aluminium AW6082-T6

alloy 102

Figure 5.16: Specific energy size effect in machining of AISI 1045 steel alloy 102

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Figure 5.17: Specific energy size effect in machining titanium 6Al-4V alloy 103

Figure 6.1: Determination of specific energy coefficient. 112

Figure 6.2: Specific cutting energy variation with feed per tooth in milling


Figure 6.3: Specific cutting energy variation with un-deformed chip thickness in

milling AISI 1045 steel alloy 114

Figure 6.4: Specific energy comparison for 0.4 mm, 0.8 mm and 1.2 mm nose

radius tools in milling of AISI 1045 116

Figure 6.5: Specific energy demand for dry and flood cutting environment of


Figure 6.6: Optical microscope view of flank wear land 120

Figure 6.7: Specific energy coefficient increases with flank wear during

turning operation of EN8 steel alloy 121

Figure 6.8: Effect of cutting time on k during a turning operation of EN8 steel

alloy 122

Figure 7.1: Effect of un-deformed chip thickness ratio to the cutting edge

radius in orthogonal cutting adapted from [72] 129

Figure 7.2: Power –Material removal rate graph at 18.20 Swept angle 133





Figure 7.7: Optimum swept angle 137

Figure 7.8: Cutter engagement with workpiece. 138

Figure 7.9: Impact of size effect on Specific cutting energy for dry cutting


Figure 7.10: Shear energy estimation of AISI 1045 steel alloy 145

Figure 7.11: Shear energy estimation of aluminium AW6082-T6 alloy 145

Figure 7.12: Shear energy estimation of titanium 6Al-4V alloy 146

Figure 7.13: Ploughing energy variations with process parameter for AISI 1045

steel alloy 149

Figure 8.1: Power-time curve for Stratasys Dimension SST FDM machine

building from room temperature 155

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Figure 8.2: Power-time curve for Stratasys Dimension SST FDM machine

building from room temperature 157

Figure 8.3: A simple model fabricated on 3 FDM machines to study energy

demand 159

Figure 8.4: From left Dimension SST FDM, Dentford Inspire D290 and PP3DPP 161

Figure 8.5: Detailed view of low a cost FDM machine model PP3DPP 161

Figure 8.6: Power-time curve for Stratasys Dimension SST machine building

from room temperature. 162

Figure 8.7: Power-time plot for Dentford Inspire D290 machine building

from room temperature 163

Figure 8.8: Power-time plot for PP3DP machine building from room temperature 163

Figure 8.9: Power profile end-milling 9000 mm3 on Mikron HSM 400

Machining centre 166

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List of Tables

Table 3.1: Average cutting edge radius measured under Leica DM2500M

Microscope 44

Table 4.1: Machine tool contribution to electrical energy demand 51

Table 4.2: A summary of other models for direct energy requirements in

machining 53

Table 4.3: A summary of specific energy models in machining 56

Table 4.4: Workpiece type and process parameters 65

Table 4.5: Power and total energy demand estimation of machine

tools under investigation 72

Table 5.1: Global specific energy models found in literature combining both

basic and tip energy 78

Table 5.2: Models of specific cutting pressure 81

Table 5.3: Cutting parameters for milling trials 84

Table 5.4: Cutting tool geometry 85

Table 5.5: Taguchi L9 Experimental Design and Responses 87

Table 5.6: Effect ranking based on Minitab 16 analysis 89

Table 5.7: Experimental values of k at different un-deformed chip thickness h 97

Table 7.1: Cutting tool geometry 132

Table 7.2: Cutting parameters for AISI 1045 steel alloy 133

Table 7.3: Specific energy coefficient data for AISI 1045 steel alloy obtained

from tests 136

Table 7.4: Workpiece materials and cutting parameters for milling trials 141

Table 7.5: Experimental specific energy coefficient values 143

Table 8.1: FDM Machines investigated 160

Table 8.2: Parameters for milling on Mikron HSM 400 Machining centre 165

Table 8.3: Energy benchmarking FDM versus mechanical milling 167

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List of Nomenclature

Notation Description Units

ae Width of cut mm

ap Depth of cut mm

fz Chip load or feed per tooth mm/tooth

h Un-deformed chip thickness µm

hm Minimum chip thickness µm

havg Average un-deformed chip thickness µm

hmax Maximum un-deformed chip thickness µm

rpm Revolutions per minute /min

re Tool edge radius µm

t2, tc, Tm,

tcutting

Cutting time s

Vc Cutting velocity m/min

vf Feed velocity mm/min

α Effective rake angle deg

Ø Swept angle of cut deg

φopt Optimum swept angle deg

Øs Shear angle deg

Q, z Material removal rate mm3/s

P0 Basic power demand W

.

v Material removal rate mm

3/s

k, Esp Specific cutting energy Jmm-3

Ks Specific cutting pressure N/mm2

ke Specific energy Jmm-3

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Ke Specific area energy Jmm-2

kAl Specific cutting energy of aluminium AW6082-T6 alloy Jmm-3

ks Specific cutting energy of AISI 1045 steel alloy Jmm-3

kTi Specific cutting energy of titanium 6AL-4V alloy Jmm-3

kt Total specific cutting energy Jmm-3

kf Specific friction energy Jmm-3

kp Specific ploughing energy Jmm-3

ks Specific shearing energy Jmm-3

P1 Constant power W

Pb Basic power demand W

Pr Ready power demand W

Ps Spindle power demand W

p2, pcut, pc Cutting power W

P3 Spindle and table power consumption W

pavg Average total power W

pair Air cutting power W

pm Operating state power of spindle transmission module W

ptool, Ptc Tool change power W

pcool Coolant power W

pi Power of ith-axis of feed motor W

Pva Value adding power demand W

T1 Non-cutting time s

T2 Cycle time s

T3, tms Spindle speed acceleration time s

tb Running time at basic state s

tr Running time at ready state s

∆t Processing time s

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t1 Set up time s

t3, ttc Tool change time s

tfei Feed motor acceleration time s

T Tool life s

tva Value adding process time s

Topt-c Optimum tool-life s

yE Embodied energy per cutting edge of the cutting tool J

E, Etotal Total energy consumption J

Espindle Spindle energy consumption J

Efeed Axis feed energy consumption J

E3, Etool, Etc Tool change energy consumption J

Ecool Coolant pump energy consumption J

Efix Fixed energy consumption J

Eprocess Energy demand of physical process of machine tool J

Eperipheral Energy demand of auxiliary units and to overcome efficiency loses J

E1 Idle cutting J

E2, Etip Cutting energy J

E4 Embodied energy of tool J

E5 Embodied energy of material J

Eb, Ebasic Basic state energy demand J

Er, Eready Ready state energy demand J

em Specific material printing energy Jmm-3

VR Volumetric manufacturing rate mm3/s

α Cutting velocity exponent -

C, β, A, B Constant depending on workpiece and cutting tool geometry -

x Specific energy exponent -

yc Tool cost per cutting edge £

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t3 Tool change time s

Fv Cutting force N

Fp Thrust force N

Vb Horizontal band speed m/s

Vf Vertical feed speed m/s

Lcut Horizontal length of cut m

Achip Chip cross-sectional area m2

η Machine tool efficiency -

b Steady-state specific energy Jmm-3

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List of Abbreviations

Notation Description

APT Automatic programming tool

BAU Business as usual

CAD Computer aided design

CAM Computer aided manufacturing

CED Cumulative energy demand

CES Carbon emission signature

CIRP The International Academy for Production Engineering

CNC Computer numerical control

CO2PE Cooperative effort in process emission

DUKES Digest of United Kingdom energy statistics

EEI Energy efficiency index

EIA Energy information administration

HSS High speed steel

IEA International energy agency

MQL Minimum quantity lubrication

MRR Material removal rate

NC Numerical controls

OEEM Online energy monitoring system

PVD Physical vapour deposition

RPM Revolution per minute

SCE Specific cutting energy

TiN Titanium nitride

TiAlN Titanium Aluminium Nitride

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VBA Visual basic application

WC/C Tungsten carbide with carbon

WEO World energy outlook

AISI American Iron and Steel Institute

TiAlCr Titanium aluminium chromide

TiSi Titanium silicate

NaOH Sodium hydroxide

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Abstract Rising carbon dioxide emissions present a global grand challenge due to their impact on climate.

Power generation is one of the major sources of CO2 emissions especially when carbon based fuel

such as coal is used. Hence, the electricity that is used in homes and in manufacturing industry has

an environmental burden attributable to CO2 emissions when it was generated at the power stations.

In the UK on average, industry consumed 292 TWh of the electrical energy according to 2012

statistics. The rising cost of electricity in the UK coupled with the demand for eco-friendly

consumer products requires a better understanding of energy demand in manufacture.

In manufacturing, mechanical machining is one of the most widely used processes that consumed

on average 38 TWh. This amounted to 13% of the average UK industrial energy demand and the

reduction of energy intensity in this process is an area of current and urgent focus. In order to

control electrical energy usage in mechanical machining, it is essential to understand electrical

energy demand by machine tools and associated processes. This requires the development of

mathematical models to predict electrical energy demand. The models will support selection of

optimum machining process parameters to reduce direct energy demand and associated carbon

footprint.

Literatures reviewed indicate that energy demand modelling in machining was in its infancy and

the integrity of electrical energy data needed to be significantly improved. In particular a number of

energy studies had ignored the impact of feedrate, cutting velocity, depth of cut and tooling. It was

further observed that where specific energy values were used these were assumed constant

irrespective of the thickness of materials to be removed. The motivation for this research work was

to improve the integrity of electrical energy demand modelling in mechanical machining

addressing current limitations.

Based on electrical energy monitoring in mechanical machining, the energy demand for machining

processes was characterised. Building on the literature review and the concepts of “Basic and Tip”

energy, a new and improved energy model was developed which addressed a number of limitations

and omissions from existing models. The modelling of Tip energy is based on a specific energy and

material removal rate. Having discovered that the impact of chip thickness had not been considered

before in modelling specific energy a follow-on study undertook fundamental modelling of the

specific energy as a function of chip thickness. This led to new generic equations for specific

energy in machining. These models were developed based on machining of 3 common engineering

materials. Furthermore, to raise the practical value of the models and data, the effect of tool wear

on energy demand was studied and this was used to develop an improved understanding of the

evolution of specific energy with tool wear. By linking the cutting mechanism to specific energy,

the use of specific energy coefficients as a surrogate for defining energy efficient machining

conditions was identified and is proposed in this thesis. The impact of machine tools on energy

demand was investigated in a cooperative study between UK and Singapore. This enabled

quantification of the impact of machine tools on energy efficiency and the net result on carbon

dioxide footprint when both machine tool energy demand and national carbon emission signatures

are considered.

The research work provides significant advances in energy demand modelling, presenting new

specific energy data for machining three different workpiece materials and 2 generic and novel

methodologies and equations for (i) energy demand in machining, (ii) the effect of chip thickness

on specific energy. It also for the first time suggests a unique methodology for defining and

benchmarking the energy efficiency of cutting based on specific energy range. The energy models

and data presented in the thesis provide a foundation and possible input for developing software for

energy smart machining. This can be pursued with industrial partners providing a route for

exploitation.

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List of Publications

1. Balogun, V. A., Mativenga, P. T., Modelling of direct energy requirements in

mechanical machining processes, Journal of Cleaner Production, 2013; 41: 179-

186.

2. Balogun, V. A., Aramcharoen, A., Mativenga, P. T., Chuan, S. K., Impact of

Machine Tools on the Direct Energy and Associated Carbon Emissions for a

Standardized NC Toolpath, in: Re-engineering Manufacturing for Sustainability,

Springer, 2013: 197-202.

3. Balogun, V. A., Mativenga, P. T., Impact of un-deformed chip thickness on specific

energy in mechanical machining processes, Journal of Cleaner Production, 2014;

69: 260-268.

4. Balogun, V. A., Kirkwood N. D., Mativenga, P. T., Direct Electrical Energy

Demand in Fused Deposition Modelling, CIRP LCE Norway 2014, accepted for

publication.

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Declaration

No portion of the work referred to in the thesis has been submitted in support of an

application for another degree or qualification of this or any other university or other

institute of learning.

xxiii

Copyright Statements

1. The author of this thesis (including any appendices and/or schedules to this thesis)

owns any copyright in it (the “Copyright”) and the author has given The University

of Manchester certain rights to use such Copyright, including for administrative

purposes.

2. Copies of this thesis, either in full or in extracts and whether in hard or electronic

copy, may be made only in accordance with the Copyright, Designs and patent Act

1988 (as amended) and regulations issued under it or, where appropriate, in

accordance with licensing agreements which the University has from time to time.

This page must form part of any such copies made.

3. The ownership of certain Copyright, patents, designs, trademarks and other

intellectual property (the “Intellectual Property”) and any reproductions of

copyright works in the thesis, for example graphs and tables (“Reproductions”),

which may be described in this thesis, may not be owned by the author and may be

owned by the third parties. Such Intellectual Property and Reproductions the prior

written permissions of the owner(s) of the relevant Intellectual Property Rights

and/or Reproductions

4. Further information on the conditions under which disclosure, publication and

commercialization of this thesis, the Copyright and any Intellectual Property or

Reproductions described in it may take place is available in the University IP

Policy (see:

http://www.campus.manchester.ac.uk/medialibrary/policies/intelectualproperty), in

any relevant thesis restrictions declarations deposited in the University Library, The

University Library’s regulations (see:

http://www.manchester.ac.uk/library/aboutus/regulations) and in The University’s

policy on presentation of thesis.

http://www.campus.manchester.ac.uk/medialibrary/policies/intelectualproperty

http://www.manchester.ac.uk/library/aboutus/regulations

xxiv

Dedication

To God is the Glory.

xxv

Acknowledgements

My special thanks go to my supervisor Professor Paul Mativenga for his valuable guidance

and support as the research progresses. The progress made so far was due to his

encouragement and support of which without it, the progress would have been hampered.

My thanks are also dedicated to all my friends and colleagues at the University of

Manchester, especially in the School of Mechanical, Aerospace and Civil Engineering

(MACE) for the ongoing support, discussions and debate I had with them. I would like to

also appreciate the efforts of the workshop technicians Richard and Stuart for their

assistance in conducting some of the machining experiments.

Finally, my special thanks to my father the late Mr. Richard Gideon Balogun, my mother

Mrs. Mabel Gilohi Balogun and to all members of my family especially my beloved wife

Mrs. Mabel Ebele Balogun and my children Emmanuel, Theresa, Mary and Peter for all

their unquantifiable and numerous supports. God bless you all in Jesus name. Amen!

1

CHAPTER 1

INTRODUCTION

1.1 Manufacturing Sustainability and Resource efficiency

The United Nations World Commission on Environment and Development defined

sustainable development as a process of meeting the basic needs of all and giving all the

opportunity to satisfy their aspirations for a better and prosperous life [1]. Based on this

definition and as reported in literature [2] sustainable developments can be grouped into

three pillars. The three sustainability pillars are economy, social responsibility and the

environment as illustrated in Figure 1.1.

Figure 1.1: Sustainability Pillars (adapted from) [3]

Chapter 1 Introduction

2

The concentric nature of the circles is important. Figure 1.1 shows that all economic and

social needs should all be met with an environmental sustainability framework. In the same

way the economic activity is there to address and meet society needs. In addition to

providing positive economic and social impact, manufacturing businesses should engage in

activities that positively impact on the ecosystems for example ensuring availability of

fresh water, clean air, productive land and robust biodiversity. Industry should have

comprehensive plans for reducing the negative impact of their production or services on

the environment.

1.2 Global Energy Consumption in Manufacturing

Manufacturing is an industrial production process of tangible goods which entails the input

of raw materials in combination with machines, human resources, energy and output of

products from the synthesis of the inputs. Manufacturing processes have been reported to

be energy intensive and as a result, they have high environmental impact [4]. Dang et al.

[5] reported that manufacturing industries consumed 37% of world total electrical energy

generated in 2006. The Energy Information Administration (EIA) [6] reported that 42.6%

of the world total electrical energy was consumed by the industries in 2011 as depicted in

Figure 1.2. This Figure is an indication of increase of electrical energy consumption trend

within the sector from 1971 to 2011.


3

Figure 1.2: World electricity consumption by sectors [6]

In the United Kingdom UK, Digest of UK Energy Statistics’ (DUKES) [7] reported that in

2012, industry consumed on average 17.9% (292 TWh) of the total energy consumption in

the UK. Energy consumption associated with machine tools and accessories (i.e. metal

products, machinery and equipment) on average accounted for 38 TWh. This amounted to

13% of the average UK industrial energy consumption as shown in Figure 1.3.

Mil

lio

n t

on

nes

of

oil

eq

uiv

ale

nt

Industry


4

Figure 1.3: UK final energy consumption by main industrial groups [7]

It is clear that the manufacturing industries are a significant electrical energy consumer

globally and in the United Kingdom. The World Energy Outlook, WEO-2008 [8],

estimated that without any new policy, world primary energy demand will grow by 1.6%

per year on average in 2006 to 2030 from 11,730 Million tonnes of oil equivalent (Mtoe) to

just over 17,010 Mtoe. This would lead to an energy demand increment of 45% between

2006 and 2030.

The International Energy Agency (IEA) also reported an increasing trend in global energy

demand for 34 countries of the Organization for Economic Co-operation and Development

(OECD) from 1971 to 2011 [6] as shown in Figure 1.4. This analysis included international

aviation and international marine bunkers. These increases have been due to an increase in

demand for tangible and intangible consumer goods and services, and also technological

advancement in the area of product and services development and deliveries. The surge in

0 5 10 15 20 25 30

Other industries

Chemicals

Unclassified

Food, beverages and tobacco

Metal products, machinery and

equipment

Paper, printing and publishing

Iron and steel and non-ferrous metals

Percentage energy demand (%)


5

electrical energy demand was created as a result of increased demands for consumer goods

and services also implies that a proportional increased pressure on electricity generation

and distribution is registered [6].

Figure 1.4: World total energy consumption [6]

The trend of industrial electricity consumption from 1971 to 2011 and as reported in

literature is an increasing one. Since carbon dioxide emission is attributable to electrical

energy consumption and in order that the emission rate is curtailed as proposed by United

Nations Environment Program (UNEP) to meet the sustainability agenda, urgent action is

required at all levels of electrical energy usage in order to help cushion the impact of

electrical energy consumption on the environment.

Mil

lio

n t

on

nes

of

oil

eq

uiv

ale

nt


6

1.3 Energy Demand in Machining processes

In the United Kingdom UK, Digest of UK Energy Statistics’ (DUKES) [7] reported that in

2012, machine tools and accessories (i.e. metal products, machinery and equipment)

consumed on average 38 TWh of electrical energy. This generated on average 16 Million

tonnes of CO2 emitted to the environment in the UK in 2012. Therefore, a reduction of

energy usage in this domain (machining) would reduce the CO2 emission globally and in

the UK.

He et al. [9] reported that the energy efficiency of machine tools is generally less than

30%. This is one of the reasons why machine tools were included by the European Union

EU in the ECO-Design directive [10] to be regulated in terms of its energy consumption

characteristics and efficiency. The aim was for machine tool designers to further

investigate critically how the efficiency of machine tool during the use phase could be

improved in order to reduce and /or optimize electricity consumption. Therefore there is a

need to increase the perceptive and awareness of the energy efficiency improvements for

machine tools.

The development of machine tool has transited through numerous technology and

advancement since the end of the First World War. For example, the improvement from

the use of hydraulics based movement of the machine moving components to more

efficient electrically controlled servos, from gear controlled indexing system to step motors

and more recently, from manually controlled motions to Numerical Control NC codes.

The motorized spindle characteristics of machine tools have also greatly influenced its

efficiency [11]. This motorized system ensures that optimal spindles speeds are selected

especially during turning operations when and if specified within the NC codes.

Improvements such as increased production rate, reduced cycle time and overall


7

production cost optimizations which are enhanced as a result of mass production and

flexible manufacturing systems are some of the advantages derived through technological

development of machine tools. It is worth to note that the technological advancement also

brought about increased electrical energy consumption. These are due to high level of

automation of complex manufacturing processes and the addition of more energy

consuming auxiliary units that have been introduced to modern machining centres. This

addition has greatly affected electricity consumption of machine tools. However, the

introduction of more auxiliary units has brought about great improvement in terms of scrap

rate, process time and product delivery. Since the introduction of more auxiliary units

which now present machine tools with improved manufacturing capability, little has been

done by researchers to investigate the impact of cycle time, high speed machining and

mass production on the electrical energy consumption for these improved processes.

Due to high electrical energy consumption of machine tools, different material

characteristics and process variables, an understanding of the energy demand for the

machining process is therefore required. This energy demand is influenced by machine

tool, cutting tool geometry, workpiece material characteristics, and process variables. The

understanding of the impact of these parameters will elucidate on the optimization of

machining processes and underpin reduction of electrical energy demand and carbon

footprints.

1.4 Aim and Objectives

The aim of this investigation was to develop a deeper understanding and to produce new

data and mathematical models for the electrical energy demand in mechanical machining

processes. The driver was to support energy centric product and process planning.


8

The objectives are as follows:

1. To use event streaming and current monitoring in cutting tests in order to better

understand energy demand in machining processes. Event streaming is a data

gathering method to monitor and record and analyse different actions and/or tasks

that are performed during an operative state of the equipment. An example is using

a power clamp meter to monitor current consumption and to analyse the time series

data and trends to identify distinct machining activities/events.

2. To develop and validate a new model for predicting the electrical energy

requirements in machining processes, explicitly capturing the link between energy

demand and cutting variables of feed rate, speeds and depth of cut.

3. To exploit machining science in modelling and to generate specific energy data for

cutting widely used workpiece materials.

4. To investigate the sensitivity of specific energy and electrical energy demand to the

variation in machining conditions and hence develop robust guidance for electrical

energy demand evaluation.

1.5 Thesis Outline

This thesis is structured into nine chapters based on the research work conducted in the

course of this investigation. The thesis is based on the alternative PhD format. In this

format, research papers are appended to a literature review and conclusions in a coherent

flow.


9


This chapter introduces the background of the study for this research. The broad impact of

the research work was carefully presented from the global perspective and structured in

such a way to defining the aim and objectives of the investigation.

Chapter 2 Literature Review

This chapter provides a critical review of past, present and on-going research work on

electrical energy intensity and estimation methodology in machining processes.

Chapter 3 Research Methodology and Experimental Details

This chapter outlines the methodology of the research, and details of the cutting tests and

evaluation.

Chapter 4 Modelling of direct energy requirements in mechanical machining processes

This chapter focuses on the development of a new mathematical model and logic for

predicting the direct electrical energy requirements in cutting tool paths.

Chapter 5 Impact of Un-Deformed Chip Thickness on Specific Energy in Mechanical

Machining Processes

This chapter is inspired by the mechanistic force modelling and the size effect

phenomenon in machining. The goal was to test the hypothesis that the specific cutting

energy in machining should be dependent on the scale of machining and the thickness of

material to be removed. The chapter focuses on the development of specific energy model

for different workpiece materials and its relationships with the machinability of materials.


10

Chapter 6 Improving the integrity of specific cutting energy coefficients for energy

demand modelling

In this comprehensive study, the evaluation of specific tip energy is undertaken and the

effect of chip thickness, tool wear, nose radius and cutting environment is quantified. This

work is an essential guide for the application of models to estimate energy demand in

practical machining processes.

Chapter 7 Specific Energy Based Evaluation of Machining Efficiency

This chapter discussed the evaluation of the energy efficiency of machining processes

based on specific tip energy and its link to process mechanism.

Chapter 8 Direct electrical energy demand in Fused Deposition Modelling

This chapter focused on the logic for modelling the direct electrical energy demand in

fused deposition. Various additive manufacturing strategies discussed and a case study

where electrical energy demand for 3D modelling was benchmarked to material removal

operation presented.

Chapter 9 Conclusions and Recommendations for Further Work

This chapter summarizes the major findings and conclusions deduced from this research

work and suggest areas for future research.

11

CHAPTER 2

LITERATURE REVIEW

2.1 Energy Demand of Machine Tools

Machining is a mechanical manufacturing process where a thin layer of material is

removed by the relative motion of a cutting tool and a workpiece. The removed material is

called a chip or swarf. The material is removed according to the cutting parameters which

are set to satisfy required dimensional size and accuracy. It is one of the dominant

processes in manufacturing [12-13] by which parts and consumer products are

manufactured directly or indirectly (i.e. to produce moulds and dies for other

manufacturing equipments). The machining of components requires the use of a machine

tool (the main equipment used in the process), a cutting tool (used for material removal

operations) and material to be shaped into the required geometrical tolerances as specified

by the product designer. Machining processes such as milling, turning, shaping, grinding,

gear hobbing etc., have been reported to be energy intensive [14]. This electricity demand

is due to new improvements and additional auxiliary features added to the machine tool

model to improve its performance. For example improved product deliveries (i.e. reduced

cycle time), reduced scrap rate and optimization of process parameters.

Machine tools, machine features and process optimization strategies differ in their

electrical energy consumption, automation levels, complexity and intended use [11]. Initial

Chapter 2 Literature review

12

machine tool improvements were dedicated to reduction of scrap rate and surface finish of

manufactured products. However, the effects of these improvements on electrical energy

demand were not considered during the initial design stages.

The introduction of CNC in machine tools has led to the development and advancement of

flexible manufacturing systems [15]. The CNC mechanism also brought about improved

operational characteristics through advanced machine tool automation and utilization. This

on the one hand contributed to the improvement attained on the surface finish of machined

components, reduced human interaction and errors and scrap rate. It created automated

standalone equipment that can be used in the production line. The added machine features

however, increased the level of electrical energy consumption during the use phase.

The environmental impact and carbon footprint caused by electrical energy consumption

makes machining sustainability agenda a priority and an area of global concern. Issam et

al. [16] investigated cutting of PEEK-CF30 using TiN tools and reported that attention

should be directed towards optimization of process time, electrical energy utilization and

flow of workpiece material. It has been established in literature [17] that significant

improvements in sustainability during machining processes can be obtained by optimizing

process parameters, and adequate characterization of energy consuming units of machine

features [18].

In driving home the sustainability agenda peculiar to machine tools, the International

Standard Organization is developing ISO 14955 1-4 [19] framework based on energy

efficiency index (EEI) and cumulative energy demand (CED) for machine tool design. The

framework anticipates four areas of focus for this standard:

1. ISO 14955-1: Eco-design methodology for machine tools.


13

2. ISO 14955-2: Methods of testing of energy consumption of machine tools and

functional modules.

3. ISO 14955-3: Test pieces/test procedures and parameters for energy consumption

on metal cutting machine tools.

4. ISO 14955-4: Test pieces/test procedures and parameters for energy consumption

on metal forming machine tools.

The functional framework module will allow details of related electrical energy

consumption of machining processes to be investigated and analyzed in a well defined and

structured format. This is to improve the integrity of the electrical energy data collated for

life cycle inventory. However, this framework only addresses the electrical energy demand

of machine tools. The framework does not include for example the computers, fans,

unloaded motors, pumps, servos, etc in the estimation methodology [20-23]. These data

and information are required for an accurate Life Cycle Analysis for manufactured

products.

The Cooperative Effort on Process Emissions in Manufacturing (CO2PE!) methodology

and taxonomy [24-25] is another approach introduced for global standardization of

electrical energy consumption modelling in manufacturing processes proposed by

researchers. This framework developed a joint research methodology, coordinated data

gathering system, systematic data sharing and analysis strategy and output dissemination

strategies. This is initiated to encourage researchers and machine designers to have a global

database whereby electrical energy demand data and process variability for different

machine tools, workpiece material and manufacturing processes are collated and presented

in a format useful for inclusion in Life Cycle Inventory and analysis databases. This will


14

also aid the creation of designs rules and guidelines that will support eco-design and eco-

labelling system of machine tools and machining processes.

Few experimental works have attempted to provide solutions to improving the

methodology and evaluation of the total electrical energy demand in machining processes.

Chapman [26] suggested that studying a particular process in detail is one of the many

ways to evaluating energy usage in a machining process. Therefore, it is paramount to

investigate the factors that govern electrical energy usage of manufacturing process.

From the literature, the evaluation of electrical energy consumption of mechanical

machining processes was categorized into three topics i.e. use phase, specific cutting

energy and online monitoring approach [23, 27-29]. These approaches are discussed in turn

in the successive sections.

2.2 Use Phase energy consumption approach

2.2.1 Direct energy demand

A number of researchers have considered the estimation of energy consumption through

the use phase by disaggregating the energy consuming units of machine tools systems [14,

30-31]. It was reported that the energy demand for actual material removing processes is

considerably lower than that consumed by the equipment during the machining operation.

[32]. For example, Gutowski et al. and Dahmus and Gutowski [14, 31] evaluated the

energy demand of a Toyota automobile production line. In the analysis, the idle stages

dominated the electrical energy consumption. It was reported that 85.2% of the total

energy consumed was used up by the auxiliary units of the machine tools. The actual

cutting process consumed 14.8%. The authors reported that there is significant energy

demand at start-up and to maintain the equipment in an operational mode. The additional


15

energy demand for cutting is proportional to the quantity of material being processed. This

is shown in Figures 2.1 and is modelled in Equation 2.1.

Figure 2.1: Energy used as a function of production rate for an automobile production

machining line adapted from Gutowski et al. [14]

(2.1)

where E is the total energy consumed in J, Po is the idle power (or Basic Power according

to COP2E methodology) in W, k represents the specific cutting energy of the material in

Jmm-3

, material removal rate in mm3/s and t is the cutting time in s.

Behrendt et al. [33], after a survey of 232 machine tools, proposed three assessment

methods for estimating the energy demand of machining operations i.e. idle mode,

operational sequences, and machining operations. In their analysis, they reported that

machine tools standby power varied significantly across different classes and brand of


16

machines available and most importantly, increased with complexity of machine tool. For

example, small and complex 5-axis NMV1500 vertical milling machines, has a standby

power that is almost 2 kW above the medium-sized Haas VF-0 machine. Furthermore, the

cutting energy Ecut, which is given as a percentage of total energy demand, varies between

2% and 20%. This range agreed with Gutowski et al. [14] model previously reported.

Gutowski et al.’s [14] work is a fundamental contribution to the understanding of energy

intensity in machining processes and a great contribution to machining science however,

further investigations is required to categorize and model the energy consuming auxiliary

functions. This addition would allow the basic energy demand at the primary levels and

when the equipment is within the no-cutting zone to be exclusively modelled. This would

also enable a comprehensive process-centric energy estimation model to be developed.

Categorizing the machine tools at this level will aid the estimation of the total energy

demand for machining a given component. Also, the machinability criterion of workpiece

material, which also is one of the parameters that determines the energy demand of

machining a workpiece, needs to be properly modelled.

The energy demand by the auxiliary units and at no-cutting stages was further analyzed by

Mori et al. [34] who modelled the total power consumption during the manufacturing

processes with respect to time. The power consumption was measured while changing

cutting conditions for drilling, end milling and face milling operations on machining

centres. The Taguchi method with an L9 orthogonal array design of experiment was

employed for the analysis. The cutting speed (varied between 50 to 130 m/min), feed rate

(0.20 to 0.35 mm/rev), axial (5 to 15 mm) and the radial depth of cut (0.5 to 1.5 mm) were

among the machining parameters investigated on S45C carbon steel workpiece material.

The cutting tools were multi-layer 10 mm diameter drill coated with TiAlCr + TiSi, with a


17

point angle of 135o and a helix angle of 30

o, for end milling, a 10 mm diameter two-fluted

carbide end mill, multi-layer coated with TiAlCr + TiSi, and for face milling, an 80 mm

diameter face mill with carbide alloy inserts, multi-layer coated with TiAl-N + AlCrN.

From their work, several processes for example, positioning and acceleration of the spindle

following a tool change, actual cutting operation, returning the spindle to the tool change

position after machining, and stopping the spindle were among those investigated. These

stages have great impact on the basic energy demand of machine tools before, during and

after the machining operations. The authors proposed a model based on the processes

investigated and the resultant energy model is shown in Equation 2.2.

(2.2)

where, E is the total energy demand in J, P1 in W is the constant power demand during the

machine operation regardless of the running state, T1 (in s) is the cycle time during non-

cutting state, T2 (in s) is the cycle time during cutting state, P2 (in W) is the power

consumption for cutting by the spindle and servo motor, which fluctuates with cutting

conditions, P3 (in W) is the power consumption to position the workpiece and to

accelerate/decelerate the spindle to the specified speed, and T3 (in s) is the time required to

accelerate the spindle.

The constant power, P1 could vary depending on machine tools and types of auxiliary units

designed to perform auxiliary functions before, during or after the machining process. In

the model proposed (Equation 2.2) the impact of the workpiece materials and process

parameters were ignored for example, the specific cutting energy (tool-tip energy) depicted

as k by [14, 31] and Equation 1 was ignored. The specific cutting energy affects the total

energy demanded for the actual cutting process.


18

In the analysis of Avram and Xirouchakis [35], the relationship between energy

consumption of machine tool state, the transient state and fixed energy consumption of the

peripheral units were established through the use of an automatic programming tool (APT)

file which can be generated by CAD/CAM software. The software called GREEM –

Global Reasoning for Eco-Evaluation of Machining was based on Visual Basic for

Application (VBA). With this software, a methodology was developed for the estimation

of the variable energy requirements of machine tool system through machining toolpath.

The methodology involves the reading and interpretation of an APT file from CAD/CAM

software, estimations of the cutting forces based on process parameters and cutter

geometry, estimating the electric motors load and finally, the variable mechanical power

requirements and the fixed power derived from measurements. All values are then

integrated with respect to the processing time into the software which then provides an

overall estimation of the energy required by the entire machine tool system to complete the

machining of component. This work is an additional contribution to understanding energy

demand of machining processes through APT file generated from CAD/CAM software

however, there were still no distinguishable energy characteristics of various units of the

machining system that will adequately account for the total energy demand and also, the

machinability characteristics of workpiece materials are not modelled explicitly.

He et al. [36] investigated the energy consumption for numerical control machining and

presented a model based on tool path criteria. The authors reported that since machine tool

and features can be controlled through the NC codes, it is therefore possible to estimate

their energy consumption using the related codes that governs the relative movement of

machine features in order to perform specified operations. They approached the energy

estimation based on the linear interpolation of Numerical Codes (NC). In their paper, the


19

general understanding of energy classifications i.e. fixed part and a variable part [37] was

adopted. The fixed and/or constant energy consumption during machining processes

represents that required by the auxiliary features and servos system. This energy demand

ensures and keeps the machine in the running mode. The variable part is required for the

actual machining operations. He et al. [36] presented an energy estimation model as in

Equation 2.3. This model was based on a milling test of C45 steel alloy conducted on

PL700 machining centre at a spindle speed of 2000 RPM, feed of 1500 mm/min and a

depth of cut of 0.2 mm and under the programmed NC codes, the energy consumption of

each machine unit and the machining process was measured by power measurement

devices separately and the result is as shown in Figure 2.2.

Figure 2.2: Energy consumption of the machining process on PL700 machining centre

[36].


20

From Figure 2.2, the fix energy Efix i.e. fan motors and servos accounted for 49% of total

energy consumption; 25% by coolant motor Ecool, unloaded spindle motor Em 13%, cutting

energy Ec 8% and unloaded axis feed motors Efeed 5%. The authors reported that total

energy consumption can be grouped into two parts: constant energy consumption i.e. Efix,

Ecool, Em and Efeed which is the energy demand of idle states and the variable energy

consumption i.e. Ec which is related to the cutting power. The constant energy

consumption depends on machine tool and the installed technology system. The variable

part which is influenced by cutting parameters has the potential to be optimized through

engaging the optimal values of cutting variables for the cutting process. The proposed total

energy estimation model is as shown in Equation 2.3.

(2.3)

where Etotal is the total energy consumption of NC machining, Espindle, Efeed, Etool, Ecool and

Efix are the energy consumption of spindle, axis feed, tool change system, coolant pump,

and the fixed energy consumption respectively.

The model however does not include the start-up energy demand which in most cases is

not negligible. Also, associating the cutting energy with the energy consumed by the

machine tool spindle and called Espindle does not clarify impact of cutting variables. The

electrical energy demand of the spindle and the specific cutting energy should be modelled

separately to capture spindle energy demand characteristics as influenced by spindle and its

drive motors. The specific cutting energy is a function of the spindle-speed characteristic,

workpiece material and the cutting tool geometry. The specific cutting energy is co-related

to the process variables employed during the machining processes and cannot just be

restricted only to the energy demand of the spindle. Also, He et al’s., model ignored other


21

machine auxiliary features, for example fans, computers, chillers; etc. which cannot be

ignored hence, the proposed model requires further improvements and modifications.

Hu et al. [38] reported that the energy required for actual machining is a function of the

machine tool spindle states i.e. start-up, idle and cutting states. They classified total energy

demand into constant energy obtained during the non-machining state and variable energy

obtained at the machining energy state.

Jingxiang et al. [39] proposed a methodology to model the energy demand of CNC

machine tools based on Therbligs (i.e. a set of fundamental motions required for machine

tool to perform an operation). In their study, they established energy supply models of

CNC machine tools by developing the power models of each machine tool Therblig and

obtained the total energy demand for the machining process through summation of each

power of Therblig (i.e. standby operation, lighting, axis feeding cutting, etc). This method

of energy estimation for machining processes could take lot of time for production

planning and process optimization because more data are required to be collated. The

process and product planners require a simple, easy to use methodology to estimate energy

demand.

Salonitis and Ball [40] reported that having an energy audit at the process level can be one

of the ways to characterize energy demand for machine tools subsystems. In their analysis

for grinding operations, they reported that the total energy required by a machine tool to

perform specific process can be estimated using Equation 2.4.

peripheralprocesstotal EEE (2.4)

where Eprocess is the energy required for the physical process to occur and Eperipherals

represents additional energy consumed by the machine tool (e.g. for operating the coolant


22

pump, for overcoming the efficiency losses, etc.). The authors reported that the energy

required for physical process Eprocess depends on the mechanics of the cutting process and

thus can be estimated from the specific cutting energy. The electrical energy consuming

units of the machine tool were lumped into and included in the Eperipheral as modelled in

Equation 2.4. This clearly ignores need for disaggregating energy consuming auxiliary

units to enable modelling of different machine designs.

Seow and Rahimifard [41] categorized energy consumption in the manufacturing sector

into direct and indirect energy. The direct energy is the energy demand during the

manufacturing processes while the indirect is the energy demand to maintain the

environment in which the processes are executed. The direct energy was sub-divided into

theoretical energy (as the minimum energy required carrying out the machining process)

and auxiliary energy (as the energy demand of the supporting activities and auxiliary

equipments). Seow and Rahimifard’s [41] work is a theoretical representation of energy

utilization and is not in line with ISO 14955 [19] or the ‘Basic’ and ‘Tip’ energy concept.

The authors do not disaggregate energy consuming units into the proposed categories in

order to distinctly represents the basic and tip energy concepts.

2.2.2 Energy footprint- Direct and embodied energy

To optimize the total direct and indirect electrical energy associated with a machining

process based on minimum energy and optimization criterion, Mativenga and Rajemi [29,

42] analyzed the energy footprint in machining a given product. They considered the direct

energy demand and the energy embodied in tooling. The model to estimate the total energy

footprint in a single pass turning operation proposed by the authors is as shown in Equation

2.4.

(2.4)


23

where E1 is the energy consumed by the machine during setup operation (Idle energy), E2

the cutting energy, E3 the tool change energy, E4 is the embodied energy of the tool and E5

the embodied energy of the material.

This model is expanded into Equation 2.5.

(2.5)

where t1 is machine setup time in s, t3 is tool change time in s, T is the tool-life in s and yE

represent the embodied energy per cutting edge of the cutting tool in J. Other parameters

retain their initial definitions.

They evaluated an optimum tool life for minimum energy demand that can be used to

constrain cutting velocity and this is modelled by Equation 2.6.

(2.6)

where Topt-c is the optimum tool-life in minutes, α is the cutting velocity exponent, yc is the

tool cost per cutting edge, x is the machine cost rate and t3 is the tool change time in

minutes.

In order to estimate energy demand during the actual cutting process i.e. ‘Tip energy’, the

specific cutting energy k was assumed constant.

2.3 Specific cutting energy approach

Few researchers modelled the total energy demand in machining using the specific cutting

energy approach. The specific cutting energy is the tool-tip energy demand to remove 1

cm3 of material. The specific cutting energy is process dependent and thus has a correlation

with the machinability of materials. Its values have been assumed constant in the models

found in literature [12, 31].


24

In this approach, the relationships between the power demanded during the machining

operations and the material removal rate are statistically evaluated and the resultant

relation is called the specific cutting energy. This relationship normalizes the effect of

power demand with the material removal rates.

Following this methodology, Sarwar et al. [43], in their analysis with three different

workpiece materials (ball bearing steel, stainless steel and Ni-Cr-Mo steel), and a bimetal

band saw cutting tool (High Speed Steel [HSS] edge and low alloy steel backing material)

under vertical feed band saw machine (NC-controlled, Behringer HBP650/850A/CNC)

showed the relationships that existed between specific cutting energy and process

variables. Their specific energy model is as presented in Equation 2.7.

(2.7)

where Esp is specific cutting energy in J/m3, Tm is the time required for cutting in s, Fv is

the cutting force in N, Fp is the thrust force in N, Vb is the horizontal band speed in m/s, Vf

is the vertical feed speed in m/s, Lcut is the horizontal length of cut in m and Achip is the chip

cross-sectional area in m2.

The authors showed that the specific cutting energy increases as the number of cuts

increases and also as the cutting tool width deteriorated. Though they did not model tool

wear, it can be inferred here that their results suggest a link between specific energy and

tool wear. It was reported that the variation of specific cutting energy as a function of

different workpiece materials can provide useful information in estimating machinability

characteristics for selected workpieces. The specific cutting energy can be a function that

reflects the efficiency of metal sawing process.


25

This methodology was also attempted by Li and Kara [44] in which a turning operation

was carried out on aluminium 2011, bright mild steel 1020, and high tensile steel 4140

workpiece materials. The general insert designation used was, WNMG 06T208-PP with

grade IC9025. During the turning process, cutting velocity, feed and depth of cut were

varied so as to generate different values for the material removal rate. This enabled the

ANOVA analysis to be conducted at three levels. The result showed a strong correlation

between the specific cutting energy and material removal rate. Their analysis yielded a

specific energy model as in Equation 2.8.

(2.8)

where Esp in kJ/cm3 is the specific energy consumption; Q is the material removal rate; Co,

and C1 are empirical coefficients. The value of Co and C1 were further deduced as shown in

Equation 2.9 thus:

(2.9)

In this model, the empirical coefficients i.e. Co and C1 which represents energy demand by

machine tools features are to be determined prior to evaluation of specific consumed

energy. The authors reported that these coefficients are machine tool dependant and their

values can be estimated through empirical modelling of the machine tools investigated.

This coefficient is to account for the basic energy consumption for a specific machine tool.

Although, the methodology presented provided an insight to the understanding of power

demand by machine tools and auxiliary functions, however, the model which was proposed

to represent the total energy demand does not consider the power consumption of auxiliary

units that was proposed in the methodology. Also, the effect of coolant application on the

total energy demand of mechanical machining processes was also ignored. The model


26

therefore, cannot be used to estimate the total energy demand for the machine tool which

theoretically should include energy demand by all auxiliary functions, servos, pumps,

lights, fans, air cutting and other energy consuming units. The model also does not

represent the tool tip energy demand since it failed to consider the impact of chip

thickness. The tool tip energy demand is influenced by the un-deformed chip thickness and

process variables. The authors do not consider the impact of un-deformed chip thicknesses

on the specific cutting energy demand model. The specific energy model proposed hid the

effect of process variables and workpiece materials.

In another development, Draganescu et al. [45] attributed the specific cutting energy to

machine tool efficiency. It was evaluated as a ratio of cutting power, Pc, efficiency, η, and

material removal rate, Q as shown in Equation 2.10. In their analysis on a vertical milling

machine FV-32 and a design of experiment that used a 26 composite factorial experimental

design, they carried out a face milling test on aluminium alloy ATSi10Mg and a

relationship was established between specific consumed energy, cutting power, machine

tool efficiency and material removal rate. These relationships are all influenced by cutting

parameters and tool cutting capacity. The analysis also showed that feed has a greater

influence during milling operations on the specific consumed energy especially at values

smaller than 0.1 mm/tooth. This effect is known to be ‘size effect’ in micro-machining.

(2.10)

The authors also proposed that the consumed energy Ec can be estimated as the product of

specific consumed energy Esp and material removal rate Q as depicted in Equation 2.10b.

(2.10b)


27

The machine tool efficiency is an important factor that determines mathematical models

for electric energy consumption since the input variables that yields an output i.e. finished

product are governed by the efficiency of the equipment at use. The efficiency is the ratio

of cutting power, Pc, and consumed power, Pmc, absorbed from power network by the

electric motor [45]. Based on these relationships, it was also observed that feed (the un-

deformed chip thickness) is one of the parameters that relates with the efficiency of the

machine tool and as such, influences specific cutting energy as it varies with process rate.

Their model did not separate the Basic energy from the tip energy. Specific energy

calculated from total energy does not reflect machinability of materials. Its value is

influenced by machine design. Specific energy calculated from tip energy will be a

measure of machinability of materials.

Diaz et al. [46] also modelled energy consumption using the specific cutting energy

approach. Their analysis involves the variations of process parameters that determine the

material removal rate (Q). Cutting tests was conducted on a Mori Seiki NV1500 DCG. The

power demand was measured with a watt node MODBUS Wattmeter. In order to have

different values for material removal rate Q, the width and depth of cut were varied in two

separate experiments and their corresponding power demand measured. The milling test

was conducted with 2-flute uncoated carbide, 2- flute TiN coated carbide and 4- flute TiN

coated carbide end mills. The workpiece material was AISI 1018 steel and 101 mm long. It

was shown that as the material removal rate increases, the power demand also increases

while the energy demanded reduces. This is due to the fact that machining time reduces

with increased material removal rate for a given length of material. In characterizing the

energy consumption of a machine tool, the authors reported that as the material removal

rate approaches infinity the specific energy is expected to reach a steady state of zero. In


28

cutting, friction, rubbing and ploughing always exist and hence zero energy is not a

possible outcome. However, given the work volume, spindle speed, and table feed

constraints of a machine tool as well as the maximum loads that can be applied without

deforming the main body frame or breaking the spindle motor, the operator will never

reach a material removal rate anywhere near infinity. So under the constraints of the

material removal rate the relationship produces a regression equation as depicted in

Equation 2.11. They concluded that the total energy demand can be estimated by

multiplying the specific energy with Q.

(2.11)

where Esp is the specific cutting energy, k is a constant and has units of power and b

represents the steady-state specific energy.

Although, in their report, the need to include the air cutting and power demand of the

machine tool was mentioned, this was not incorporated into the developed model.

In one of the earliest work, Lucca and Seo and Arsecularatne [47-48], it was reported that

tool tip energy demand i.e. specific cutting energy can be influenced by the un-deformed

chip thickness, cutting edge radius and process variables. Although, not investigated, the

authors reported the impact of ploughing on determining the process mechanism and tip

energy. This phenomenon was also supported by Ghosh et al. [49], Pawade et al. [50] and

Guo et al. [51]. These works directly and/or indirectly contribute to the understanding of

specific cutting energy and process mechanisms; directly in the sense that they reported

that higher specific energy values relates to an increase of ploughing effect. However, the

methodology presented does not model this phenomenon explicitly. Up-to-date, the

specific cutting energy model found in literature does not incorporate un-deformed chip

thickness and process mechanisms into proposed specific energy models. Also, the models


29

does not consider the fact that specific cutting energy could vary depending on cutting

parameters, tool geometry and process mechanism at play during the cutting process.

Hence the need for more understanding of the process mechanisms that governs

mechanical machining processes and re-evaluation of specific cutting energy models found

in literature.

2.4 Online monitoring approach

In this approach, energy demand of the manufacturing processes is monitored and

measured in a real time event through the use of sensor devices or software applications

commonly used for online measurement. Teti et al. [52] and Shi and Gindy [53] developed

a PXI-based online machining process monitoring system. This system was developed in

LabVIEW environment and was used to acquire, present and analyze sensory signals

automatically through the use of advanced queue and triggering technique. The developed

online machining process monitoring system was validated on a Swedturn 4-axes CNC

twin lathe when turning Inconel 718 disc. Ceramic insert RCGX 35T-0320 with constant

tool edge preparation (clearance angle 1° and rake angle 13°) and different tooling

conditions were employed to conduct turning trials. The result indicated that the

monitoring system could be used to monitor cutting forces, power, vibrations and tool wear

in real time during the turning operations.

Vijayaraghavan and Dornfeld [22] investigated the energy requirement of machine tools

and their effect on the overall Life cycle and power consumption through system

monitoring and data analysis software. The authors proposed a software-based approach

for automated energy reasoning which can support decision making at all levels. The

software architecture included the ability to monitor energy use with process data,


30

standardized data sources, architecture for large data volumes and ability to analyze data

across different manufacturing platforms as shown in Figure 2.3.

Figure 2.3: Software architecture for temporal analysis of energy used in manufacturing

adapted from Vijayaraghavan and Dornfeld [22].

The software utilizes “Complex Event Processing (CEP)” which handle data reasoning and

information processing. “MTConnect” interface was used to link data from the machine

tool and /or other manufacturing equipment to the “Event Cloud” for information

processing and strategic decision making. MTConnect is based on “eXtensible Markup

Language (XML)”, which provides semi-structured and machine readable data for

exchange [54], process planning and process optimization during the manufacturing

operations. MTConnectSM

, allows operational data of manufacturing equipment for


31

example machine tool to be monitored with respect to energy consumption data. This could

enhance process planning and environmental impact assessment reporting. The software

can monitor and stream events as at when these actions take place. It might be complex to

disaggregate energy consuming units from the total energy demand as only the highs and

the lows of energy consumption of a process are streamed to the event cloud. Also, the

machining theory and impact of process variables cannot be understood since the software

is only used to gather direct energy data during the machining process and estimate the

averages of the total energy demand as output. This does not allow intelligent process

planning before machining.

Hu et al. [38] proposed an on-line approach to monitor the energy efficiency of machine

tools and developed an architecture for the on-line energy monitoring system (OEEM

system). The OEEM system can accurately acquire energy and some other useful energy

efficiency-related information of machine tools and can therefore be used to estimate the

total energy demand. The proposed OEEM system shown in Figure 2.4 is the combination

of spindle power measurement and off-line constant energy consumption measurement.


32

Figure 2.4: Architecture of OEEM system adapted from Hu et al. [38].

The authors reported that the energy estimation with OEEM system involves acquiring the

constant energy consumption in a non-machining state, the variable energy consumption in

a machining state and exportation and visualization of the data related to energy efficiency.

This approach by Hu et al. [38] is a great addition to various ways developed by

researchers in understanding energy intensity of machining processes however, the

proposed methodology is not appropriate during the pre-machining process planning as

data will not have been collated.

In a similar work, He et al. [9] presented a task-oriented modelling method for machining

simulated using event graph on Matlab software. It was reported that in estimating the

energy demand of a process, the operations within the process can be categorized into

event blocks as shown in Figure 2.5. The authors modelled machining system tasks into

three events i.e. start machining, end machining and idle/waiting. In their analysis it was

proposed that a task based simulation can present alternative machining strategy that could

lead to energy saving potentials and sustainable manufacture. This method will aid


33

estimation of total energy demand in machining processes based on executed tasks and can

aid process managers make optimum decisions on selecting the flexible processes of tasks

to meet actual production requirements and save energy. However, other machine tool

features are not properly decomposed into machining phase and sub-phases to model in

more detail energy consumption for tasks proposed.

Figure 2.5: Basic event graph model for an energy consumption cycle adapted from He et

al. [9]

Other researchers also attempted to characterize the energy intensity in machining

processes and their reported methodology does not categorically fit within the three groups

found in literature. De Filippi et al. [55] presented NC machine tools as electric energy

tatp

tpis the processing time of task on machine tool (s)

tais the time interval waiting for the next task on machine tool (s)


34

users. Helu et al. [56] reported on an evaluation of the relationship between use phase

environmental impacts and manufacturing process precision. Behrendt et al. [33]

developed an energy consumption monitoring procedure for machine tools. Li et al. [57]

investigated the fixed energy consumption of machine tools and attempted using the

empirical methodology to estimate the total energy demand. Although their contributions

was an addition to knowledge of energy requirement for machine tools, the respective

energy models presented are not a standalone energy model where energy demand can be

estimated pre- production for process planning.

From the literature review, the following knowledge gaps and facts were established:

Electrical energy demand in mechanical machining can be modelled based on

‘Basic’ and ‘Tip’ energy. The ‘Basic’ energy is the energy demanded by a machine

tool at zero load while the ‘Tip’ energy is the additional energy required for

material removal.

‘Basic’ energy is greater than the ‘Tip’ energy and dominates total electrical energy

demand in mechanical machining processes.

While a limited number of researchers have evaluated the specific energy, they

have grouped all variables together and not captured the effect of differences in

workpiece materials. This is required so that users can tailor model to material

being cut.

Existing models for electrical energy demand are not comprehensive and most do

not capture effect of cutting variables or preparatory states.

It has been assumed that specific energy is constant and not influenced by thickness

of material been cut. This assumption needs to be tested.


35

The impact of process mechanisms on specific cutting energy has not been

comprehensively researched. The specific cutting energy varies with changing tool

geometry, workpiece material and cutting parameters during mechanical machining

operations.

The machinability characteristics of workpiece materials has not brought into the

context of modelling specific cutting energy during the machining process.

36

CHAPTER 3

EXPERIMENTAL DETAILS

3.1 Introduction

In order to address the knowledge gaps identified, a series of cutting tests were conducted

on different machine tools. This was done to define a strategy to estimate electrical energy

demand for machining processes.

In this section, the machine tools, measuring equipments, cutting tools and workpiece

materials used in the course of this study are introduced.

3.2 Machine tool

The focus of this research was the electrical energy demand when machining engineering

materials. For this purpose, an MHP lathe, Takisawa milling machine and Mikron HSM

400 high speed machining centre were used for the cutting tests.

3.2.1 The MHP lathe

The MHP lathe shown in Figure 3.1 is one of the commercially available CNC lathe. The

machine spindle is controlled by an 18 kW DC Servo Motor while the x and z axes are

controlled by 1.1 kW and 1.8 kW DC Servo Motor respectively. The rapid positioning of

the machine axis is constrained to 5 m/min along the x-axis and 10 m/min along the z-axis.

The machine has 450 mm swing over bed and 250 mm turning diameter between centres.

Chapter 3 Experimental Details

37

The turret controlled tool positions have 12 stations and it takes 1.2 seconds to index from

station to station (bi-directional). The machine uses the capability of an MDSI open

architecture controller as the post processor to interact and coordinate the CAD-CAM

application and machine tool units.

Figure 3.1: MHP Lathe

3.2.2 Takisawa milling machine

The Takisawa MAC-V2 is a multi-purpose vertical-type machining centre designed to

manufacture small parts by combining operations such as milling, end milling, drilling,

tapping and boring in one process set up. The machine is controlled by an AC 5.5 kW/ 30-

min rating main motor, the x, y and z feed motors provide 0.85, 0.85 and 1.2 kW

maximum power respectively. The machine is controlled by 3 axes movement i.e. x, y and

z axis. The machine table can travel 510 mm by 400 mm along the X-Y direction and the


38

spindle can traverse 300 mm along z axis. The rapid traverse feeds on the x and y axis is

12,000 mm/min and z axis is 10,000 mm/min. The machine post processor is controlled by

the capability of an MDSI open architecture controller to interact and coordinate the CAD-

CAM application and machine tool features. The Takisawa MAC-V2 is shown in Figure

3.2.

Figure 3.2: Takisawa Milling Machine


39

3.2.3 Mikron HSM 400 high speed machining centre

The Mikron HSM 400 high speed machining centre shown in Figure 3.3 has a maximum

spindle power of 10 kW. The spindle, HVC140-SB-10-15/42-3F-HSK-E40 is equipped

with ceramic hybrid bearings lubricated by a means of oil-air lubrication system. The

spindle speed ranges from 60 RPM to 42000 RPM. This speed range enables different

workpiece materials to be machined under conventional and high cutting speed. The

CAD/CAM system is linked to the post processor of the machine. The machine controller

is controlled by Heidenhain iTNC 530 controller. This enables the machine tool to interact

between the NC codes and machine features. The precision machine has feedback

mechanisms that engage a probe to measure tool dimensions. The tool magazine can hold

up to 18 tools.

Figure 3.3: Mikron HSM 400 machining centre.


40

3.3 Measuring Equipments

3.3.1 Fluke 345 Clamp meter

Fluke 345 has in-built functions for current, voltage, frequency and power measurement

both for single and 3-phase voltage input. An Oscilloscope and Data Logger incorporated

into the hand-held tool allows both online monitoring and data acquisitions to be possible.

The equipment has a capability to measure and record True-rms ac and dc current up to

2000 A. The Fluke 345 clamp meter is shown in Figure 3.4. It is equipped with a low-pass

filter to remove high frequency noise and used to troubleshoot power quality on switching

loads. The power measurement is based on the Hall Effect (variation of output voltage in

response to a magnetic field) characteristics. This makes measurement of DC current

possible without the need to break the circuit. The internal memory of the power clamp

meter enables long-term power quality logging. With 3 logging areas, the recorded data

can be saved and transferred to a computer.

Figure 3.4: Fluke 345 Clamp Meter


41

3.3.2 ProgRes® microscope camera

This ProgRes® microscope was used to visualize and measure cutting tool flank wear. The

ProgRes® microscope camera is a reliable and powerful digital microscope camera used

for image analysis and documentation. It is controlled by 3.3 Megapixel SONY Super HAD

CCD sensors with RGB colour mask and micro lenses. The sensors are active within an

area of 7.2 x 5.3 mm2 and at an exposure time of 0.2 micro seconds to 180 seconds. The

photographic image of samples is made visible through software called ProgRes™ Capture

Basic for MS Windows® 2000/XP installed on a computer workstation as shown in Figure

3.5. The microscope has a power consumption of 4 W and work table distance of 145 x 93

x 123 mm. The operating temperature conditions ranges from 5oC to 35

oC. This minimises

the effect of expansion and compression on the physical properties of the equipment.

To assess the microscopy view of sample surfaces, the sample is placed on the work table

and fully focused with the laser beam emitted onto the work sample. The image is then

projected to the computer system where it can be analyzed, measured and documented.


42

Figure 3.5: ProgRes® microscope workstation

3.3.3 Leica DM2500M Microscope

The Leica DM2500M Microscope shown in Figure 3.6 is designed to deliver pin-sharp

image quality during material analysis. This was used for imaging the cutting tool. It has

an input voltage of 90-250V with a frequency range of 50 -606 Hz. The pre installed power

input of 160 W is controlled by a F3, 15A and 250V fuses. The working temperature is

between 15-35oC ambient temperatures under a relative humidity of 30 to 80% maximum.

The sample under investigation is mounted on the specimen stage with the specimen

holder and could be magnified by 1X, 1.5X and 2X respectively depending on the

magnification desired to give a clearer image of the specimen.


43

Figure 3.6: Leica DM2500M Microscope

The cutting tool edge radius was visualized and estimated under the Leica DM2500M

Microscope. The values of the average cutting edge radius employed for this work ranges

from 60 µm to 70 µm as shown in Table 3.1. The edge radius was measured by inserting a

best fit circle that intersects the tangential line drawn across the rake and flank faces of the

insert. The dimension of the circle (i.e. cutting edge radius) was automatically evaluated by

the microscope as shown in Figure 3.7.


44

Figure 3.7: Cutting edge radius measurement under Leica DM2500M Microscope

Table 3.1: Average cutting edge radius measured under Leica DM2500M Microscope

Insert

Nomenclature

Measured tool edge radius

(µm) Average

(µm) 1 2 3

SOMT-

060204-HQ 56.8 62.5 59.6 59.6

HM90 APKT

1003 68.7 69.5 72.2 70.1

3.4 Workpiece materials

Cutting tests were conducted on stainless steel T316L, EN8 steel alloy, AISI 1045 steel

alloy, aluminium AW6082-T6 alloy and titanium 6Al-4V alloy. The workpiece materials

were selected based on their wide engineering applications, for example stainless steel


45

T316L materials are widely used in the fabrication of food and medical equipment,

aluminium AW6082-T6 alloy and titanium 6Al-4V alloy are used in the aerospace

industries, AISI 1045 and EN8 are used in other general applications. The chemical

compositions of all workpiece materials are as listed in Appendix A and in relevant

chapters.

3.5 Cutting tools

For this work, cutting tools inserts designated TPKN1603PPTR-P30, SHR-161-6160K,

SOMT-060204-HQ, HM90 APKT 100304PDR IC328, HM90 APKT 100308PDR IC328,

HM90 APKT 100312PDR and CNMG 120408-WF 4215 were used for the cutting tests

i.e. surface cleaning, side milling and turning operations. Cutting tool geometry and

specifications of specific cutting tool inserts used are reported in the relevant chapters.

Further details of the cutting tools geometry used are as stated in Appendix B.

3.6 Measurement of tool wear

To measure flank wear a magnified image of the cutting tool flank face was taken with the

optical microscope after cutting as shown in Figure 3.8. Lines across the flank wear land

and perpendicular to the original cutting edge were used to get an average flank wear

measure.


46

Figure 3.8: Sample flank wear observed under optical microscope

3.7 Experimental setup for machining tests

Cutting tests were conducted on MHP lathe, Takisawa and Mikron HSM 400 machining

centre in the UK and Hitachi Seiki VG-45 and Roeder RFM 700 in Singapore. This was to

ensure that data collated can be compared to other international research. The cutting test

was conducted in both dry and wet cutting environment under single and multi-fluted

cutting tools as discussed in relevant chapters.

In all the set up, and before the start of the cutting test, the Fluke 345 (Figure 3.4) power

measuring devices was attached to one of the 3-phase cable that supplies electrical current

to machine tool. The recorded data represented the electricity demand for the machine tool

during non-cutting and cutting modes/operations. The measured electrical current was then

converted by using the power Equation 3.1 and consequently energy demand was

evaluated with Equation 3.2.

3**VIP (3.1)

Flank wear


47

tPE * (3.2)

where P is the total power consumed in W, I represent the electrical current in Amps, V is

the electrical voltage in Volts for 3-phase supply, E is the total electrical energy demand in

J, and t is the total cycle time in s.

After the cutting tests, data collated were analyzed to establish relationships between

dependent and independent variables and to model these relationships accordingly. The

resulting model/s, discussion and conclusion deduced are reported in appropriate chapters.

In the course of this research, standard working procedures observed throughout the

cutting tests include:

General risk assessment was taken to ensure the safety of the machine tool,

operator and all involved in data collation.

Adequate laboratory apron, safety booths and safety goggles were worn.

Materials and cutting tools used were as supplied from recommended

manufacturers.

Cutting tests were repeated three times to ensure repeatability of data collated.

In the case where the machine electrical wiring systems was involved, trained

personnel was contacted to ensure safety from electrocution.

48

CHAPTER 4

MODELLING OF DIRECT ENERGY REQUIREMENTS

IN MECHANICAL MACHINING PROCESSES

4.1 Abstract

The aim of this research work was to contribute towards the development of a

mathematical model and logic for predicting direct electrical energy requirements in

cutting tool paths. This work is critical in order to track the visibility and process

dependence of energy and carbon footprint in machining process as well as the cost of

energy as a resource. In this study existing models were critically reviewed and their

limitations identified. The effect of machine modules, auxiliary units and machine codes

on power and energy consumption for machine tools was studied with the electrical current

consumption measured. A mathematical model for electrical energy use in machining was

developed addressing the limitations of existing models and validated on a milling tool

path. The paper provides valuable information on the impact of machine modules,

spindles, auxiliary units and motion states on the electrical energy demand budget for a

machine tool resource. This knowledge is fundamentally important in re-designing

machine tools to make them more energy efficient process planning to ensure that

components are machined using the least amount of energy thus reducing electricity costs

and carbon footprints.


49

Keywords: Machine tools, energy models, toolpaths

4.2 Introduction

In the year 2010, 27% of electrical energy consumption in the UK was attributable to

industrial sectors [7]. In most cases this electricity is predominantly generated through the

use of fossil fuel. The industrial sector places a high demand on the supply of electricity.

The use of carbon rich electricity generation sources is of critical global concern as these

processes produce CO2 emissions. This implies that the higher the consumption of

electricity in manufacturing industries, the higher the carbon footprints left by such end

products. As a result, UK government and other nation states are making an increasing

demand for energy efficiency.

Machine tools play a major role in manufacturing and they were cited by the European

Commission as being in a top three priority for inclusion into the product categories to be

regulated through the eco-design directive [10]. Thus there is an urgent need for

manufacturing sectors, particularly machining, to reduce energy use per product

manufactured, to help meeting eco-design directives and CO2 emission targets. Some

relevant targets were set by the Kyoto protocol 1997 [58]. For designers of machine tools,

this calls for an increased understanding of energy use by different design features of a

machine tool. For the manufacturing industry there is a need to understand the impact of

machine tool motions and toolpaths on energy requirements for machining.

4.3 Machine Tool States and Proposed Improvements

The Cooperative Effort in Process Emission (CO2PE!) proposed a unified taxonomy [25]

and methodology [24] so that in manufacturing, energy data collection can be standardized

and presented in a globally compatible approach. They classified machine tool states into


50

two categories of ‘Basic State’ and ‘Cutting State’ based on operational characteristics of

the processes. In the ‘basic state’, electrical energy is needed to activate required machine

components and ensure the operational readiness of the machine tool. In the ‘Cutting State’

the energy is demanded at the tool tip to remove work piece material as well as for modes

of energy loss e.g. through machine noise, friction etc.

While COP2E! sets the framework, it does not clarify the existence of a transition state

between the Basic State and Cutting State. In this paper the authors define a third and

intermediate state called the “Ready State”. This is required because after the machine is

started more energy is needed for the drives and spindle movement to bring the tool and

work piece to the correct (about to cut) position and to set-up the necessary cutting

velocity. Examples for such activities could include ‘G00’, ‘S’ and ‘T’ (rapid, spindle

speed and tool change machine features respectively). Figure 4.1 shows the extended

machine tool electrical energy states proposed by the authors.

Figure 4.1: Machine tool electrical energy consumption estimation model

Auxiliary Units

Energy

Consumption

Cutting Units

Energy

Consumption

Machine Ready

State Energy

Consumption

Basic Energy

Consumption

ECutting

Tool Tip and

Coolant Energy

EReady

Machine Spindle, Machine

Jog, Pumps, Servo Home

Location, Tool Change,

Swarf Conveyors

EBasic

Startup, Computer Units,

Lighting, Cooling Fans,

Lubrication, Unloaded

Motors

ETotal


51

In order to establish focus for electrical energy improvement, it is important to understand

how the electrical energy use or power demanded is distributed during machine use. A

small number of researchers have explored the critical aspects of electrical energy use in

machine tools. Some of these results are summarized in Table 4.1.

Table 4.1: Machine tool contribution to electrical energy demand

Authors Observations

Kordonowy [30] The constant energy on an Cincinnati Milacron 7VC

Automated Milling Machine was 51.9% of the total energy

requirements in machining.

Dahmus et al. [14] and

Gutowski et al. [31]

The energy consumption of machine tools during actual

cutting processes was 85.2% for ‘Idle’ or machine tool

auxiliary function’ and 14.8% for ‘Cutting’.

Devoldere et al. [59] The idle or stand-by mode consumed 1.7kW energy on a five-

axis milling machine with the biggest consumer as the

hydraulic pump, responsible for nearly 0.9kW.

Diaz et al. [21] Among the auxiliary units on a Mori Seiki NV1500DCG

milling machine, the servo and the spindle consumed the most

energy in the basic and idle states.

Vijayaraghavan and Dornfeld

[22]

For a Mori Seiki NV1500DCG milling machine, energy

consumption was dominated by start-up and idle states.

Rajemi et al. [42] In an MHP CNC Lathe during dry cutting the machine

module and idle power consumption were 61 to 69% of the

total power, in the cutting speed range of 500 to 300 m/min.

Anderberg et al. [60] Reported that energy demand by machine tool auxiliary units

dominates the cost components and CO2 footprint of a

manufactured product.


52

Table 4.1 clearly shows that machine tool is the dominant consumer of electrical energy in

machining compared to the actual chip forming process. It is important to further

understand how this electrical energy requirement by the machine is distributed. This can

then help the development of more accurate models of energy demanded by machine tools

and hence inform machine tool designers. A good basis for analysing energy use in

machining is through Gutowski et al.’s [31] mathematical model for direct energy

requirement in machining as shown in Equation 4.1.

(4.1)

where E is the direct energy in J required in machining processes, Po is the power in W,

consumed by the machine before it starts cutting, k is the specific energy requirement in

J/mm3 for machining a particular work piece material, is the material removal rate in

mm3/s, while t is the cutting time in seconds.

Considering both Equation 4.1, it can be noted that Po dominates the direct energy

requirement in machining and hence, and from and Table 4.1, this parameter dominates the

energy demand in machining. Thus, the selection of machine tools can have significant

impact on direct energy requirement in machining. It is noted here that Equation 4.1,

enables modelling of the machine tool energy requirements to be done distinctly from the

energy required for chip formation process. This is very valuable for supporting process

planning as it enables comparison and selection of machine tools and workpiece materials.

After this early work by the MIT group, a number of mathematical models for energy in

machining have been proposed and these are summarised in Table 4.2.


53

Table 4.2: A summary of other models for direct energy requirements in machining

Author(s) Direct Energy in Machining Model

Mori et

al. [34]

(4.2)

where E is the total direct energy requirements, P1, P2, P3 represent basic, idle and

cutting power demand in Wh and T1, T2, T3 are the corresponding time.

Diaz et

al. [61]

(4.3)

where E is the total direct energy requirements, pavg is power demand, ∆t processing

time, pcut is the cutting power and pair is air cutting.

He et al.

[36]

(4.4)

This expanded to

sefanservocoecooltooltool

m

i

t

ti

t

t

c

t

t

mtotal ttppttptpdtpdtpdtpEfe

fs

cs

ce

ms

me

cos

1

(4.5)

where Etotal is the total direct energy requirements, pm is the power for enabling the

operating state of the spindle transmission module, pc is the power for material

removal from the workpiece, tms and tme are respectively the starting time and the

ending time for spindle running during the operating state, and tcs and tce are

respectively the starting time and the ending time for cutting during the material

removal process, pi, tfei , and tfsi are respectively, the power, the starting time, and the

ending time of the ith-axis feed motor during material removal process , ptool is the

power of the tool change motor, and ttool is the turret rotation time, pcool is the power of

the coolant pump motors, and (tcoe-tcos) represents the running time of the coolant pump

motor, pservo and pfan are the power of the servos system and fan motors, respectively

and (te – ts) denotes the running time of the machine tool throughout the entire NC file.

From Table 4.2, the modelling approach used by Mori et al. [34] has some resemblance to

Gutowski et al.’s [31] but Mori et al. split Po into basic and idle power. This is in line with

the proposal put forward by this paper in Figure 4.1 assuming that the idle power can be

described as the ‘Ready State’ power. Calling it idle power suggests an unnecessary step,


54

while the term ready state clarifies the need to bring the system to an about tool to engage

movement. In the current paper, the authors further propose that the cutting power demand,

P3 in Mori et al.’s [34] model can be expanded to take into account the specific cutting

energy coefficient as introduced by Gutowski et al. [31]. Diaz et al. [61] produced an

interesting approach in that it acknowledged that in machining process the tool engage and

disengage with the workpiece and the total cycle time is not devoted to the actual material

removal. Thus, modelling the air cutting time reduces the chance of over estimation of the

energy demand. Ultimately there is need to quantify the air cutting time for toolpath in

order to estimate this impact. However, Diaz et al.’s model was not focused on process

planning and does not explicitly model the impact of machine tools, workpiece materials

and cutting variables.

Unlike Gutowski et al. [31], He et al. [36] use cutting forces instead of specific energy to

model the energy required for the chip formation process. The utility of using specific

energy is better because it is a simple concept to apply to a range of machining processes.

It enables an assessment of the energy efficiency of machining materials based on their

machinability. The limitation of He et al.’s model is that modelling the fixed energy simply

based on power for servo drives does not present a complete picture as other equipment

features are required to support machining process. For example this model ignores energy

demand for the computer used by the machine, the lights, lubrication of the machine, swarf

conveyors, chillers etc. The model is thus not generic for all machines tools and this is a

significant aspect which needs to be improved. The energy used by machine equipment

features in the unloaded state needs to be characterised according to the machine design

and energy losses. Additionally, He et al.’s part of the energy model for tool change did

not consider the number of tool changes required to finish a machining job. Moreover it


55

did not consider the fact that turret indexing could be done using the shortest route.

Additionally, on a milling machine, the axis has to be engaged to take the spindle from its

current location on the workspace to the tool magazine pick up position and back and this

required energy. Ultimately, the model did not incorporate cutting conditions of cutting

speed, feed and depth of cut and hence did not maximise the chance of being an

information source for process planning.

There are also other approaches to modelling of energy consumption in machining as

shown in Table 4.3. These are based on some sort of efficiency measure. This can be total

energy normalised by the volume machined. While these may be interesting as a

benchmarking measure, Diaz et al. [46], Draganescu et al. [45] and Li and Kara’s [44]

specific energy models do not directly give the energy footprint for a machined component

nor do they render themselves supportive to process planning which needs cutting speeds,

feeds and depth of cut information to be modelled explicitly.


56

Table 4.3: A summary of specific energy models in machining

Author(s) Energy in Machining Model

Diaz et al.

[46]

(4.6)

where ecut is the specific cutting energy, k is a constant and has units

of power and b represents the steady-state specific energy.

Draganescu

et al. [45]

(4.7)

where Ecs is the specific energy consumption, Pc is the cutting

power, is machine tool efficiency and z is the material removal

rate.

Li and Kara

[44]

(4.8)

where in kW/cm3 is the specific energy consumption; is

the material removal rate; Co, and C1 are empirical coefficients and

are not the same as the specific cutting energy and idle power

because the empirical approach considered the machine tool to be a

single holistic system. Unfortunately this hides vital information

about the machine tool design and work piece machinability.

4.4 Research Motivation

The motivation for this work was to contribute towards an improvement in the modelling

capability for energy requirements in mechanical machining, in particular improving the

explicit modelling of the machine tool, workpiece machinability and cutting variable

impact. It is essential to raise the integrity of such models and data so that they can be used

in eco-friendly process planning.


57

4.4.1 New Improved Model for Direct Electrical Energy Requirement in Machining

Considering Equation 4.1 and the new classification in Figure 4.1, the model for direct

energy requirements in machining can further be re-organised into Equation 4.9a.

(4.9a)

where Et is the direct total energy requirement, Pb , Pr and Pcool in W are the basic and

ready state power (power increment above basic power to bring the machine about to cut

position) and coolant pumping power requirements respectively, tb and tr in seconds are the

basic and ready time respectively and with units of kJ/cm3

is the specific cutting energy

which is closely related to the work piece machinability and the specifics of the cutting

mechanics; in cm3/s is the rate of material processing; and is the cutting time in

seconds. Taking into account Diaz’s et al’s [46] approach, represents the average

power requirements for non cutting approach and retract moves over the component and

represents the total time duration in seconds of these non-cutting moves. Obviously in

machining the objective is to keep the non cutting time as short as possible in order to

improve machine actual cutting utilisation.

Equation 4.9a can further be re-organised into Equation 4.9b.

(4.9b)

4.4.2 Experimental Investigation

To validate the mathematical approach suggested by Equations 4.9a and 4.9b, cutting tests

were done in milling to characterize energy requirements and further develop the model

according to observed electrical energy demand patterns. This was extended into the

application of the model for facing off an x-y plane surface on a component on a vertical


58

axis milling machine. The machines used were a CNC MHP lathe, Takisawa CNC milling

machine and a Mikron HSM 400 High Speed Milling Centre.

A Fluke 345 Power Quality Clamp Meter was clamped on the power bus at the back of the

machine tool system under investigation and used for current measurement. Fluke 345 has

in-built functions for power measurement, Oscilloscope and Data Logger in a single, hand-

held tool. True-rms ac and dc current measurements up to 2,000Amps can be measured

without disconnecting the load due to the hall effect of the instrument. The switchable low-

pass filter also allows effective measurements on variable speed drives and eliminating

noise from other electrically noisy equipment.

4.5 Results and Discussions

4.5.1 Energy consumption for machine modules and auxiliary units

In order that the energy demand of the machine modules be properly accounted for, and to

understand the dominant energy consumers, a direct assessment of the energy demand of

machine modules was undertaken. A CNC MHP lathe with Open MDSI architecture,

Takisawa CNC milling machine and Mikron HSM 400 high speed machining centre were

tested. To measure the electrical current drawn by the machines using the Fluke 345 power

clamp meter, current flow was recorded when the machine was switched ON and then

individual auxiliary units were identified through the electrical circuitry. To characterize

the electrical energy requirements by the machine, the current readings were recorded

without any cutting operation. The servos and spindle were then manually indexed through

the jog mode to measure the current during home positioning and tool change.

The power needed for switching on the machine modules of the CNC MHP lathe with

Open MDSI architecture was 1,229W. The machine start-up consumed 3,537W of power.


59

This was due to the fact that at start-up, most of the auxiliary units are powered. The rapid

movement to home location (axes jog) had required 2,394W. Rotating the spindle without

cutting (idle condition with spindle on) at a speed of 1,000 rpm required 3,594W.

Figure 4.2 shows the distribution of power consumption of the machine modules, auxiliary

units and essential motions based on machine tool states of “Basic” and “Ready”. The tip

or cutting state is not shown since this is a study for a non cutting operation. The results

show that the power demand of the basic states is 53%, 72% and 53% for CNC MHP lathe,

Takisawa CNC milling machine and Mikron HSM 400 High Speed Milling Centre

respectively, of the total power requirements for a machine operating at no cutting load.

The “Ready states” power budget is 47%, 28% and 47% respectively. This shows that the

intermediate actions of getting the machine ready have a significant power demand though

lower than start-up.

Therefore, it is important that the power demand of the “Ready” state is included in the

estimation methodology of the total energy demand for machine tool system as shown

Equation 4.9b. Hence, the total energy demand of machine tool system could be estimated

using Equation 4.9b.


60

Figure 4.2: Basic and ready states power relationship

The power requirements for individual aspects of the CNC MHP lathe, MAC-V2 Takisawa

Milling Machine, and Mikron HSM 400 high speed machining centre are shown in Figures

4.3, 4.4 and 4.5. For example taking the MHP CNC lathe, it is noted that the machine

start-up (24.04%), spindle running (24.43%), servo home location (16.27%), fluid pumping

(14.85%) and main switch (8.35%) dominate (>80%) the power demand of the ‘Basic’ and

‘Ready’ states of the machine tool under investigation. These are the key areas for

improvement for eco-design of this type of machine tool for machine utilization and

optimization.

53%

72% 63%

47%

28% 37%

0

20

40

60

80

100

MHP CNC Lathe MAC-V2 Takisawa

Milling Machine

Mikron HSM 400 High

Speed Milling Centre

Po

wer

Dem

an

d (

%)

Basic Machine Tool State Ready Machine Tool State


61

Figure 4.3: Non-cutting power consumption distribution of the MHP MDSI CNC Open

Lathe machine


62

Figure 4.4: MAC-V2 Takisawa Milling Machine auxiliary units power demand


63

Figure 4.5: Mikron HSM 400 high speed machining, auxiliary units power demand

In the cutting state, other auxiliary modules are activated which also consume electricity.

In this category are the tool change system, spindle speed acceleration or deceleration and

coolant pumps.

4.5.2 Tool Change and Spindle speed- power characteristics

The tool change process accesses the tool magazine for tool selection processes based on

the programmed NC codes. In the event of a machining task, as the machine tool is

switched ON, electrical current flows through the system to activate the machine modules

to get to the basic state as previously described. Just before actual cutting starts, there will

be a tool change action (could be null in some machine tools system as in the case of fixed


64

spindle machine tool, vertical or universal milling machines) at which the machine tool

now completes the rapid axis movement to a point where the machine tool is in a Ready

state. The energy demand for tool change task can be estimated as shown in Equation

4.10.

(4.10)

where and , represents power demand in W and time in s respectively for tool

change.

The next energy consuming unit of a machine tool system is the spindle. Its analysis is

complex [38]. However, direct measurement and/or statistical modelling of the spindle

power demand characteristics can be estimated with simplifications such as neglecting the

power loss due to friction, vibration of the bearing units, heat, viscosity of the spindle

lubricant. The assembled spindle of a machine tool generally consists of drives, motor and

mechanical transmissions. The energy efficiency of drive component and power output

characteristics depends on the ratio of delivered power to consumed power and it is

therefore the efficiency of the system. The spindle is subject to accelerations and

deceleration during machining processes. This characteristic affects power demand.

4.5.3 Effect of spindle speed on energy required by a DC motor driven MAC-V2

Takisawa Milling Machine

Current consumption of different spindle speeds was recorded at no cutting on the MAC-

V2 Takisawa Milling machine using the Fluke 345 power clamp meter. A tool holder

diameter of diameter 50 mm with four uncoated carbide tool inserts, TPKN1603PPTR-

P30, and model number Bristol Erickson 10-527-008-1.P5030 with an overhang of

105.16mm. The material and process parameter are as shown in Table 4.4.


65

Table 4.4: Workpiece type and process parameters

Parameter

Value for Takisawa Milling

Machine (with DC servo

motor model 20M, spindle

A06B-0652-B)

Value for Mikron HSM 400

Machining Centre (with

HVC140-SB-10-15/42-3F-

HSK-E40 spindle)

Workpiece

Hardness

Spindle speed

Feed speed

Cutting depth

Cutting Fluid

Tool holder Diameter

Stainless Steel T316L

220 Vickers

650 RPM

75 mm/min

0.5mm

Blasocut BC25

50 mm

Stainless Steel T316L

220 Vickers

650 RPM

500 mm/min

0.5mm

Blasocut BC25

8 mm

During the analysis, it was observed that the spindle exhibited three different

characteristics when running in non-cutting mode. These could be related to the power

spindle characteristics curve as shown in Figure 4.6. The zones were identified as zone A,

B and C. The rate at which the spindle power required rises with increase in spindle speed

depended on the spindle design and spindle power characteristics as shown in Figures 4.6

to 4.9. The influence of spindle speed on spindle power demand was evaluated and a

regression equation with R-squared of between 97 - 100% was obtained. It is therefore

possible to estimate the power demand of the spindle for each zone using the power

equation as shown in Equations 4.11, 4.12 and 4.13. The choice of spindle power

consumption equation depends on the spindle speed selected during machining processes.

From Figure 4.6 and Figure 4.7, for the power-spindle speed characteristics for spindle

speed ranges between 600-1900 rpm, the power model in Equation 4.11 should be used for

MAC-V2 Takisawa CNC Milling machine.


66

(4.11)

where is the spindle power and N, is the spindle speed.

Figure 4.6: Power-Speed Characteristics of a MAC-V2 Takisawa Milling Machine tool and

3 zones for energy profile

0

1000

2000

3000

4000

5000

6000

7000

0 2000 4000 6000 8000

Po

we

r (W

)

N (rpm)

Zone A Zone B Zone C

(6000, 5050)

(1500,5950) (4500,5950)


67

Figure 4.7: MAC-V2 Takisawa Milling Machine no load power- spindle speed

characteristic in Zone A to 1500 rpm

For Zone B, spindle speeds ranges 2000-5000 rpm,

(4.12)

y = 0.8518x - 345.26

R² = 0.955

350

700

1050

900 1200 1500

Po

wer

(W

)

Spindle Speed (RPM)

Power (Watt) Linear (Power (Watt))


68


characteristic in Zone B to 5000 rpm

Likewise, for Zone C, spindle speeds ranges 4800-5600 rpm,

(4.13)

y = 1.181x - 1682.5

R² = 0.9774

450

1800

3150

4500

1600 2800 4000 5200

Pow

er (

W)

Spindle Speed (RPM)

Power Linear (Power)


69


characteristic in Zone C to 5500 rpm

It is therefore clear that the spindle power consumption equation to be used depends on the

spindle speed selected during machining processes as shown in Equations 4.11, 4.12, and

4.13. Hence, a generic model was formulated for the spindle speed power demand as

shown in Equation 4.14.

(4.14)

where Ps, is the spindle power demand, m, represent the spindle speed coefficient and N,

represent the spindle speed in rpm and C, a constant.

y = -1.5513x + 11423

R² = 1

2600

3400

4200

4600 5100 5600

Po

wer

(W

)

Spindle Speed (RPM)

Power Linear (Power)


70

4.5.4 Development of an improved and new energy model for milling processes

The work piece type and process parameter in Table 4.4 was used to undertake a face

cleaning cutting toolpath and the generated power –time graph is shown in Figure 4.10.

The area under the graph equates to the total energy demand of machining the workpiece

which were categorized into three zones thus; ‘Basic’, ‘Ready’ and ‘Cutting’ energy states

as previously explained.

Figure 4.10: Total Power Consumption Trend for Machining Tool paths

Based on the analysis of the three states of machine tools as depicted in Figure 4.10, the

energy demand of each state can be summed and the total energy demand of a machine

tool predicted.

240216192168144120967248241

4000

3000

2000

1000

0

Time(Sec)

Po

we

r(W

)

Total Power Demand of Machining Trial

BASIC STATE ZONE

READY STATE ZONE

CUTTING STATE ZONE


71

The tool life is an important characteristic in machining processes. It needs to be used to

effect a tool change so that surface finish and product precision is not compromised. Thus

incorporating tool life into the energy equation:

Total energy demand equation can therefore be re-written thus:

(4.15a)

Incorporating spindle power demand, from Equation 14 then;

(4.15b)

where represent tool change power and tool change time respectively.

The cutting time, t2 and Tool life T and material removal rate can be modelled for turning

and milling as a function of cutting velocity variables thus enabling the use of the equation

in process planning.

4.6 Validation of Direct Energy Model during Milling processes

In order to validate the energy model in Equation 4.15, machining trials were conducted on

the Takisawa Milling machine. A tool holder diameter of diameter 50mm with four

uncoated carbide tool inserts, TPKN1603PPTR-P30, and model number Bristol Erickson

10-527-008-1.P5030 with overhang of 105.16mm was used. The test piece was also

machined on a Mikron HSM 400 High Speed Machining Centre. An SHR-161-6160K 8

mm diameter carbide mill end cutter and an HSK40E-VC13-90 tool holder were used. The

material and process parameters are stated in Table 4.3. The power consumption of the

corresponding power states of the machine tools system were measured with the Fluke 345

power meter and the results are shown in Table 4.5.


72

Table 4.5: Power and total energy demand estimation of machine tools under investigation

Machine

Tools

Power Consumptions (W) Energy Demand (Wh)

%

Error

Basic

State

Power

(W)

Ready

State

Power

(W)

Tool

Change

Power

(W)

Air

Cutting

(W)

Coolant

(W)

Total

Energy

Measured

Total

Energy

Calculated

%

MAC-V2

Takisawa

Milling

Machine

2736 496 0 0 776.33 391 426 8

Mikron

Machining

Centre

2516 401 920 55.42 1790 402 394 2

Note: Zero represents ‘do not need’ for event i.e. tool already in spindle and single pass

tested.

The total energy demand on MAC-V2 Takisawa Milling Machine and Mikron HSM 400

machining centre calculated using Equation 4.15 was 426 WHr and 394 WHr respectively.

The Fluke 345 Clamp meter gave measured values of power and cycle time which lead to

an area under the graph of 391 WHr and 402 WHr respectively. The deviation of the

prediction from the energy calculated from the experimental measurement of current

demand was only 8% and 2% for the Takisawa and the Mikron CNC milling machine

respectively. These values further prove that the energy model as stated in Equation 4.15

can be used as a generic and robust estimate of the energy requirements in machining.

4.7 Conclusion

The electrical energy requirements for a machining process needs to be modelled in order

to account for and optimize the monetary and environmental impact of electricity usage in


73

manufacture. This paper classified three categories for the energy states of machine tools.

In addition, to the start-up and tip (cutting) energy an intermediate step of the “ready state”

was proposed. The ready state brings the cutting tool and work piece to a proximity state or

an about to cut state. Current measurements were then done on an MHP CNC lathe, MAC-

V2 Takisawa Milling Machine and Mikron HSM 400 Milling centre and some conclusions

can be drawn from the study.

1. There is growing evidence from literature that the tool tip energy is typically lower

than the energy required by a machine tool operating at no load. For this reason it is

important to further understand what constitutes the power requirements and hence

energy usage for a machine tool. The study shows that machine tools should not be

left in a no-cutting mode unnecessarily otherwise its energy footprint is

significantly increased.

2. On a CNC MHP lathe machine, the power requirement of the basic machine state

(the machine start-up state) was 63% of the total power requirement for a machine

running at no load. At 37% the ready state power is smaller but significant and

hence should be modelled more explicitly and accurately.

3. The case study on the MHP CNC lathe machine shows an interesting fact that in a

no-cutting mode, the bulk of the power demand arises from machine start-up

(45%), spindle power (15%), servo home location (10%), hydraulic pumps (8.9%)

and coolant pumps (8.2%). These are the key areas of focus on the redesign of the

MHP lathe to target a lower energy footprint resource. Fluid pumping was a major

energy consumer as it required 17.1% of the total power. The design of more

energy efficient pumps should be a target.


74

4. Total energy demand can be estimated using the generic model presented. The

model was developed to consolidate the following key machine tool energy trends:

a. In addition, to the Basic and Cutting States, explicitly modelling the energy

required to take a machine tool from the Basic State to a state where the

axis and tool is ready for action and about to cut. This has been named the

Ready State.

b. Modelling of energy requirements for spindles based on spindle speed used

and machine tool spindle – power characteristic zones.

c. Accounting for the number of tool changes required and associated energy

for tool change by incorporating the tool life.

d. Modelling energy demand for air cutting during toolpath execution to

account for repositioning the cutting tool.

e. Modelling energy with an explicit consideration and incorporation of

cutting speeds, feed and depth of cut to support process planning.

f. Acknowledging that there are differences in number and design of machine

tool accessories/modules.

5. Further work is required to compare the data presented here with other machine

tools and to model the energy consumed by machine axis and its dependence on

G01, G02 and G03 axis as well as plane of interpolation.

75

CHAPTER 5

IMPACT OF UN-DEFORMED CHIP THICKNESS ON

SPECIFIC ENERGY IN MECHANICAL MACHINING

PROCESSES

5.1 Abstract

Energy demand reduction is a grand challenge for manufacturing sustainability in order to

reduce the escalating cost of energy and to cut down on the carbon footprint of

manufacturing processes. The direct electrical energy requirements in manufacturing and

machining in particular can be modelled from the basic energy required by the machine

tool and the energy for actual material removal (tip energy). However, energy centric

modelling of manufacturing processes is in its infancy and related material processing data

is limited and of low integrity. It has often been assumed that the specific cutting energy is

a constant value for particular workpiece materials. This paper is inspired by the

mechanistic force modelling and the size effect phenomenon in machining. The aim of this

work was to investigate the specific electrical energy demand in machining and model its

relationship to thickness of material removed. To this end, specific energy evaluated in

cutting tests was empirically modelled. This work was comprehensive in that it covered a

wide range of un-deformed chip thickness as well as three workpiece materials. A new and

fundamental understanding of the variation of specific energy with chip thickness is

Chapter 5 Impact of un-deformed chip thickness on specific energy in mechanical machining

processes

76

reported for the first time. This can be an evidence base for a generic model for the

dependence of specific energy on un-deformed chip thickness. This information is vitally

important to raise the integrity of energy labelling of machining processes and as a

backbone to process optimisation in order to reduce electrical energy demand and promote

manufacturing sustainability.

Keywords: specific cutting energy coefficient; un-deformed chip thickness; manufacturing

sustainability.

5.2 Introduction

Reducing electricity consumption and CO2 emission is the driving force for optimising

energy demand in manufacturing industry. This addresses the objectives of manufacturing

sustainability and resource efficiency. Optimization of direct electrical energy consumption

and improving the energy efficiency of mechanical machining processes is influenced by

material characteristics and process parameters. The fundamental approach to modelling

energy in manufacturing processes is based on the ‘Basic’ energy state and ‘Tip’ energy

[14]. This has been extended recently to include the ‘Ready’ energy state [62]. These states

are described by others as start-up, idle and cutting states [38]. For a manufacturing

process, energy is required to start the production equipment or resource (Basic energy), to

prepare the process for value adding activity (Ready State) and finally for actual

manufacturing for example ‘Tip’ energy in machining. The tip energy is the energy

demand at the cutting tool tip (cutting edge) and represents the energy for actual material

removal. A fundamental energy demand model was proposed by Gutowski et al. [31] as in

Equation 5.1.

tvkPE 0 (5.1)


processes

77

where E represents the total energy demand by the manufacturing process in J, P0 is the

power in W demanded by the production equipment in this case the machine tool when

operating at zero load , t is the production time in s, v is the rate of material processing in

mm3s

-1 and k is specific cutting energy in Jmm

-3.

Gutowski’s et al. [31] energy model presented above is in synergy with ‘The Cooperative

Effort in Process Emission’ (CO2PE!) [24] approach. The aim of the CO2PE!

methodology was to standardise energy reporting and data collection in manufacturing

processes. This initiated a more unified and globally compatible classification of energy

consuming machines in the manufacturing sector. In the CO2PE! methodology, machine

tool states was classified into two categories: ‘Basic State’ and ‘Cutting State’. These

classifications are aimed to define and report, on a global scale, how energy consumption

in machine tools is distributed.

Few researchers for example Li et al. [63] investigated the specific cutting energy of a unit

process and presented energy models as shown in Table 5.1 and Equations 5.2 to 5.5. Also,

Wang et al. [64] reported that the specific energy can be used as a measure of energy

efficiency in manufacturing. These approaches normalised the total energy consumption in

machining to the volume of material removed. These approaches do not disaggregate

energy consumption into the standardised framework as proposed by CO2PE! and as

implied by Equation 5.1. Thus, there still exists a knowledge gap with regard to

characterising and modelling the specific tip energy requirements in machining. This vital

information will enable consideration of differences in workpiece material when modelling

and selecting optimum cutting conditions for minimum energy demand in machining.


processes

78

Table 5.1: Global specific energy models found in literature combining both basic and tip

energy

Authors Specific energy model

Draganescu et al. [45]

Z

PEcs c

60 (5.2)

Li and Kara [44]

MRR

CCSEC 1

0 (5.3)

Diaz et al. [46] b

MRRkecut

1* (5.4)

Li et al. [63]

MRRk

MRR

nkkSEC

1210 (5.5)

where Ecs, SEC, ecut represents specific energy consumption, Pc is the cutting power,

is machine tool efficiency, C0 and C1 are empirical coefficients, Z and MRR represents

the material removal rate, k is a constant and has units of power and b represents the

steady-state specific energy, k0 is the specific energy requirement in cutting operations,

k1 is the specific coefficients of spindle motor, k2 is the constant coefficient of machine

tools and n is the spindle speed in rev/s.

For process level energy efficient machining to be achieved, an understanding of the

influence of material characteristics and process variables on the specific cutting energy

coefficient is necessary. Process planners have to select optimum process variables for

achieving manufacturing sustainability and energy efficient machining in particular [42].


processes

79

Inappropriate selection of cutting variables can hinder energy savings. For example,

selection of cutting conditions can lead to significantly higher specific cutting energy in

grinding operations [65]. Moreover, specific cutting energy is an important parameter

which is directly related to chip morphology, cutting forces, tool wear and machined

surface integrity [50]. Kuram et al. [66], related specific energy values to cutting fluid

effectiveness, tool wear and machined components surface roughness.

5.3 The Wider Importance of Specific Energy Data

The specific cutting energy varies for different machining processes even when the work

piece material properties remain the same. For example, the specific cutting energy for

grinding operations is higher compared to other machining processes like turning and

milling. This is due to the inefficient nature of the abrasive grit in cutting compared to the

use of defined cutting edges as in other mechanical machining processes. This knowledge

of specific energy can be important because for example, the specific cutting energy in

grinding operations influence surface integrity of machined components [67]. The specific

energy can also be linked to the process mechanisms. Ghosh et al. [49], reported that chip

formation, ploughing, primary and secondary rubbing phenomenon are major factors

affecting the surface integrity in grinding. Polini and Turchetta [68], presented a model for

the specific energy in stone grinding. In their analysis, it was shown that, the specific

energy is related to the equivalent chip thickness by a power function. The specific energy

range for grinding stone was shown to decrease with the increase of the equivalent chip

thickness from a maximum of 25 kJ/mm3 at 200 mm/min to 6 kJ/mm

3 at 600 mm/min.

Workpiece surface integrity [51] has been correlated to specific cutting energy for ‘High

Speed Machining’ conditions. The cutting edge angle [47], swept angle [69], rake angle


processes

80

and other cutting tool geometries have distinct influences on the specific cutting energy.

Burr formation has also been reported as linked to higher specific cutting energy. In their

analysis with AISI 1045, Zhang et al. [70-71], reported that “Poisson burr” height

increased when the ratio of un-deformed chip thickness to the cutting edge radius was less

than 1. This result implies that higher specific cutting energy can be associated with larger

burr size and this can be an important attribute for process monitoring.

5.4 Size effect in machining

In milling, chip formation not only depends on material characteristic and cutting tool

geometry, but also on the ratio of the feed per tooth to the cutting edge radius. For

example, in machining, the minimum chip thickness is the ratio of feed per tooth to cutting

tool edge radius below which no chips are formed. This is the lower limit for machining.

The minimum chip thickness has been reported to be in the range of ratio of un-deformed

chip thickness to cutting edge radius of 0.2 - 0.4 [72]. At a value below the minimum chip

thickness the process is dominated by high frictional force due to rubbing and plastic

deformation. If the ratio of un-deformed chip thickness to the cutting edge radius is less

than 1, the dominance of the size effect phenomenon increases and rubbing and ploughing

is associated with size effect in machining [73]. This phenomenon increases the specific

cutting forces [74]. Filiz et al. [75] reported that the size effect induced a nonlinear increase

in specific cutting force. The concept of a non-linear variation of specific cutting force and

specific cutting pressure has been the backbone of empirical force modelling in machining.

Table 5.2 shows other specific cutting pressure models as documented in literature.


processes

81

Table 5.2: Models of specific cutting pressure

Author(s) Specific cutting pressure

model

Kronenberg [76] ha

s sCtK (5.6)

Schroder [77] 1 hKs (5.7)

Kienzle [78] x

s hKsK 1.1 (5.8)

Hucks1 [79] 25.0

1

qCKs (5.9)

Hucks2 [79] 11 BhAKs

(5.10)

Sabberwal [80] x

s ChK (5.11)

where Ks represent the specific cutting pressure, C, α, β, K, C1,

A, B, a and x are constants depending upon the workpiece

material and cutting tool geometry, t, is the depth of cut, h is

the chip thickness at any instant, s is the feed per tooth and q

is the area at any instant.

It is clear from the review above that a number of researchers over the years have modelled

the specific cutting force and specific cutting pressure through the measurement and

estimation of force component and the material removal rate. Others attributed the specific

cutting force trend to the size effect in machining. Chip thickness, machining mechanisms


processes

82

and the so called ‘size effect’ should influence the magnitude of the specific energy

required in machining.

5.5 Aim and Objective

The aim of this work was to investigate the specific electrical energy requirement in

machining and its relationship to thickness of material removed and ‘size effect’ and to

improve the integrity of data for specific cutting energy coefficients. The methodology was

to undertake cutting tests at set values of un-deformed chip thickness and to evaluate the

specific energy coefficient. During the cutting tests, electrical current demand was

measured and the variation of power requirement for different material removal rate was

evaluated and the gradient used as a measure of the specific energy requirement. This was

achieved through the direct measurement of the electrical energy consumption by varying

the un-deformed chip thickness at different process parameter levels, cutting tool geometry

and swept angles. This work will contribute towards the development of a realistic and

robust model for estimating the specific cutting energy coefficient, and will provide

valuable data for resource efficient machining in particular energy centric production

planning.

5.6 Modelling and Experimental setup

5.6.1 Research Methodology

Cutting tests were planned to assess the effect of chip thickness on the specific energy

demand in machining. The idea was to undertake cutting tests in single tooth milling mode

and at the same time evaluate the current drawn by the machine. This enables tracing the

impact of chip thickness. The electrical current was then used as the basis for calculating


processes

83

the power demand. If power demand is considered with machining time, then the area

under the power-time curve is the energy consumed. When the cutting tests are done at

different material removal rates, the plot of power demand versus material removal rate

has a gradient equal to the specific cutting energy in Jmm-3

. The specific cutting energy

was evaluated at a defined chip thickness and for a number of chip thicknesses and the

variation of the specific cutting energy with chip thickness was characterised.

5.6.2 Cutting Test Details

Machining trials were conducted on a Mikron HSM 400 machining centre. This machine

has a HVC140-SB-10-15/42-3FHSK-E40 spindle and Heidenhain TNC 410 NC controller.

The investigation was done for three different materials shown in Table 5.3. The chemical

composition and cutting parameters of the three different workpiece materials are provided

in Table 5.3. The materials selected for investigation were aluminium AW6082-T6 alloy,

AISI 1045 steel alloy, and titanium 6Al-4V alloy. The workpiece materials were selected

to represent the major applicable engineering material classes. The cutting speeds used

were derived from cutting tool manufacturers recommendations. The depth of cut was

determined by the thickness of the workpiece material and the feedrates were selected to

overlap the process window. The radial width of cut was varied to create different material

removal rate. This parameter has a low influence on basic energy and hence is beneficial to

vary when investigating tip energy.

During the machining process, cutting was undertaken on the straight cutting edge by

avoiding engaging the nose radius in side milling. The set up was near- orthogonal

machining. A single insert was used and the conditions were such that the maximum

number of cutting edges engaged at any instance was 1. This ensured that the variation of

power requirement could be related to the chip thickness. The investigations covered seven


processes

84

different feedrates (un-deformed chip thickness). As mentioned before, at each feedrate,

the material removal rate was varied by machining at four different radial width of cut.

Each experimental run was repeated three times.

Table 5.3: Cutting parameters for milling trials

Aluminium

AW6082-T6 Alloy

AISI 1045

steel alloy

Titanium 6Al-

4V alloy

Feed (mm/tooth) 0.01 – 0.55 0.01 – 0.55 0.01 – 0.55

Depth of cut (mm) 3.5 3.5 3.5

Cutting velocity (m/min) 210 156 80

Radial width of cut

(mm)

0.25 – 1.00 0.25 – 1.00 0.25 – 1.00

Tool diameter (mm) 8 8 8

Chemical composition

(Max)

1%Mn, 0.5%Fe,

1.2%Mg, 1.3%Si,

0.1%Cu, 0.2%Zn,

0.1%Ti, 0.25%Cr,

Balance Al.

0.46%C,

0.40%Si,

0.65%Mn,

0.40%Cr, 0.10

Mo, 0.40%Ni,

0.63% Others

89.37%Ti,

6%Al, 4%V,

0.08%C,

0.3%Fe,

0.2%O2,

0.05%N

Workpiece material

Hardness

HV 104.5 HV 238.2 HV 353.2


processes

85

A tool holder E90X-D08-C10-06 with an overhang of 25 mm was used. The holder was

mounted with a single insert, SOMT-060204-HQ and used for an end milling operation in

order to mimic orthogonal cutting. The insert was a general purpose TiAlN coated carbide

insert with geometry shown in Table 5.4. This was used for milling the three selected

materials to enable adequate comparison and standardization between the different

materials. The machining trials were conducted under a dry cutting environment in order

not to mask the differences brought by workpiece materials.

Each workpiece material was 100 mm x 50 mm x 3.5 mm. The material was held in a vice

with a protrusion of 12 mm, just enough to accommodate a set of machining trials. This

was done in order to reduce the workpiece and cutting tool vibrations to the barest

minimum. The length of cut was 50 mm. Each experimental trial was repeated three times.

A new cutting tool edge was used in order to minimize the effect of tool wear. The

electrical current consumption during the machining process was measured with a FLUKE

345 Power Clamp meter.

Table 5.4: Cutting tool geometry

Geometry Values

Nose radius (mm) 0.4

Edge radius (µm) 60

Rake angle (deg.) +5

Rake face primary chip

breaker land (µm)

60

Clearance angle (deg.) 7


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86

5.6.3 Influence of varying cutting parameters on power demand during mechanical

machining processes

In machining, there is the need to understand the dominant parameters that influences the

specific energy. This information is useful in developing process control strategies for

reducing energy demand of different workpiece materials. A pilot test was therefore

carried out to study the influence of process variables on power demand during machining

process. A dry milling operation was conducted on the Mikron HSM 400 with single

inserts of code SOMT-060204-HQ on AISI 1045 steel. The process variables investigated

were cutting velocity vc, feed fz, depth of cut ap and radial depth of cut ae. The

experimental design was an L9 Taguchi orthogonal array. The results were analyzed on

Minitab 16 software to access the effects of varying cutting parameters that influence the

power consumption in machining AISI 1045 steel. The experimental design and responses

were as shown in Table 5.5.

Taguchi experimental design can be used to identify significant input parameters or

process variables that can be used to control an output or response. The analysis can be

extended to the ranking of input dominant parameters and to the selection of optimum

input conditions. The purpose of this particular study was to identify parameters that could

be varied to generate different material removal rates and those that should be held

constant in order to model the effect of feedrates only (on specific energy). There was no

need therefore to undertake confirmation tests as is customary in process optimisation

studies.


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87

Table 5.5: Taguchi L9 Experimental Design and Responses

Cutting

Velocity, vc

(m/min)

Feed per tooth,

fz (mm/tooth)

Depth of

Cut,

ap

(mm)

Width of

Cut,

ae

(mm)

Material

Removal

Rate,

Q (mm3/s) Power (W)

100 0.1 0.5 0.6 1.33 3054.90

100 0.2 1.0 0.8 7.07 3074.07

100 0.3 1.5 1.0 19.89 3090.13

120 0.1 1.0 1.0 5.30 3113.68

120 0.2 1.5 0.6 9.55 3135.28

120 0.3 0.5 0.8 6.37 3164.69

150 0.1 1.5 0.8 7.96 3101.11

150 0.2 0.5 1.0 6.63 3084.31

150 0.3 1.0 0.6 11.94 3083.66

Figure 5.1 shows the main effects plot for power demand in machining. Using the

minimum the better objective, the highest point on the signal to noise ratio curve is the set

of cutting conditions which lead to minimum power requirements. Therefore, Figure 5.1

shows that in order to minimise power demand, a low cutting velocity of 100 m/min, low

feedrate of 0.1 mm/tooth, mid-range depth of cut of 1.0 mm and a low radial width of cut

of 0.6 mm must be selected. In Figure 5.1, the variable with the largest signal-to- noise

ratio gradient is the dominant parameter. Therefore, it is evident that the cutting velocity is

the dominant parameter as supported by Table 5.6. This is well accepted because in


processes

88

machining the machine tool ‘Basic’ energy state dominates power demand and a major

component of this is the spindle.

150120100

-69.75

-69.80

-69.85

-69.90

-69.95

0.30.20.1

1.51.00.5

-69.75

-69.80

-69.85

-69.90

-69.95

1.00.80.6

Cuting velocity (m/min)

Me

an

of

SN

ra

tio

s

Feed (mm/tooth)

Depth of cut (mm) Width of cut (mm)

Main Effects Plot for SN ratiosData Means

Signal-to-noise: Smaller is better

Figure 5.1: Key Process variable ranking for power demand in machining of AISI 1045

steel alloy


processes

89

Table 5.6: Effect ranking based on Minitab 16 analysis

Level Vc

(m/min)

fz

(mm/tooth)

ap

(mm)

ae

(mm)

1 3073 3090 3101 3091

2 3138 3098 3090 3113

3 3090 3113 3109 3096

Delta 65 23 18 22

Rank 1 2 4 3

From the results it can be inferred that varying either the depth of cut ap, and/or radial

width of cut ae, will have a lower impact on the power demand in mechanical machining

compared to changing cutting velocity or spindle speeds. The second most important factor

is the feed per tooth which drives the size effect in machining. Hence, in the subsequent

study and machining cuts to evaluate specific energy, the cutting velocity Vc, was kept

constant so that its dominant effect would not mask the modelling impact of feed per tooth

(chip thickness). The axial depth of cut was fixed by the thickness of the material hence the

radial width of cut was varied between 0.25 mm and 1.00 mm in order that different

material removal rates could be computed and power demand measured. This enabled

power and material removal rate to be plotted and specific cutting energy coefficient

evaluated.


processes

90


After milling on the Mikron HSM 400 high speed milling centre the power demand was

calculated from the measured current and the material removal rate Q for each set of

experiment was plotted against the power demand. The slope of each graph represents the

specific cutting energy coefficient in J/mm3 for the selected workpiece material at the

defined cutting conditions. This is in accordance with the modelling approach introduced

by Gutowski et al. [31] in Equation 5.1 where the basic energy is modelled separately from

the tool tip energy. As mentioned earlier, this approach also supports the EU based

COP2E! [24]. Figures 5.2, 5.3 and 5.4 showed the variation of power demand in machining

with material removal rate for cutting aluminium AW6082-T6 alloy, AISI 1045 steel alloy

and titanium 6Al-4V alloy respectively at a feed fz of 0.01mm/tooth. It is observed from

Figures 5.2, 5.3 and 5.4 that at the lowest feed of 0.01mm/tooth, the specific cutting energy

as represented by the slope of the graph was 13.08, 5.38 and 10.66 Jmm-3

with R2 of 0.96,

0.81 and 0.90 for aluminium AW6082-T6 alloy, AISI 1045 steel alloy and titanium 6Al-

4V alloy respectively. At this feed per tooth and axial width of cut of 3.5mm, the average

un-deformed chip thickness was evaluated to be 3µm using Equation 5.12 [65]. This un-

deformed chip thickness and the specific energy values would be used latter to evaluate the

dependence of the two.

s

df

hs

zavg

0

sin (5.12)

where havg represents the average un-deformed chip thickness in mm, fz is the feed in

mm/tooth and Ø is the swept angle in degrees and Øs is the swept angle in radians.


processes

91


aluminium AW6082-T6 alloy

Figure 5.3: Evaluation of specific cutting energy coefficient at 0.01 mm/tooth for AISI

1045 steel alloy

P= 13.08Q + 3018.2

R² = 0.96

3030

3045

3060

3075

3090

1.0 2.0 3.0 4.0 5.0

Pow

er (

W)

Q (mm3/s)

fz = 0.01 mm rev-1

ap = 3.5 mm

Vc = 210 m min-1

P = 5.38Q + 3076.9

R² = 0.81

3080

3085

3090

3095

3100

0.85 1.55 2.25 2.95 3.65

Pow

er (

W)

Q (mm3/s)

fz = 0.01 mm rev-1

ap = 3.5 mm

Vc = 156 m min-1


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92

Figure 5.4: Evaluation of specific cutting energy coefficient at 0.01 mm/tooth for titanium

6Al-4V alloy

Figures 5.5, 5.6 and 5.7 show the estimated specific cutting energy coefficient at a feed of

0.28 mm/tooth which is equivalent to an average un-deformed chip thickness of 97 µm. It

is observed from Figure 5.5, 5.6 and 5.7 and at the feed of 0.28 mm/tooth that the specific

cutting energy as represented by the slope of the graph was 0.78, 1.97 and 2.55 Jmm-3

with

R2 of 1.0, 0.96 and 0.94 for aluminium AW6082-T6 alloy, AISI 1045 steel alloy and

titanium 6Al-4V alloy respectively. Compared to a feedrate of 0.01 mm/tooth as in Figure

5.2, 5.3 and 5.4, in Figure 5.5, 5.6 and 5.7 the specific cutting energy significantly reduced

to values reported in literature [12]. The increase in specific energy for 0.01mm/tooth

compared to 0.28 mm/tooth can be attributable to increased ploughing and less dominant

shearing at the lower un-deformed chip thickness. This is expected because the tool edge

radius for the new tooth was evaluated to be 0.06 mm. This means machining at 0.01

P = 10.66Q + 2969.4

R² = 0.90

2974

2978

2982

2986

2990

0.4 0.9 1.4 1.9

Pow

er (

W)

Q (mm3/s)

fz = 0.01 mm rev-1

ap = 3.5 mm

Vc = 80 m min-1


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93

mm/tooth (3 µm un-deformed chip thickness) is associated with highly negative effective

rake angles.



P = 0.78Q+ 3091.3

R² = 1.0

3115

3145

3175

3205

30 57 84 111 138

Pow

er (

W)

Q (mm3/s)

fz = 0.28 mm rev-1

ap = 3.5 mm

Vc = 210 m min-1


processes

94


1045 steel alloy


6Al-4V alloy

P = 1.97Q + 3016.7

R² = 0.96

3070

3095

3120

3145

3170

3195

3220

25 45 65 85 105

Pow

er (

W)

Q (mm3/s)

fz = 0.28 mm rev-1

ap = 3.5 mm

Vc = 156 m min-1

P = 2.55Q + 3123.6

R² = 0.94

3140

3163

3186

3209

3232

3255

12 23 34 45 56

Pow

er (

W)

Q (mm3/s)

fz = 0.28 mm rev-1

ap = 3.5 mm

Vc = 80 m min-1


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95

The evaluation of specific energy was conducted for 0.01, 0.10, 0.19, 0.28, 0.37, 0.46 and

0.55 mm/tooth. For the purposes of saving space, the graphs shown are for the lowest,

middle and highest feed rates used. The detailed specific energy values obtained are shown

in Table 5.7. The results for the highest federate of 0. 55 mm/tooth (un-deformed chip

thickness of 190µm) are shown in Figures 5.8, 5.9 and 5.10. For this condition, the specific

cutting energy coefficient was 0.21, 1.47 and 1.13 with R2 of 1.0, 0.99 and 0.92 for

aluminium AW6082-T6 alloy, AISI 1045 steel alloy and titanium 6Al-4V alloy

respectively. The results at the highest feed settings reveal that the specific cutting energy

in the shear cutting regime remains fairly constant despite an increase in feedrate.



P = 0.21Q + 3372.8

R² = 1.00

3385

3400

3415

3430

65 135 205 275

Pow

er (

W)

Q (mm3/s)

fz = 0.55 mm rev-1

ap = 3.5 mm

Vc = 210 m min-1


processes

96


1045 steel alloy


6Al-4V alloy

P = 1.47Q + 3171.9

R² = 0.99

3230

3278

3326

3374

3422

3470

48 87 126 165 204

Pow

er (

W)

Q (mm3/s)

fz = 0.55 mm rev-1

ap = 3.5 mm

Vc = 156 m min-1

P = 1.13 + 3207.4

R² = 0.92

3220

3255

3290

3325

23 43 63 83 103

Pow

er (

W)

Q (mm3/s)

fz = 0.55 mm rev-1

ap = 3.5 mm

Vc = 80 m min-1


processes

97

A summary of the specific energy coefficient obtained from the study is shown in Table

5.7 for all the feedrates tested.

Table 5.7: Experimental values of k at different un-deformed chip thickness h

Data obtained in this study

Feed, fz

(mm/tooth)

0.01 0.10 0.19 0.28 0.37 0.46 0.55

havg (µm) 3 35 66 97 128 159 190 Kalpakjian

and Schmid

[12].

Spec

ific

cutt

ing e

ner

gy

(Jm

m-3

)

Aluminium

AW6082-T6

Alloy

13.08 1.99 1.52 0.78 0.87 0.21 0.21 0.40- 1.00

AISI 1045

steel alloy

5.38 3.73 2.08 1.97 1.65 1.55 1.47 2.00- 9.00

Titanium alloy 10.66 4.45 3.78 2.55 2.65 1.14 1.13 2.00- 5.00

The specific cutting energy coefficient was plotted against the un-deformed chip thickness

as shown in Figures 5.11, 5.12 and 5.13.The relationship between specific energy and un-

deformed chip thickness can be represented by a power function. The graphs clearly show

that the specific energy coefficient in cutting is fairly constant at higher un-deformed chip

thickness (typical of roughing operations) but increases exponentially at low (typical of

finishing and/or micro-scale machining operations) un-deformed chip thickness. The

exponential increase in specific energy at reduced un-deformed chip thickness can be


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98

attributable to highly negative rake angles and increased ploughing and friction. The chip

thickness to specific energy trend mirrors that in specific cutting force. Table 5.2 shows

specific cutting pressure model found in literature.

Figure 5.11: Specific cutting energy model of aluminium AW6082-T6 alloy

Figure 5.12: Specific cutting energy model of AISI 1045 steel alloy

k = 0.071h-0.94

R² = 0.89

0

4

8

12

16

20

0.00 0.05 0.10 0.15 0.20

k (

Jm

m-3

)

h (mm)

k = 0.900h-0.33

R² = 0.91

1.00

2.50

4.00

5.50

0.00 0.05 0.10 0.15 0.20

k (

Jm

m-3

)

h (mm)


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99

Figure 5.13: Specific cutting energy model of titanium 6Al-4V alloy

Based on Figures 5.11, 5.12 and 5.13, new specific energy equations were deduced and

these are shown as Equations 5.13, 5.14 and 5.15.

94.0*071.0 hkAl (5.13)

33.0*900.0 hkS (5.14)

51.0*670.0 hkTi (5.15)

where kAl, kS and kTi represents the specific cutting energy in Jmm-3

of aluminium

AW6082-T6 alloy, AISI 1045 steel alloy and titanium 6Al-4V alloy respectively and h is

the un-deformed chip thickness in mm.

Figure 5.14 compares the specific energy trend for aluminium AW6082-T6 alloy, AISI

1045 steel alloy and titanium 6Al-4V alloy. The variations of specific energy with un-

deformed chip thickness for three workpiece materials follow similar power function trend.

It can be seen that aluminium AW6082-T6 alloy specific energy coefficient varies from

k = 0.670h-0.51

R² = 0.86

0.0

3.0

6.0

9.0

12.0

0.000 0.050 0.100 0.150 0.200

k (

Jm

m-3

)

h (mm)


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100

13.08 to 0.21 Jmm-3

, and for titanium 6Al-4V alloy the range is 10.66 to 1.13 Jmm-3

while

the range for AISI 1045 steel alloy is 5.38 to 1.47 Jmm-3

. It is observed that for aluminium

alloys the specific energy for cutting can be significantly high at very low un-deformed

chip thickness to significantly low at chip thicknesses typical of conventional machining.

Figure 5.14: Specific energy comparison for aluminium AW6082-T6 alloy, AISI 1045

steel alloy and titanium 6Al-4V alloy

From the three workpiece materials, it can be concluded that a generic model for the

relationship between specific energy and the un-deformed chip thickness can be

represented by Equation 5.16.

x

ee hKk (5.16)

k(Ti) = 0.670h-0.51

k(S) = 0.907h-0.32

k(Al) = 0.071h-0.94

0.0

3.0

6.0

9.0

12.0

15.0

0.00 0.05 0.10 0.15 0.20

k (

Jm

m-3

)

h (mm)

k_Titanium k_1045 k_Aluminium


processes

101

where ke is the specific cutting energy in Jmm-3

at the required un-deformed chip thickness

and Ke is the specific area energy in Jmm-2

at un-deformed chip thickness of 1 mm, and h is

the un-deformed chip thickness in mm while x is the specific energy exponent.

5.7.1 Specific energy and size effect

The ratio of the un-deformed chip thickness to the cutting edge radius is one of the key

measures for defining the size effect in machining. The specific energy was plotted as a

function of this ratio in order to elucidate the effect of machining length scale on the

energy efficiency for material removal. The coefficient of the graphs in Figure 5.15, 5.16

and 5.17 is equivalent to the specific energy at un-deformed chip thickness equal to tool

edge radius. This value is the upper limit for the specific energy experienced in machining

when shearing mechanisms instead of ploughing dominate. These experimental values

derived are in agreement with the range of values published by Kalpakjian and Schmid

[12] (i.e. 0.40 to 1.00, 2.00 to 9.00 and 2.00 to 5.00 Jmm-3 for aluminium AW6082-T6

alloy, AISI 1045 steel alloy and titanium 6Al-4V alloy respectively). It therefore implies

that the empirical modelling adopted is a robust approach for determining the specific

cutting energy for various materials. So, from Figure 5.15, 5.16 and 5.17, the coefficients

of 1.01, 2.26 and 2.78 indicate that aluminium AW6082-T6 alloy has the lowest average

specific energy in shear dominated machining followed by AISI 1045 steel alloy and

titanium 6Al-4V alloy in increasing order of difficult-to-cut materials. Thus, the tip energy

in machining processes and environmental impact of materials processing is influenced by

material machinability as driven by workpiece material properties.


processes

102

Figure 5.15: Specific energy size effect in machining of aluminium AW6082-T6 alloy

Figure 5.16: Specific energy size effect in machining of AISI 1045 steel alloy

k = 1.007(h/re)-0.94

R² = 0.89

0

4

8

12

16

20

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

k (

J/m

m3)

h/re

k = 2.260(h/re)-0.33

R² = 0.91

1

2

3

4

5

6

7

0.0 0.7 1.3 2.0 2.6 3.3

k (

J/m

m3)

h/re


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103

Figure 5.17: Specific energy size effect in machining titanium 6Al-4V alloy

5.8 Conclusions

This research investigated the variation of specific energy coefficient for a wide range of

un-deformed chip thicknesses and three different workpiece materials that are widely used

in engineering. The specific energy coefficient is a fundamental quantity required for the

estimation of tool tip energy and can have impact on surface integrity of machined parts.

The following conclusions were drawn as a result of this study:

1. A new generic model for the specific cutting energy coefficient based on the un-

deformed chip thickness for aluminium AW6082-T6 alloy, AISI 1045 steel alloy

and titanium 6Al-4V alloy has been presented in this work. The model is in

agreement with the theory for specific cutting force models. The specific cutting

energy can be modelled from the following generic relationship.

k = 2.782(h/re)-0.51

R² = 0.86

0

2

4

6

8

10

12

14

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

k (

J/m

m3)

h/re


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104

x

ee hKk


at the required un-deformed chip

thickness and Ke is the specific area energy in Jmm-2

at un-deformed chip thickness

of 1 mm, and h is the un-deformed chip thickness in mm while x is an

experimentally determined specific energy exponent.

2. The variation of specific energy with un-deformed chip thickness for three

workpiece materials follows similar power function trends. For the aluminium

AW6082-T6 alloy, specific energy coefficient varies from 13.08 to 0.21 Jmm-3

, and

for titanium 6Al-4V alloy the range is 10.66 to 1.13 Jmm-3

while the range for AISI

1045 steel alloy is 5.38 to 1.47 Jmm-3

.

3. For aluminium alloys the specific energy for cutting can be significantly high at

very low un-deformed chip thickness to significantly low at chip thicknesses

typical of conventional machining.

4. A representative average value of specific energy for different workpiece materials

is evaluated at a condition where the un-deformed chip thickness is equal to the tool

edge radius. Since this is the upper limit for the shear dominant mechanism. On this

basis the average specific energy in conventional machining for a positive 5 degree

rake angle carbide tool is 1.007, 2.260 and 2.782 Jmm-3

for aluminium AW6082-T6

alloy, AISI 1045 steel alloy and titanium alloy respectively.

5. This study shows that the specific energy is significantly influenced by federate in

milling. To reduce energy consumption during manufacturing it is recommended

that bulk material removal (roughing) should be undertaken at feedrates greater

than the tool edge radius.


processes

105

6. This study is based on specific energy evaluation without a focus on modelling

impact of tool wear; more research work is needed to investigate the sensitivity of

this model to tool wear.

7. Electrical energy demand in manufacturing is a significant contributor to the global

warming potential (GWP) and environmental burden of manufacturing industries.

Fundamental to evaluating the energy demand is a need for data on specific energy

requirements for machining different materials. Thus, the paper contributes key

data required for energy demand modelling and energy smart and environmentally

friendly manufacturing.

106

CHAPTER 6

IMPROVING THE INTEGRITY OF SPECIFIC CUTTING

ENERGY COEFFICIENTS FOR ENERGY DEMAND

MODELLING

6.1 Abstract

Energy modelling for mechanical machining processes is essential for energy labelling of

machined products and as a foundation for selecting optimum cutting conditions that meet

economic objectives while reducing energy demand and CO2 footprint. Electrical energy

demand in machining can be modelled in two parts, Basic Energy demand by the machine

tool and Tip Energy for actual material removal. A significant amount of research and

energy evaluation is based on the use of average specific energy values and ignores the

impact of machining conditions. In this comprehensive study, the evaluation of specific tip

energy is undertaken and the effect of chip thickness, tool wear, nose radius and cutting

environment is quantified. This work is an essential guide for the application of models to

estimate energy demand in practical machining processes. It is of significant importance to

improve accuracy in energy centric modelling of machining processes for sustainable

manufacture and resource efficiency.

Keyword: Energy demand; tool wear; cutting conditions

Chapter 6 Improving the integrity of specific cutting energy coefficients for energy demand

modelling

107

6.2 Introduction

The electrical energy demand for mechanical machining processes can be defined and

modelled as the basic and tip energy [31]. A third preparatory state called the ‘Ready state’

was also recently proposed [62]. The ‘Basic state’ is the energy demand of the machine

tool resource and auxiliary units at zero load. The ‘Ready state’ energy demand represents

the energy consumed for all transitional movement made by the machine axes up to the

point the tool is just about to cut. ‘Tip’ or ‘Cutting state’ energy is the specific energy

demand for the actual material removing operation. During a machining process, an

average three quarters of the total energy is consumed by the machine tool which

constitutes the basic and ready energy states [81-82]. There is thus a need for machine tool

designers to reduce the energy demand in the machine basic and ready state. Moreover, the

European Union (EU), Eco-design directive [10], Co-operative effort on process emissions

in manufacturing CO2PE! [24], ISO 14955 [19], ISO 14020:2000 [83] and ISO 5001:2011

[84] all promote energy demand reduction for machine tools. Design approaches include

the improvement of energy efficiency of machine tool auxiliary units and the use of more

energy efficient electric motors and drives.

From literature it has been reported that, energy demand in machining can be reduced by

optimizing cutting parameters, synchronizing spindle acceleration/deceleration with rapid

traverse [34], reducing non-productive modes [59], optimizing energy demand for coolant

pumps and centralizing coolant systems [85-86], use of low coefficient of friction tools

coatings [87] and selection of optimum cutting conditions [29, 34].

For manufacturers and machine shops, energy demand reduction can be achieved through

process planning by selection of energy efficient machine tools and cutting conditions. The

energy demanded for material removal i.e. the specific cutting energy is driven by material


modelling

108

machinability factors. It is this tip energy which is under the direct control of end users and

machinists because its magnitude is influenced by machining conditions.

Central to modelling, this tip energy is the evaluation of the specific energy for material

removal as defined in Equations 6.1 and 6.2.

tipreadybasictotal EEEE (6.1)

where Etotal is the direct total energy demand, Ebasic, Eready and Etip represent electrical

energy demand for basic, ready and cutting states respectively in (J) in a machining

process.

Equation 6.1 can further be expanded to explicitly model tip energy. The basic and ready

energy depend on the machine design and will be machine specific.

cuttingreadyreadybasicbasictotal kQttPtPE (6.2)

where tbasic, tready, tcutting are the time in seconds when the machine is in the basic, ready

state and actual cutting mode respectively. While Pbasic, Pready are the average power

demand in the basic and ready state respectively, k is the specific cutting energy coefficient

in J/mm3 and Q is the material removal rate in mm

3/s.

The specific energy coefficient represents the energy demand to remove 1 mm3 from a

workpiece material. To date there is no comprehensive study that examines how this key

energy modelling parameter varies with cutting conditions. The aim of this paper is to

address this knowledge gap and to raise the integrity of data used for specific energy

modelling and at the same time provide guidance for end users in energy demand

modelling of machined products.


modelling

109

6.2.1 Research aim and motivation

This research aim was to understand the factors that influence the tip energy in machining

processes. To achieve this, cutting tests were undertaken to assess the impact on specific

energy coefficients of cutting tool geometry (nose radius), cutting environment (dry and

flood coolant) and tool wear in machining AISI1045 steel alloy and EN8 steel alloy

materials. This information is essential because at present most data on energy demand

modelling is based on constant specific energy values derived in short run cutting tests.

Understanding how changes in conditions will influence the normalized tip or specific tip

energy will enable accurate analysis of direct electrical energy requirements in machining.

6.3 Research Strategy and Experimental Details

6.3.1 Research Strategy and Procedure

The research approach was to measure the current demand in machining processes and

hence evaluate the associated power and electrical energy demand. The electrical current

consumption was measured with a FLUKE 345 power clamp meter. Given that the total

energy in machining can be modelled as proposed in Equation 6.1, then the specific tip

energy can be obtained by plotting the power demand for different material removal rates.

The gradient for such a curve represents the specific cutting energy coefficient. This

specific energy coefficient was to be evaluated at different values of flank wear, tool nose

radius and cutting fluid in order to quantify and assess the effect of such variables on the

electrical energy demand in machining processes.


modelling

110

6.3.2 Experimental Details – Milling Tests

For the cutting tests, the machines used were the high speed milling Mikron HSM 400

machining centre that has an HVC140-SB-10-15/42-3FHSK-E40 spindle and Heidenhain

TNC 410 NC controller. For the milling tests, a tool holder E90X-D08-C10-06 with an

overhang of 25 mm was used. The holder had single insert.

To evaluate the variation of specific cutting energy with feed per tooth, cutting tests were

performed using SOMT-060204-HQ. The insert has a 0.4 mm nose radius, the edge radius

was evaluated to be 60 µm, the rake face primary chip breaker length was 60 µm and the

rake and clearance angles were 5o and 7

o respectively. This tool was used for end milling

operation. The insert was a general purpose TiAlN coated carbide insert with geometry as

described above.

Side milling tests were conducted under a dry cutting environment on AISI 1045 alloy

steel under the “One-Factor-at a Time” design of experiments. To evaluate the effect of

different nose radii, three insert types HM90 APKT 100304PDR IC328, HM90 APKT

100308PDR IC328 and HM90 APKT 100312PDR with 0.4 mm, 0.8 mm and 1.2 mm nose

radius respectively and edge radius of 70 µm and primary rake face land of 130 µm were

used.

The cutting variables were derived from insert manufacturers’ recommendations. The

cutting velocity was kept constant at 156 m/min in order to fix the spindle speed and avoid

large variations in basic power requirement. The depth of cut was 3.5 mm as defined by the

end milled plate thickness. The feed per tooth and radial width of cut were varied from

0.01 to 0.55 mm/tooth and 0.25 to 1.0 mm respectively.


modelling

111

6.3.3 Experimental Details – Turning Tests

Turning trials were conducted on the MHP lathe for continuous cutting in order to enable a

systematic evaluation of tool wear and electrical energy demand for each wear land value.

The MHP Lathe had an 18 kW rated DC Servo motor spindle. A tool holder

PCLNL2020K12 and insert CNMG 120408-WF 4215 were used. The insert was coated

with TiCN + Al2O3 + TiN. The turning tests for tool wear evaluation were conducted

under a flood cutting environment on EN8 steel alloy.

The turning tests involved establishing a pre-defined wear land value through cutting for

extended times. Each test was repeated to generate a sufficient number of tools with

particular wear land values. Using these tools and at a particular wear land, more

experiments were conducted to evaluate specific energy coefficient values in relation to the

wear previously generated. A CNMG 120408 insert was used in the turning tests to

evaluate effect of tool wear and cutting fluid. This was used to machine EN8 steel at 0.3

mm/rev, for a range of depth of cut varying from 0.25 mm to 1.0 mm (to generate different

materials removal rate) and a cutting velocity of 415 m/min. The cutting variables were

selected to be within the process window as recommended by Sandvik Coromant the tool

manufacturer.


6.4.1 The effect of chip thickness on specific cutting energy

During the end milling tests, the radial width of cut was varied from 0.25 mm to 1.0 mm to

generate different values for the material removal rate. The feed (chip load) was also

varied from 0.01 mm/tooth to 0.55 mm/tooth and the total power demand measured for

each feed was plotted against the material removal rate generated by varying the radial


modelling

112

with of cut. The slope of the curves obtained represents the specific energy coefficient of

milling AISI 1045 steel alloy as shown in Figure 6.1.

Figure 6.1: Determination of specific energy coefficient.

The average un-deformed chip thickness is estimated using Equation 6.3.

s

df

hs

zavg

0

sin (6.3)

where havg represents the average un-deformed chip thickness in mm, fz is the feed per tooth

in mm/tooth and Ø is the swept angle in degrees and Øs is the swept angle in radians.

In order to understand the effect of feed on the tool tip energy, the specific energy was

plotted against the feed (chip load) as shown in Figure 6.2. It can be observed from Figure

6.2 that the specific energy decreases by a power function as shown in Equation 6.4. In

P = 3.73Q + 3013

R² = 0.94

3040

3067

3094

3121

3148

9 16 23 30 37

Po

wer

(W

)

Q (mm3/s)

fz = 0.100 mm/tooth

ap = 3.5 mm

Vc = 156 m/min


modelling

113

increasing the feed from 0.01 mm/tooth to 0.55 mm/tooth, the specific energy decreases

from 5.38 J/mm3 to 1.47 J/mm

3 for AISI 1045 steel alloy.

Figure 6.2: Specific cutting energy variation with feed per tooth in milling AISI 1045 steel

alloy

34.025.1 zfk (6.4)

where k is the specific cutting energy in J/mm3 and fz is the feed per tooth in mm/tooth

Figure 6.3 shows the relationship of the specific cutting energy coefficient and the un-

deformed chip thickness.

Figure 6.3 show that as the un-deformed chip thickness increases, the energy efficiency of

the cutting process tends to improve i.e. the specific cutting energy decreases. This can be

related to the changing of the process mechanisms towards a shearing dominated process

as a result of the effective rake angle reducing from being highly negative to a more

k = 1.25fz - 0.34

R² = 0.92

0.00

2.00

4.00

6.00

0.00 0.10 0.20 0.30 0.40 0.50 0.60

k

(J/m

m3)

fz (mm/tooth)


modelling

114

positive angle. This decreasing trend implies that as the process mechanisms shifted from

ploughing and rubbing dominated zone to shearing dominated zones, the specific energy

decreases by 73% (considering the lowest un-deformed thickness of 3 µm as shown in

Figure 6.3).

Figure 6.3: Specific cutting energy variation with un-deformed chip thickness in milling

AISI 1045 steel alloy

Thus, based on tip energy, the energy intensity of the actual material removal process

depends on the process mechanism at play. These process mechanisms are driven by the

ratio between the un-deformed chip thickness and the tool edge radius [88].

The values of the specific energy gradually decrease as un-deformed chip thickness

increases up to a point whereby it is greater than the cutting edge radius. At this value, the

process mechanism would have shifted to dominant shearing. This zone tends to be the

k = 0.90h-0.33

R² = 0.91

0

2

4

6

0.00 0.05 0.10 0.15 0.20

spec

ific

ti

p e

ner

gy

, k

(J

/mm

3)

un-deformed chip thickness, h (mm)


modelling

115

value for the specific energy at which roughing operations are conducted. This represents

an energy efficient tip energy material removal zone. Inappropriate process parameters

selection, especially when the ratio of un- deformed chip thickness to the cutting edge

radius is less than unity can cause an increase in the specific energy demand during

machining operations. It is important to select chip thickness that ensures dominant shear

mechanism if the energy intensity of roughing operations is to be reduced.

6.4.2 The effect of nose radius on specific cutting energy

In this section, 3 insert types HM90 APKT 100304PDRIC328, HM90 APKT100308PDR

IC328 and HM90 APKT 100312PDR with nose radius of 0.4, 0.8 and 1.2 mm respectively

were evaluated in milling tests. Other geometry and coatings of the 3 inserts were the same

as stated previously. The milling test was similar to that as previously described. The

current consumption was measured and the power demand calculated and analyzed to

determine the specific energy coefficient as described before.

Figure 6.4 shows the variations for the specific energy coefficients when different cutting

tool nose radius was engaged at a range of un-deformed chip thickness.


modelling

116

Figure 6.4: Specific energy comparison for 0.4 mm, 0.8 mm and 1.2 mm nose radius tools

in milling of AISI 1045

It can be observed that at an average un-deformed chip thickness of 3 µm, the specific

energy coefficient was 6.33, 4.95 and 3.94 J/mm3 for 1.2, 0.4 and 0.8 mm nose radius

respectively. This also shows that for energy efficiency, a mid-range tool nose radius of 0.8

mm should be preferred when milling AISI 1045 at a feed per tooth fz of 0.01 mm/tooth. At

this feed per tooth 0.01 mm/tooth, and a step-over of 1 mm for 8 mm diameter tool the

maximum un-deformed chip thickness is 6.6 µm which is 11% of the 60 µm cutting edge

radius. As the un-deformed chip thickness increases to a value closer to the cutting edge

radius of the tool, in this case 0.07 mm, the specific energy requirements for different nose

radius becomes comparable in magnitude. The data shows that, broadly, for conventional

machining in shear dominated cutting zone, the decision to use a 0.4 mm, 0.8 mm or 1.2

mm nose radius insert does not significantly affect the specific tip energy.

0

2

4

6

8

0.00 0.08 0.16 0.24 0.32 0.40

Sp

ecif

ic c

utt

ing

en

erg

y k

(J

/mm

3)

feed per tooth, fz (mm/tooth)

k_0.4 k_0.8 k_1.2


modelling

117

6.4.3 The effect of cutting environment on specific cutting energy

Dry and flood cutting environment was tested and analyzed and the result as displayed in

Figure 6.5.

Figure 6.5: Specific energy demand for dry and flood cutting environment of AISI 1045

steel alloy

In the region of feed per tooth between 0.01 and 0.2 mm/tooth the specific energy in

machining under flood coolant is higher than for dry machining. At 0.2 mm/tooth, the

average un-deformed chip thickness was 66 µm and greater than the 60 µm edge radius.

This means the higher specific energy for flood compared to dry is experienced when feed

per tooth is lower than the tool edge radius and in this zone ploughing and rubbing

dominates process mechanisms and have the effect of hindering the penetration of cutting

fluid to the cutting zone due to the highly negative rake angle. Additionally, the fluid

R² = 0.97

R² = 0.97

0

2

4

6

8

10

0.00 0.10 0.20 0.30 0.40 0.50 0.60

Sp

ecif

ic c

utt

ing

en

erg

y (

J/m

m3)

feed per tooth, fz (mm/tooth)

k_flood k_dry


modelling

118

pressure can generate an additional load that could increase specific energy requirements.

When machining at federates higher than the tool edge radius the access of the cutting fluid

to the cutting zone reduces the coefficient of friction enabling lower specific energy. It has

been suggested by Childs, [89] in 2006, that the effectiveness of cutting fluid is cutting

velocity and un-deformed chip thickness dependent.

Figure 6.5 shows that the impact of cutting fluid as a lubricant was not effective at lower

un-deformed chip thickness. This is due to the fact that fluid accessibility to the contact

zone becomes extremely difficult at such lower un-deformed chip thickness of less than

0.02 mm (i.e. 0.10 mm feed per tooth). The coolant pump flow rate was 30 L/min at an

operative pressure of 300 kN/m2. The pressure of the cutting fluid increases the energy

required for the machining processes.

Flood cooling increases ploughing effect at nano un-deformed chip thickness since flood

cooling tends to avoid chips build up at the tool-chip cutting interface because during

ploughing effect chips are built up until the size is up to the minimum chip thickness and

could therefore be removed as chip. Hence, higher specific energy was observed at lower

un-deformed chip thickness.

It can also be noticed from Figure 6.5 that as the un-deformed chip thickness is increased

further, the average specific energy coefficient for dry is 2.26 J/mm3 while that for flood

milling is 1.26 J/mm3. This implies that the tip energy can be reduced by approximately

28% with flood cutting when compared to dry cutting under the same process parameters.

Assuming tip energy to be 25% of total direct electrical energy demand then 7% energy

reduction due to effective use of cutting fluid is possible.


modelling

119

From Figure 6.5 and at a feed of 0.2 mm/tooth, the specific energy of flood cutting is equal

to that of dry cutting. This suggests that cutting conditions should be set above 0.2

mm/tooth for the effectiveness of cutting fluid lubricating effect to be realized.

6.4.4 The effects of tool wear on specific cutting energy

For the tool wear evaluation, a turning operation was conducted on the MHP lathe and on

EN8 workpiece material. The cutting tool and process parameters were as stated in Section

6.3.3 above. After the turning operations, each cutting tool edge was examined under the

optical microscope for tool wear measurement. Figures 6.6a and 6.6b are some samples of

the cutter showing the flank wear as imaged by the optical microscope. It is observed as

expected, that flank wear grows with cutting time.


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120

(a) Flank wear at k = 2.27 J/mm3 after 4.16 minutes

(b) Flank wear at k = 4.50 J/mm3 after 8.06 minutes

Figure 6.6: Optical microscope view of flank wear land

From Figure 6.7, it is deduced that the specific energy coefficient is directly proportional to

flank wear and as the flank wear increases from 0.055 mm to 0.135 mm, the corresponding


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121

specific energy also increased from 2.14 to 4.50 J/mm3. This indicates that, as cutting

progresses, tool wear can increase the energy coefficient by an average of 52% when

turning EN8 steel alloy. This is the case when the tool life criterion was set at 0.15 mm VB

in order not to compromise component accuracy due to change in actual cutting variables

as the tool edge recesses.

Figure 6.7: Specific energy coefficient increases with flank wear during turning operation

of EN8 steel alloy

Also Figure 6.8 showed that as the cutting time increases, specific energy coefficient also

increases. This result is based on the wear land established as indicated in Figure 6.6a and

Figure 6.6b for EN8 alloy steel. The observed linear trend makes it easier to account for

the effect of tool wear on energy demand.

2

3

4

5

0.05 0.07 0.09 0.11 0.13 0.15

Sp

ecif

ic c

utt

ing

en

erg

y k

(J

/mm

3)

Flank Wear Vb (mm)


modelling

122

Figure 6.8: Effect of cutting time on k during a turning operation of EN8 steel alloy

6.5 Conclusions

This work was focused on improving the integrity of specific cutting energy coefficients

for mechanical machining processes. The study has provided data on how the specific

cutting energy varies with un-deformed chip thickness, tool wear, cutting tool nose radius,

dry and flood coolant. The following conclusions were drawn from the study:

Increasing the feed from 0.01 mm/tooth to 0.55 mm/tooth, the specific energy

decreases from 5.34 J/mm3 to 1.47 J/mm

3 for AISI 1045 steel alloy. This is a 72%

reduction in tip energy, which is about 18% reduction in total direct energy demand

assuming that tip energy is on average 25% of total direct energy demand. The

change from low feed per tooth to high feed per tooth shifts the dominant process

mechanism from rubbing/ploughing to shearing dominated zones. Therefore, to

k = 0.408t + 1.0278

R² = 0.93

2.0

2.5

3.0

3.5

4.0

4.5

2.0 3.5 5.0 6.5 8.0 9.5

Sp

ecif

ic c

utt

ing

en

erg

y k

(J

/mm

3)

Cutting time (mins)


modelling

123

reduce the energy intensity of machining processes, roughing or bulk material

removal should be undertaken at un-deformed chip thickness greater than the tool

edge radius.

Tool wear can increase the specific energy coefficient by an average of 52% when

turning EN8 steel alloy. Assuming that tip energy is 25% of the total energy

demand then tool wear can increase total energy by an average of 13%.

The flood cutting environment decreases the specific energy coefficient by an

average 28% when compared to dry cutting in normal shear mode cutting

conditions. This is estimated to be a 7% reduction in total energy demand in

machining when assuming that tip energy is on average 25% of the total energy

demand.

For nose radius of 1.2, 0.8, and 0.4 mm the specific energy does not significantly

change in shear dominated cutting mechanism zones. However, at very low un-

deformed chip thickness, the 0.8 mm nose radius tool appears to give the best and

lowest specific energy values.

This study has provided a benchmark that total energy demand in mechanical

machining processes can be reduced or increased by 18%, 13%, or 7% by selection

of un-deformed chip thickness, by tool wear and use of cutting fluids respectively.

These factors need to be taken into account if the margin of energy demand

improvement is comparable. There is a strong case for selection of optimum

feedrates and hence chip thickness in order to reduce the energy intensity of

machining processes. Choosing between a tool nose radius of 0.4 mm, 0.8 mm or

1.2 mm does not significantly alter the specific energy demand in shear dominated

cutting zones. These choices are within the grasp of machine shops and end users


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124

since they may not significantly influence the basic power demand unless by choice

of machines.

125

CHAPTER 7

SPECIFIC ENERGY BASED EVALUATION OF

MACHINING EFFICIENCY

7.1 Abstract

Rubbing and ploughing increases the tool tip energy demand in machining. An efficient set

of cutting conditions would direct the energy into material shearing and hence value

adding activity. In this work, specific ploughing energy was investigated on AISI 1045

steel alloy, titanium 6Al-4V alloy and aluminium AW6082-T6 alloy materials. The

relationship between shear angle and specific ploughing energy is explored. An optimised

model for width of cut and un-deformed chip thickness at which ploughing effect would be

minimal is proposed. The proposed methodology can be used to derive the specific

ploughing energy for minimum tip energy demand. It also contributes towards the design

of machines.

Keywords: Specific ploughing energy, swept angle, cutter engagement, un-deformed chip

thickness, specific cutting energy coefficient.

Chapter 7 Specific energy based evaluation of machining efficiency

126

7.2 Introduction

7.2.1 The Total Specific Energy and Chip Morphology

The electrical energy input into machining processes can cater for material shearing,

ploughing, friction, new surface generation, chip momentum change [12] and machine

tool energy losses and process upkeep. The modelling of specific energy in machining

relates to the tip energy [44, 46, 60, 62] i.e. the energy required for actual material

removal. Since the surface energy and the momentum energy represents a very small and

negligible amount compared to the specific frictional and specific shear energies, and also

because they do not contribute to chip removal processes, they are incorporated into the

specific ploughing energy [48]. Therefore, the total specific cutting energy Ke in Jmm-3

can

be categorised as in Equation 7.1.

spfe kkkK (7.1)

where Ke is the total specific cutting energy, kf represents the specific friction energy in

Jmm-3

; kp is the specific ploughing energy in Jmm-3

and ks is the specific shearing energy in

Jmm-3

.

The estimated value of the specific cutting energy varies for different machining processes

even when the workpiece material properties remain the same. For example, the specific

cutting energy of grinding operations is higher compared to other machining processes like

turning and milling [49]. This is due to the inefficient nature of the abrasive grit in grinding

compared to the use of defined cutting edges as in other mechanical machining processes.

The knowledge of specific energy can be important [49] because for example, the specific

cutting energy in grinding operations influences surface integrity of machined components

[67] and is one of the characteristics of ploughing effect.


127

Ploughing effect can be explained as the action of the cutting tool pushing the material

(especially at un-deformed chip thickness lower than the cutting edge radius) upwards

and/or side ways to form a ridge-like structure and burrs on top, at the entry, exit or side of

the machined surfaces depending on the type of machining operations.

The consumed electrical energy demand for ploughing is undesirable. This is because

often, no desired work is done and this is a waste of energy (with the exception of grinding

operations) since; the ploughed materials remain attached to the workpiece material after

the tool pass. This effect is indicated by the surface integrity [72, 90]. Ploughing effect has

also been identified to affect the geometrical accuracy of machined products [72, 91-92].

Therefore, it is desirable to reduce or if possible eliminate the specific ploughing energy in

mechanical machining processes. Ploughing is encouraged by a lower ratio of un-deformed

chip thickness to the cutting edge radius. This scenario is known as ‘size effect’ in

mechanical machining processes.

Lucca et al. [47] used ploughing and elastic spring back effect to explain the increase in

specific cutting energy. In a previous work, Lucca et al. [93] used the relation between the

un-deformed chip thickness and the cutting edge radius to explain the transition from

shearing dominated machining process to ploughing dominated process. The authors

further reported that in ploughing dominated machining processes, the force per unit width

in the thrust direction was found to increase more rapidly than the force per unit width in

the cutting direction. This implies that the tool edge condition has a significant effect on

the thrust forces when the depth of cut was below the tool edge radius. In this case, rubbing

phenomenon is predominant and this resulted in higher friction forces at the tool-chip

interface [94]. Increase in cutting forces means a corresponding increase in specific cutting

energy.


128

In studies on brass materials, Taminiau and Dautzenberg [95] reported an increased

specific cutting energy when machining at an un-deformed chip thickness less than the

cutting edge radius. The average specific cutting energy was almost constant when the

ratio of the un-deformed chip thickness to the cutting edge radius is equal or more than

unity.

Singh et al. [96] in their analysis and study of specific ploughing energy for mild steel and

composite ceramics during a grinding operation deduced an equation for the specific

ploughing energy using single grit scratch test [97]. They reported that the specific

ploughing energy was a significant component of total specific grinding energy which is

responsible for around 40% to 80% of the specific grinding energy. This was found to

dominate at low depth of cut especially with materials of hard and high strength such as

conductive ceramic.

7.2.2 Process mechanisms in mechanical machining operations

Chip formation is a good indication of material characteristics and the machinability of

workpiece materials. It has been shown that chip formation not only depends on material

characteristic and cutting tool geometry, but also on the ratio of feed per tooth to cutting

edge radius. Chae et al. [98] show that this ratio is between 5% and 35% of the tool edge

radius. At a value below the minimum chip thickness, no chip will be formed and the

process will be dominated by rubbing and ploughing. This is an indication of high

frictional force at the tool-chip contact interface and plastic deformation of the cutting tool

as a result of high temperature [74, 99].

Researchers explained that the machining process mechanisms were dominated by

rubbing, ploughing and shearing. For example, Chae et al. [98], Ducobu et al. [74] and

Aramcharoen et al. [72] use the relationship between un-deformed chip thickness and


129

cutting tool edge radius in orthogonal cutting process to define the three established

mechanisms during a mechanical machining process. These are as depicted in Figure 7.1.

Figure 7.1: Effect of un-deformed chip thickness ratio to the cutting edge radius in

orthogonal cutting adapted from [72]

Figure 7.1 shows the relationship between cutting edge radius and the un-deformed chip

thickness during a machining process. The first scenario occurrs when the ratio h/re is less

than the minimum chip thickness. In this case, the cutter will deform elastically and the

workpiece material will be compressed by the cutting tool. Material spring back effect is

dominant where workpiece material is forced under the cutting tool and then recovers back

after the tool passes as shown in Figure 7.1a. The cutting mechanism at this zone is

dominated by rubbing and ploughing effect [73] and as a result of this phenomenon,

cutting and frictional forces increases rapidly [74], rake angle will also increase as a result

of materials gathering around the cutting tool edge radius which will increase the chip

thickness [92]. This will eventually cause an increase of specific cutting energy.

In the second scenario (Figure 7.1b) where the ratio h/re is approximately equal to 1, the

process mechanism consist effect of ploughing and shearing. The process mechanism tends


130

to move from a rubbing and ploughing dominated area to a shearing dominated zone.

However still, the effect of ploughing exist at this zone and shearing effect tends to be

more dominant [74]. Although a chip is formed, the workpiece material undergoes an

elastic deformation and recovery at the desired depth of cut after the tool pass. Thus, the

removed material is less than the desired value giving rise to a poor dimensional accuracy

and surface integrity.

Figure 7.1c show the third scenario. In this zone, the ratio h/re is greater than 1. The elastic

deformation of the workpiece decreases rapidly and an improved chip is formed. In this

zone, the process mechanism tends to be value adding and sustainable machining. A lower

specific energy demand is an expected characteristic in this zone and an indication of the

efficiency of the process.

Other force components, for example the ploughing force components, are neglected either

because they cannot be measured or they do not contribute to chip formation processes and

considered too small. The ploughing force, difficult to be isolated from the measured force

data [100] is notably significant in tool wear assessment and monitoring, material flow

stress calculation, chip formation mechanisms, and machined surface integrity. The impact

of the process mechanisms ultimately affects the tool tip energy demand of the process.

The process mechanisms can also be used to define the efficiency of the machining

operations. In the case where rubbing and ploughing are said to be dominant, the process is

within the ‘Waste’ dominated zone since no chips are removed from the workpiece. In the

other hand, if the process is dominated by shearing effect, then it can be said to be a ‘Value

adding’ operations. Therefore, for a process to be energy centric, efficient and sustainable,

it should be within the value adding zone. A zone whereby rubbing and ploughing effect

are reduced and/or eliminated and shearing effect encouraged.


131

7.3 Research aim and Objective

The aim of this work was to investigate the process mechanism during milling operations.

This was to enable the identification of process parameters and evaluate process efficiency

at which the mechanisms of rubbing, ploughing and shearing effect are dominant. The

study also aimed to propose a methodology to estimate the specific ploughing energy and

optimise cutter engagement. The result will enable process and system designers to

optimise electrical energy usage for resource efficiency and sustainable manufacture of

products.

7.4 Experimental strategy and set up

7.4.1 Swept angle optimisation and their influence on specific ploughing in milling

processes

In order that the optimised radial depth of cut is engaged, a pilot test was carried out on

AISI 1045 steel alloy. A general purpose TiAlN coated carbide single insert SOMT-

060204-HQ with geometry shown in Table 7.1 was used for the side milling test. The

milling test was conducted on a high speed Mikron HSM 400 machining centre under a dry

cutting environment. This study was to investigate the correlation between the radial depth

of cut and ploughing effect during a milling operation.

For this experiment, the radial depth of cut ae, feed rate, and depth of cut were varied as

shown in Table 7.2. The side milling test was conducted in such a way that each test

engaged a different swept angle. The swept angles and the un-deformed chip thicknesses

were estimated with Equations 7.2 and 7.3 [65] respectively considering the radial depth

of cut for each set of milling test.


132

(7.2)

where Ø is the swept angle in degrees, r is the cutter radius in mm and ae is the cutter

engagement or step over in mm.

(7.3)

where hm(avg) is the average un-deformed chip thickness in mm, fz is the feed in mm/tooth,

φs is the swept angle in radians.

Table 7.1: Cutting tool geometry

Geometry Values

Nose radius (mm) 0.4

Edge radius (µm) 60

Positive Rake angle

(degrees)

5

Rake face primary chip

breaker land (µm)

60

Clearance angle (degrees) 7


133

Table 7.2: Cutting parameters for AISI 1045 steel alloy

Feedrates

(mm/min) 62 621 1179 2855 3413

Feed fz

(mm/tooth) 0.01 0.10 0.19 0.46 0.55

Radial depth

of cut ae (mm) 0.20 0.40 0.60 0.80 1.00

Figures 7.2, 7.3, 7.4, 7.5 and 7.6 shows the relationship between power demand and

material removal rate during side milling operations of AISI 1045 steel alloy.

Figure 7.2: Power –Material removal rate graph at 18.20 Swept angle

P = 7.80Q + 2990.5

R² = 0.92

2900

3000

3100

3200

3300

3400

0 5 10 15 20 25 30 35 40

Po

wer

(W

)

Material removal rate (Q) (mm3/s)

vc = 156 m/min

fz = 0.01 - 0.55 mm/tooth

ap = 3.5 mm


134



P = 3.73Q + 3009

R² = 0.85

2900

3025

3150

3275

3400

0 20 40 60 80

Po

wer

(W

)


vc = 156 m/min

fz = 0.01 - 0.55 mm/tooth

ap = 3.5 mm

P = 2.78Q + 3017.1

R² = 0.88

2850

2950

3050

3150

3250

3350

0 20 40 60 80 100 120

Po

wer

(W

)


vc = 156 m/min

fz = 0.01 - 0.55 mm/tooth

ap = 3.5 mm


135



P = 2.11Q + 3024

R² = 0.88

2900

3000

3100

3200

3300

3400

0 50 100 150 200

Po

wer

(W

)


vc = 156 m/min

fz = 0.01 - 0.55 mm/tooth

ap = 3.5 mm

P = 1.56Q + 3070.7

R² = 0.80

2900

3000

3100

3200

3300

3400

0 50 100 150 200

Po

wer

(W

)


vc = 156 m/min

fz = 0.01 - 0.55 mm/tooth

ap = 3.5 mm


136

The specific energy coefficient is represented by the slope of the Power- Material removal

rate trend line and tabulated in Table 7.3. It was observed that as the swept angle increases,

the specific energy reduces varying from 7.80 to 1.56 Jmm-3

. This is due to the fact that the

milling test gradually moved from smaller to higher chip thickness and from a ploughing

dominated area to a shearing dominated one. Therefore, it can be deduced that to reduce

ploughing effect in milling processes, a higher swept angle must be engaged.

Table 7.3: Specific energy coefficient data for AISI 1045 steel alloy obtained from tests

Swept angle

fz (mm/tooth) hm average (mm) Degrees (o) Radians

(rad)

k (Jmm-3)

0.010 0.002 18.2 0.318 7.80

0.100 0.022 25.8 0.450 3.73

0.190 0.051 31.8 0.555 2.78

0.460 0.143 36.9 0.644 2.11

0.550 0.190 41.4 0.723 1.56

Analyzing further, the relationship between specific energy coefficients was plotted against

the swept angles. The result shows a non-linear relationship. It can be seen from Figure 7.7

that at lower swept angles 0.318 rad (ae of 0.2 mm), the specific energy was 7.80 Jmm-3

while the values decreases to a value of 1.56 Jmm-3

for swept angle 0.723 rad. The lower

range of specific energy values are relatively comparable to published values in literature

[12].


137

Figure 7.7: Optimum swept angle

Following on from Figure 7.7, a quadratic trend curve fitted well with R2 of 0.98. The

regression equation derived from Figure 7.7 is stated in Equation 7.4.

Differentiating the regression equation:

(7.4)

Differentiating Equation 7.4 with respect to the swept angle;

(7.4b)

(7.4c)

It can be seen that the optimum swept angle for minimizing the specific energy for the

insert nose radius of 0.4 mm used is 39.64o (i.e. 0.692 rad). At this angle, the shearing

effect will be dominant. The specific cutting energy is therefore at an optimized value

k = 42.25Q2 - 58.46Q + 21.96

R² = 0.98

1.50

2.50

3.50

4.50

5.50

6.50

7.50

8.50

0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75

Sp

ecif

ic C

utt

ing

En

erg

y k

(J

/mm

3)

Swept Angle (Radians)


138

when compared with a lower swept angle of 18.2o (i.e 0.318 rad). Hence, the step over

which equates to the optimum specific cutting energy can be estimated.

From Figure 7.8, the average un-deformed chip thickness is defined as in Equation 7.3 and

the swept angle deduced based on Equation 7.2.

Figure 7.8: Cutter engagement with workpiece.

From Equations 7.2 and 7.4c, the cutter engagement can be estimated thus:

(7.5)

From this case study, the cutter diameter was 8 mm, and the experimental optimum swept

angle was 39.64o, it therefore follows from Equation 7.5 that the optimum cutter


139

engagement should be 0.92 mm offset. Also, from Figure 7.8 and Equation 7.3, the un-

deformed chip thickness can be estimated as stated in Equation 7.6.

(7.6)

= Optimised maximum un-deformed chip thickness (7.6b)

where hmax is the maximum un-deformed chip thickness in mm, fz is the chip load in

mm/tooth and ae is the step over or the radial depth of cut in mm.

Hence, for optimized values of the specific cutting energy that would improve process

efficiency, the step over ae should not be less than 0.23*r and the maximum un-deformed

chip thickness should not be less than 0.64*fz. This equation agrees with Campatelli et al.

[101], when they reported that the optimal value of the radial engagement (i.e. radial width

of cut) to minimize the specific energy that is related only to the efficiency of cutting is

achieved by maintaining the value suggested by the tool manufacturer, about 1 mm and at

that value, the feed per tooth shows an optimal design value for a 0.12 mm/tooth. For

example, in comparing Campatelli et al. [101] results and from Equation 7.6b, (assuming

feed/tooth is 0.12 mm/tooth), it therefore implies that the un-deformed chip thickness is

equal to 0.077 mm. This also implies that the ratio of the un-deformed chip thickness to

cutting edge radius is equivalent to 1.28 (cutting edge radius of insert used is 0.06 mm from

Table 7.1). This value also correlates to the point at which the effect of shearing dominates

the rubbing and ploughing effect. The specific energy at this point is expected to be

comparably lower when compared to the rubbing-ploughing dominated machining.

In order to estimate the machining process efficiency that would distinguish the value

adding and waste processes, an understanding of the process mechanism is necessary. This

will enable the process to be adequately categorized and evaluated. Therefore, the milling


140

experiment was designed with the understanding that the specific energy is increased when

step over << 0.23r and h <<0.64fz. This range of values would adequately account for the

specific energy controlled by the process mechanisms i.e. rubbing, ploughing and shearing.

This range of values also define value adding and waste criterion of the process.

Since the impact of the ploughing effect is increased at values of ae and fz less than the

proposed values, the ranges of values for the step over were set to overlap the proposed

values. Hence, the step over were set at 0.063r, 0.125r, 0.188r and 0.25r. This values

equates to swept angles of 20.36o, 28.96

o, 35.66

o and 41.41

o. The value of fz was set at

0.01, 0.10, 0.19, 0.28, 0.37, 0.46 and 0.55 mm/tooth. These values equates to un-deformed

chip thickness h of 0.003, 0.035, 0.066, 0.097, 0.128, 0.159 and 0.190 mm respectively as

shown in Table 7.4. These ranges would allow the milling to be carried out within the

ploughing and shearing domain so that a clearer picture of the ploughing effect could be

observed and properly represented on the specific energy variation curve.

7.4.2 Estimation of the specific ploughing energy

With knowledge of the optimized cutter engagement and un-deformed chip thickness

values, a side milling test was conducted on aluminium AW6082-T6 alloy, AISI 1045 steel

alloy and titanium 6Al-4V alloy under Mikron HSM 400 machining centre with a spindle

HVC140-SB-10-15/42-3FHSK-E40 and Heidenhain TNC 410 NC controller. A general

purpose multi-layered TiAlN coated carbide single insert SOMT-060204-HQ with

geometry as in Table 7.1 was mounted on a tool holder E90X-D08-C10-06 with an

overhang of 25mm. The cutting parameters and chemical composition of the workpiece

materials were as stated in Table 7.4. The machining trials were conducted under a dry

cutting environment. Each workpiece materials of size 50mm X 100mm X 3.5mm was

clamped supported by parallel slips on a milling vice. The material overhang was limited


141

to 12 mm just enough to accommodate a set of machining trial and to eliminate the effect

of vibrations of the workpiece materials during the machining process. Each milling trial

was repeated three times. A new cutting tool edge was introduced for a new set of trials in

order that wear characteristics was not introduced into the electrical current measured. The

electrical current consumption was measured with a FLUKE 345 Power Clamp meter. The

side milling tests were conducted based on the procedure previously published by Balogun

and Mativenga [88].

Table 7.4: Workpiece materials and cutting parameters for milling trials

Aluminium Alloy

AW6082-T6

AISI 1045 Titanium alloy

6Al-4V

Feed (mm/tooth) 0.01 – 0.55 0.01 – 0.55 0.01 – 0.55

Depth of cut (mm) 3.5 3.5 3.5

Cutting velocity (m/min) 210 156 80

Radial depth of cut (mm) 0.25 – 1.00 0.25 – 1.00 0.25 – 1.00

Tool diameter (mm) 8 8 8

Chemical composition

(Max)

1%Mn, 0.5%Fe,

1.2%Mg, 1.3%Si,

0.1%Cu, 0.2%Zn,

0.1%Ti, 0.25%Cr,

Balance Al.

0.46%C,

0.40%Si,

0.65%Mn,

0.40%Cr, 0.10

Mo, 0.40%Ni,

0.63% Others

89.37%Ti,

6%Al, 4%V,

0.08%C,

0.3%Fe,

0.2%O2,

0.05%N

Material Hardness HV 100 HV 146.4 HV 329


142

In order for that the specific ploughing energy be properly accounted for, an analysis based

on the work of Balogun and Mativenga [88] was adopted and the specific energy

coefficient values derived based on the optimised swept angle and un-deformed chip

thickness analysis previously reported in this paper. The specific energy coefficients

obtained on three different wokpiece material was as shown in Table 7.5. It can be seen

that at a lower feed for example 0.010 mm/tooth, the specific energy coefficients were

13.08, 10.66 and 5.31 Jmm-3

for aluminium AW6082-T6 alloy, titanium 6Al-4V alloy and

AISI 1045 steel alloy respectively.

The higher values of specific energy at such un-deformed chip thickness is a result of the

contribution of ploughing effect at which rubbing, higher frictional effect and plastic

deformation of the cutting tool dominate. From Figure 7.9, it is shown that as the un-

deformed chip thickness increases, the ploughing effect tends to decrease and eventually

eliminated as the feed increases. This phenomenon is not the same for all machining

processes. For example, in micro-machining where components are miniaturized,

ploughing effect cannot be avoided since the ratio of un-deformed chip thickness to the

cutting edge radius is always less than unity. However, with adequate knowledge of the

range of specific energies, an optimised value can be estimated to avoid catastrophic wear

and/or damage of the cutting tools. This will cause an improvement on the surface

integrity of the machined component and also reduce the values of specific ploughing

energy.


143

Figure 7.9: Impact of size effect on Specific cutting energy for dry cutting AISI 1045 steel

alloy

Table 7.5: Experimental specific energy coefficient values

Materials

Cutting variables AISI 1045 Aluminium alloy Titanium alloy

fz (mm/tooth) vc (m/min) ap (mm) Specific energy coefficient (Jmm-3

)

0.01

156 3.5

5.31 13.08 10.66

0.10 3.73 1.99 4.45

0.19 2.08 1.52 3.28

0.28 1.97 0.78 2.55

0.37 1.65 0.87 2.65

0.46 1.55 0.21 1.14

0.55 1.47 0.21 1.13

Value

adding

Process

waste


144

7.4.3 Proposed analysis of the Specific Ploughing Energy

The specific energy coefficients obtained for AISI 1045 steel alloy, aluminium AW6082-

T6 alloy, and titanium 6Al-4V alloy respectively were plotted against the ratio of the un-

deformed chip thickness to the cutting edge radius for the three materials under

investigation. Figures 7.10, 7.11 and 7.12 show the contribution of ploughing effect on the

specific energy curves.

From Figure 7.9 two distinctive regions can be observed. The first region is where the un-

deformed chip thickness is less than the cutting edge radius. At this region, it is observed

that higher values of specific energy resulted. This is due to the influence of ploughing

effect. At this point also, the spring back effects are pronounced and diminutive or no chips

are formed [72, 74, 102-104]. The boundary is a point where the ratio of un-deformed chip

thickness and the cutting edge equals unity and the second region is a near constant trend

zone of specific energy values where the un-deformed chip thickness is greater than the

cutting edge radius.

In micro, nano and peso machining, the cutting plane usually falls within the first region

where ploughing is dominant thereby an increase in the tool tip energy will be observed.

Whereas, in the case of macro-machining, the cutting plane lies within the third region

where shearing is dominant and the values of the specific energy recorded agrees with that

available from literature [12].

Figures 7.10, 7.11 and 7.12 show that at higher value of un-deformed chip thickness, for

example above 66 µm for AISI 1045 steel alloy, the relationship between specific energy

and h/re is more or less linear. The tool edge radius was 60 µm. Therefore fitting a straight

trend line AB to the experimental data curve for h > 66 µm and extrapolating to h/re = 0,

gives a value which can be interpreted as the point of maximum shear. Therefore the


145

intercepted point A gives a value equivalent to the maximum specific shear energy of 2.25,

2.42 and 4.91 Jmm-3

, for aluminium AW6082-T6 alloy, AISI 1045 steel alloy and titanium

6Al-4V alloy respectively as depicted in Figures 7.10, 7.11 and 7.12. Machining processes

conducted within these range of values are called the “Value-adding process”.

Figure 7.10: Shear energy estimation of AISI 1045 steel alloy

Figure 7.11: Shear energy estimation of aluminium AW6082-T6 alloy

k = -0.32(h/re) + 2.42

R² = 0.94

k = 2.26(h/re)-0.33

R² = 0.91

0

1

2

3

4

5

6

7

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

k (

J/m

m3)

h/re

B

A C

Process

waste

Value

adding

process

k = -0.71(h/re) + 2.25

R² = 0.92

k = 1.01(h/re)-0.94

R² = 0.89

0

4

8

12

16

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

k (J

/mm

3)

h/re

B

A CValue

adding

process

Process

waste


146

Figure 7.12: Shear energy estimation of titanium 6Al-4V alloy

The trend line Equations 7.7, 7.8 and 7.9 of line AB shown in Figures 7.10, 7.11 and 7.12,

represents the maximum specific shear energy equations at which ratio h/re equals zero.

e

Sr

hk 32.042.2max (7.7)

e

Alr

hk 71.025.2max (7.8)

e

Tr

hk 27.191.4max (7.9)

where kS(max), kAl(max) and kT(max) represents maximum specific shear energy of AISI 1045

steel alloy, aluminium AW6082-T6 alloy and titanium 6Al-4V alloy respectively in Jmm-3

.

It also can be deduced that a specific energy value above line AC indicates effects of

ploughing mechanisms (also could include rubbing) and termed “Process waste” zone. For

example during the cutting tests and at fz of 0.01mm/tooth, the total specific energy

k = -1.27(h/re) + 4.91

R² = 0.92

k = 2.78(h/re)-0.51

R² = 0.86

0

2

4

6

8

10

12

14

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

k (

J/m

m3)

h/re

B

A C

Process

waste

Value

adding

process


147

calculated was 5.31, 10.66 and 13.08 Jmm-3

(Table 7.5) for AISI 1045 steel alloy, titanium

6Al-4V alloy and aluminium AW6082-T6 alloy respectively. At these ranges of values, the

ratio h/re tends towards zero (cutting edge radius re = 0.06 mm). This means that the bulk

of the specific energy is due to ploughing mechanisms and infinitesimally small rubbing

mechanisms. It can be deduced therefore that the specific ploughing energy is 54%, 54%

and 83% of the total specific energy demand for AISI 1045 steel alloy, titanium 6Al-4V

alloy and aluminium AW6082-T6 alloy respectively (these values are true for cutting test

conducted at a feed of 0.01 mm/tooth). This percentage gradually decreases as the ratio h/re

increases more than unity. These values also confirms the work of Singh et al. [96] where

they reported that ploughing effects contributes to about 40% to 80% of the total specific

cutting energy in mechanical machining processes.

This value can be said to be true since ploughing effect dominates as values of h/re << 0 or

approaches an infinitesimal values. Therefore the difference between the maximum

specific shear energy and the experimental maximum value is equivalent to the total

specific ploughing energy used up due to the size effect for side milling the workpiece

materials under investigation.

Therefore, from Figures 7.10, 7.11 and 7.12 the area below the linear trend line AB

indicates an area at which the process mechanisms are optimised and is said to be the value

adding zones. Comparatively, area above the maximum specific shear (line AC) indicates

higher specific energy (i.e. increased ploughing and rubbing mechanisms) hence termed

process waste zone. For the purpose of sustainability and resource efficiency, it is therefore

recommended that machining operations be conducted within area within the maximum

specific shear line AC. This area can be estimated for different workpiece materials as

proposed in this paper.


148

The process mechanism model can therefore be deduced from the above analysis as shown

in Figure 7.13. The process mechanism model can be used to further describe the cutting

characteristics depicted in Figure 7.10, 7.11 and 7.12. The process mechanism includes

three mechanisms i.e. rubbing, ploughing and shearing. The mechanisms engaged during

machining are dependent on type of cutting operations and ratio h/re.

Looking at the graphs at Figures 7.10, 7.11 and 7.12 the specific cutting energy shows an

exponential increase when the ratio of un-deformed chip thickness to the cutting edge

radius is less than unity and approaches zero. In this zone, the effective rake angle is

predominantly negative and ploughing and rubbing are the dominant process mechanisms.

It can therefore be inferred that higher specific energies are correlated to process

inefficiency through rubbing and shearing.

The above result shows that at lower ratio h/re, the specific ploughing energy is higher

more than 50% of the specific energy required for milling any workpiece materials. More

so, the values become higher with ductile materials. For example for aluminium AW6082-

T6 alloy, this could be above 60%. The result further confirms the work of Schaller et al.

[105] and Mian et al. [106] that the specific ploughing energy is relatively higher in ductile

materials. This methodology can be applied to determine maximum specific shear energy

demand and specific ploughing energy for machining.

In analysing the process mechanisms, the impact of ploughing and rubbing was quantified

based on the average specific shear energy required to create a value adding operation.

This was achieved by assuming a range of values for the ratio h/re from 0.01 to 2 and

substituting their values into, for example, Equation 7.7 for AISI 1045 steel alloy. The

effect of rubbing and ploughing can hence be shown as in Figure 7.13.


149

This energy demand value is an indicator of the efficiency of the machining operation

engaged. From Figure 7.13, it can be seen that as the ratio of un-deformed chip thickness

to the cutting edge radius increases from 0.01 to 2 and above, the percentage of the

ploughing and rubbing effects gradually decreases from 78% to 5%. This is a further proof

that in order to improve the efficiency of a machining operation and at low tip energy

demand, the ploughing effect should be reduced or eliminated if possible. However, since

the machining processes are a combination of roughen and finishing, the effect of

ploughing might not be eliminated or ignored. For example, in grinding operation whereby

the grit size of the grinding wheel are often equal to or less than the chip thickness, the

ploughing energy could be considerably higher compared to other machining operations.

Figure 7.13: Ploughing energy variations with process parameter for AISI 1045 steel alloy

Assuming machining AISI 1045 alloy steel with h/re = 1 ( i.e. un-deformed chip thickness

and cutting edge radius are both 0.06 mm); then the optimised specific energy for milling

AISI 1045 will be 2.26 J/mm3 as previously shown in Figure 7.9. The estimate for the

0

20

40

60

80

0.01 0.10 0.50 1.00 1.50 2.00

Plo

ugin

g +

ru

bb

ing en

ergy (

%)

h/re


150

specific energy was derived using the power function equation obtained from the specific

energy- h/re.

7.5 Conclusion

In this study, a new optimised value at which ploughing is minimum was developed. The

values correspond to 39.65o swept angle when machining with a tool that has an edge

radius of 60 µm. This value was derived for different workpiece materials and cutting

parameters. Other conclusions derived from the study include:

The specific ploughing energy can be estimated with the proposed methodology of

extrapolation of the specific energy curve to the point where h/re is zero.

The specific shear energy is 52%, 63.8% and 69% for AISI 1045 steel alloy,

titanium 6Al-4V alloy and aluminium AW6082-T6 alloy respectively when

compared to the specific energy values.

Ductility characteristics of workpiece materials affect the specific cutting energy.

In order to improve the process efficiency in mechanical machining, shearing

dominated machining should be encouraged.

Machining efficiency can be improved by over 50% without compromise to

workpiece surface integrity by controlling tool stepover to reduce the energy

wasted in rubbing and ploughing.

A pre-knowledge of the values of the specific ploughing energy can aid pre-process

and support energy resource management.

151

CHAPTER 8

DIRECT ELECTRICAL ENERGY DEMAND IN FUSED

DEPOSITION MODELLING

8.1 Abstract

3D printing is predicted to grow and underpin distributed manufacture of customized and

geometrically complex products. At this early stage of technology development it is timely

to consider and optimize the resource efficiency of these layered manufacturing

technologies. In this work, the direct electrical energy demand in one of the most popular

technologies, fused deposition modelling was studied and a generic model for direct

energy demand in layered manufacture proposed. The performance of Fused Deposition

Modelling was further benchmarked to machining processes in order to throw light on the

relative energy demands for alternative manufacturing processes. The work is a foundation

for electrical energy demand modelling and optimisation for the rapidly expanding 3D

printing processes.

Keywords: Energy; Rapid prototyping; Fused deposition

8.2 Introduction – Layered Manufacturing Technologies

Rapid prototyping (RP) or layered manufacturing (LM) are additive manufacturing

techniques that build up the product layer by layer [107]. In these techniques the part is


152

fabricated from a 3D solid model produced in Computer Aided Design (CAD) packages.

The process is considered to be material efficient because material is added in layers and

therefore reducing the amount of material wasted in producing a part compared to material

removal processes [108]. Additional characteristics and benefits of additive manufacturing

techniques are well documented [109-110]. As rapid prototyping, this technology was

initially developed to produce prototypes of physical models as fast as possible using

polymers [111]. This largely reduces errors and cycle time in new product development

and accelerates time to market. Today, layered manufacturing is growing as a means for

3D printing of customized parts, or as a repair technique for functional high value parts.

In RP technology, CAD models are uploaded to specialist software. This software slices

the model in the z-axis so that an RP machine can construct a 3D replica of model in layers

without needing tooling. Post processing may be required if support material is used in

fabrication.

Various layered manufacturing techniques have been developed, these include Stereo

Lithography (SLA), Fused Deposition Modelling (FDM), Ink Jet Printing (IJP), 3D

Printing (3DP), Selective laser sintering (SLS), Selective laser melting (SLM), 3D laser

cladding process, Laminated object manufacturing (LOM) and Laser chemical vapour

deposition (LCVD) [112-113]. It has been reported that [114] RP can cut costs by up to

70% and reduces time to market of finished parts by 90% when compared to other

conventional manufacturing methods. Manufacturing of functional parts through these

technologies in 3D printing is predicted to grow and underpin distributed manufacture of

customized and geometrically complex products.


153

8.2.1 Fused Deposition Modelling

Fused deposition modelling (FDM) was developed by Stratasys Inc. and has grown to

become one of the most popular RP processes [110]. A thermoplastic filament is unwound

from a spool that supplies material to a heated extrusion nozzle. As the filament passes

through the nozzle, it is melted and extruded onto a build platform to form bonded bead

and road width which rapidly solidifies. The machine follows a hatch strategy for each

model cross-sectional layer as generated in the STL file slicing software. When a layer is

finished, the build platform then indexes down and another layer is fabricated. The

common material used is Acrylonitrile butadiene styrene (ABS), which combines the

strength and rigidity of acrylonitrile and styrene polymers with the toughness of

polybutadiene rubber. ABS has many daily applications as material for example for Lego

bricks, toys, golf club heads, automotive trim components, automotive bumper bars etc.

In FDM process, fabrication occurs inside a temperature controlled chamber. The heated

nozzle is mounted on to a motion system that can move in the X-Y plane within the

chamber. For the base bridge as a foundation to lay the part and for large overhangs and

complex geometries, the nozzle also extrudes support material when required. Thus, the

part has to be post processed to remove any support material. For the Stratasys Dimension

SST FDM, this is done in a heated bath of detergent which selectively dissolves the

support material and leaves the part material. Other FDM machines do not provide this

soluble support material and hence the support structures have to be broken down

manually.

It is generally acceptable that because FDM and other layered manufacturing technologies

are based on material addition they are more material efficient compared to mechanical

machining processes. However the energy intensity of layered manufacturing process has


154

not received much attention. It is timely at this early stage of the technology development

to embed resource efficiency in developing and optimizing manufacturing techniques.

8.2.2 Research Aim

This work was aimed at investigating the direct electrical energy requirements of Rapid

Prototyping (RP) and Rapid Manufacturing (RM) with a view to understand how the

energy demand varies for different FDM machines (machines based on the same process

mechanism), develop a mathematical model or framework for electrical energy modelling

in 3D printing process and evaluating the electrical energy intensity of material additive

process to that of mechanical machining.

The electrical energy requirement for a manufacturing process was studied by Gutowski et

al., [31] who proposed a mathematical model for the electrical energy based on machine

tools on a Toyota automobile production line. In their model, they categorized the

electrical energy demand into two groups i.e. ‘Basic State’ and ‘Cutting State’. Along these

lines the vision for this work was to use energy monitoring and event streaming to study

the energy demand for fused deposition modelling and explore the effect of different FDM

machines available in Manchester but made by different systems developers. To put this in

context, energy demand for Fused Deposition Modelling was benchmarked to using a high

speed milling machine to machine a similar component. The contribution would help to

assess the energy efficiency of FDM technology and identify priority areas for

improvement.


155

8.3 Energy Demand in Fused Deposition Modelling

8.3.1 Energy States of Fused Deposition Modelling Machine

In order to investigate and classify build process activities and energy profile for FDM

machines, a series of reference moves were carried out during the fabrication processes.

The FDM machine was switched ON and the current consumption for the machine states

was measured and categorised. This method allows the current consumption to be

differentiated at each stage of the build process. The current consumption was measured

with the Fluke 345 Power Quality Clamp Meter. The machine cycle was repeated three

times (on different days) to generate and compare current profile at each state. The current

consumption was measured from ‘Start-up’ and at room temperature for each day. The

current profile shown in Figure 8.1 is representative of the current profile ultimately

measured on a Stratasys Dimension SST FDM when building a first component starting

from room temperature.

Figure 8.1: Power-time curve for Stratasys Dimension SST FDM machine building from

room temperature


156

As the FDM machine Starts-up, it took the machine 270 seconds to attain a temperature of

68oC within the build chamber. This start-up time is marked in Figure 8.1. This process

occurred just once in the course of a day or after the machine had switched off and allowed

to cool back down to room temperature. However, once the machine has acquired the

required temperature, it takes less time to be ready for next build. From the first build test

on the Stratasys Dimension SST FDM as shown in Figure 8.1, there were four different

electrical energy consumption states in the FDM build process.

Start-up State: This state occurs after the power up and initial start-up of the

machine.

Warm-up State: This state occurs after the Start-up. The machine is heated up

initially until the build chamber reaches between 61oC to 68

oC inside chamber

temperature. The Warm-up stage continues until the filament materials attain a

temperature of 102oC to enable extrusion through the build nozzle or nozzles. The

melting point of Acrylonitrile Butadiene Styreneplastics ABS material is about

105°C.

Ready State: At this state, the nozzle finds the home position by referencing the x,

y and z-axes and positions itself to a point just about to start building. The machine

could be at this state for longer than necessary depending on the operator’s speed to

load the “SLICE” file.

Build State: The fabrication of the part commences at this state. This state

encompasses any operation that the machine does from receiving the “SLICE” file

(part program) initialisation to part completion. The peaks and the troughs observed

during building on all the energy profiles are a result of the nozzle movement and

material deposition by the FDM machine. The peaks and higher energy periods are


157

when the nozzle is extruding material and actually building the layers of the model.

The lower energy periods are when the nozzle is returning to its start point to begin

building another layer.

Not shown in Figure 8.1 is Post Processing for which Stratasys Dimension SST FDM uses

soluble supports, a water-based solution designed to simply wash away the support

material enabling support removal from complex models. The solution for removing

support material can be NaOH and will be in a powered tank usually operated at a warm

solution temperature and with washing mechanically assisted by ultrasonic vibration. This

adds up the energy demand.

During the first build, it was observed that the electrical energy demand to power up the

FDM machine from Start-up state to Build state was 897 Wh. Figure 8.2 shows that Start-

up, Warm-up, Ready and Build energy demand states consumed 3%, 14%, 73% and 10%

respectively when the FDM machine was started from room temperature to part

completion.

Figure 8.2: Power-time curve for Stratasys Dimension SST FDM machine building from

room temperature


158

After the first build and allowing the machine to cool down for 5 minutes (this time is

assumed the period for unloading and loading a new model for build), the same part was

fabricated again. This was done to compare the effect of temperature on the total electrical

energy demand. As expected, the energy for the warm-up state reduced by 96% as shown

in Figure 8.2. This is a clear indication that batching jobs and building more than one part

can reduce the electrical energy per part considerably.

8.3.2 New Framework for direct energy requirements in FDM

Following the increasingly common classification of manufacturing process energy states

after Gutowski et al., [31] and the Cooperative Effort on Process Emissions in

Manufacturing CO2PE! [24] into Basic and Tip energy, a generic equation for direct

electrical energy requirements in layered manufacturing is proposed as shown in Equation

1. This is based on Basic and Value Adding energy states.

vavabb tPtPE (8.1)

where E is the total electrical energy in J, Pb, Pva represents basic and value adding power

in W, and tb, tva are the corresponding time for basic and value adding operations in

seconds. Equation 8.1 can be expanded for the value adding energy demand as shown in

Equation 8.2.

vaRmbb tVetPE (8.2)

where E is the direct energy requirement in J for RP and RM processes, bP is the basic

idle power in W consumed for non value adding activities, bt is the basic energy state

duration in s, me is the specific material printing energy as determined by the materials and


159

process mechanism in Jmm-3

, RV is the volumetric manufacturing rate in mm3/s and vat is

the build time in s.

The value bP and bt can be expanded into start-up state, warm up state, ready state, basic

state, nozzle positioning and post processing power demand. These can be measured for

particular machines.

8.3.3 Benchmarking of 3 different FDM technologies

To explore the variability of energy demand according to the machine systems concepts, 3

different FDM machines available at the University of Manchester were used to build a

simple standardized model, shown in Figure 8.3, of 9,000 mm3 volume. Table 8.1 shows

the specifications of the machines used, while Figures 8.4 and 8.5 shows the images of the

FDM machines.

Figure 8.3: A simple model fabricated on 3 FDM machines to study energy demand


160

The Stratasys Dimension SST and the Dentford Inspire D290 are standard size machines

with enclosed build chambers while the PP3DP is a miniature open chamber machine as

evident from the size of the filament diameter in relation to the machine envelope.

Table 8.1: FDM Machines investigated

Stratasys Dimension SST

FDM

Dentford Inspire

D290

PP3DP

Size (mm) 914 x 686 x 1041 720 x 850 x 1650 245 x 260 x 350

Model Material ABS ABS & PLA

Plastic

ABS

Build Envelope

(mm)

203 x 203 x 305 255 x 290 x 320 140 x 140 x 135

Software Catalyst TierTime Model

Wizard

TierTime Model

Wizard

Jetting heads 2 nozzles 2 nozzles 1 nozzle

Layer thickness

(mm)

0.254 0.100 0.150

Rated Power

(KW)

1.6 2.0 0.22


161

Figure 8.4: From left Dimension SST FDM, Dentford Inspire D290 and PP3DPP

Figure 8.5: Detailed view of low a cost FDM machine model PP3DPP

Figures 8.6, 8.7 and 8.8 represent the power profile measured to fabricate a similar part

from room temperature. It can be observed that the power profile trend for the three FDM

machines follows similar trend as modelled in Equations 8.1 and 8.2. From data input


162

there is a drop in the power as the machine processes the data that has been fed, then, there

is a spike in the power as the machine begins to extrude material and build the part.

Figure 8.6: Power-time curve for Stratasys Dimension SST machine building from room

temperature.


163

Figure 8.7: Power-time plot for Dentford Inspire D290 machine building from room

temperature

Figure 8.8: Power-time plot for PP3DP machine building from room temperature


164

From Figures 8.6, 8.7 and 8.8 the machine with the largest area under the Power – Time

curve has the highest energy demand. Inspire D290 uses a lot more energy compared to the

Stratasys Dimension SST FDM machine, while the miniature open Fused Deposition

modelling machine has the least energy demand. It however, needs to be noted that the

functional unit produced is different; the Stratasys Dimension SST allows easy support

material, while the Dentford Inspire D290 builds a honey comb structure and saves

material. Comparing similar standard size machines it can be deduced from Figures 8.6,

8.7 and 8.8 that the energy consumed can be higher by 256% for one standard size FDM

machine technology and design. When the cheapest miniature machine with open build

envelope which is not temperature controlled is compared to the Inspire D290, the energy

demand is over 20 times lower. Clearly there is significant opportunity for improving the

energy demand of different FDM technologies.

8.4 Energy Demand for 3D printing versus Machining

A further study was set up to compare the energy demand in additive manufacture to that

in subtractive manufacture. Similar volume of ABS material was milled (in this case 9000

mm3) during end-milling operation on Mikron HSM 400 Machining centre. The high speed

milling machine is chosen because when geometric complexity is not an issue the machine

can be used for rapid fabrication of prototypes. Thus this part of the study compares

alternative manufacturing process in terms of resource efficiency. The cutting and process

parameters used on the Mikron HSM 400 Machining centre are stated in Table 8.2.


165

Table 8.2: Parameters for milling on Mikron HSM 400 Machining centre

Machine spindle HVC140-SB-10-

15/42-3F-HSK-E40

Workpiece Material ABS

Spindle speed

(RPM)

9549

Feedrate (mm/min) 1910

Depth of cut (mm) 0.5

Tool diameter (mm) 10

Number of cutting

edges on tool

4

The current profile as recorded by the Fluke 345 power clamp meter, was converted into a

power profile for the end-milling operation, is shown in Figure 8.9. The area under the

power-time graph and the total energy demand when end-milling ABS was 114 Wh.


166

Figure 8.9: Power profile end-milling 9000 mm3 on Mikron HSM 400 Machining centre

The result of the analysis was benchmarked with the FDM machine as shown in Table 8.3.

The result shows that the Basic power required for keeping the Mikron HSM 400 running

was 90% of that required by the FDM machine. This is the result of various electricity

consuming auxiliary units that keep the Mikron HSM 400 functional for example, un-

loaded motors, pumps, lights, computer. The Ready state of the FDM consumed 57% more

power than the Mikron HSM 400.

The cycle time to process similar volume of materials was approximately 22 times higher

to fabricate 9000 mm3 on the FDM machine when compared with machining on the

Mikron HSM 400 centre. Taking the power and cycle time into account, the FDM

machine demanded 6 times more energy processing the same volume of material compared

to the Mikron HSM 400 machining centre. Reflecting on these results it can be proposed

that the biggest challenges for FDM and layered manufacturing technologies, if they are to


167

be as resource efficient as machining, is to address the high cycle time and low fabrication

rate.

Table 8.3: Energy benchmarking FDM versus mechanical milling

FDM

machine

Mikron HSM

400 milling

machine

% difference

FDM/Milling

Basic Power (W) 270 2904 9%

Ready Power (W) 934 401 233%

Total Cycle Time (s) 3012 137 2198%

Total energy demand (Wh) 685 114 601%

The ultrasonic cleaning tank for the Stratasys Dimension SST demanded about 250W. The

solution is operated in a tank that has ultrasonic vibration and a heater. The Stratasys

Dimension SST FDM model that was benchmarked to mechanical milling was washed in

approximately 3600 s. This adds another 250Wh to the energy demand for FDM, thus

making a total energy demand of 935 Wh for the data given in Table 8.3. Thus considering

the post processing energy demand, the FDM machine required 8 times more energy

compared to using a milling machine. A step change in the build rate of FDM machines

and layered manufacturing machines will help to significantly bridge the gap in relation to

energy efficiency compared to material removal processes.


168

8.5 Conclusions

This work presented an evaluation of the direct electrical energy demand in Fused

Deposition Modelling, one of the most popular 3D printing technologies. The following

conclusions can be deduced from the study:

1. When standard size enclosed chamber FDM machines are used for the first build,

the energy demand required raising the temperature within the build chamber and

preparing the machine for extrusion can be a very large proportion of the total

energy demand in building the first component or set of nested components. The

warm up time for the FDM machine is considerably high. This can be an area of

improvement for energy efficiency and sustainable manufacture to meet the goals

of energy efficiency. New temperature ramp up cycles and heaters can be designed

to reduce this energy demand.

2. Given that in terms of production planning, first builds in FDM processes are

associated with a higher energy demand due to the thermo ramp-up cycles,

planning jobs back-to-back can help reduce the energy demand per part.

3. For FDM machines using the soluble support removal process, the energy demand

for the cleaning process is not insignificant; this is due to the need to elevate

solution temperature and to induce ultrasonic vibration for enhanced cleaning. The

energy for cleaning was 35% of the build energy for the case considered.

4. The energy demand in FDM can be modelled as Basic energy demand by the

machine and Value Adding energy for the extrusion process. A framework can be

developed by monitoring current usage and event streaming the activities

performed by the machine.


169

5. A step change in the build rate of FDM and other layered manufacturing machines

will help to significantly bridge the gap in relation to energy efficiency compared to

material removal processes.

6. When compared with the alternative machining process, although, the basic energy

demand of Mikron HSM 400 machining centre was 90% higher than the FDM

machine it only needed 6 times lower energy compared to FDM in building the

same part. This was mainly due to the relatively high cycle time for the FDM and

low manufacturing rate.

7. While for FDM machines, the long build cycle time is the major challenge that

need to be addressed in order to reduce energy intensity of manufacture, for

mechanical machine tools reducing the Basic Power and energy demand can have

significant impact on the energy efficiency.

8. Considering one 3D printing technology such as FDM, there is a major difference

in energy demand for different machine platforms. This is evident that significant

opportunities exist for system developers to radically improve the energy efficiency

of 3D printing technologies.

9. In Life Cycle Analysis (LCA) it is more accurate to include the fabrication rates if

the environmental impact of layered manufacturing processes is to be accurately

captured.

170

CHAPTER 9

CONCLUSIONS AND RECOMMENDATIONS FOR FUTURE

WORK

9.1 Conclusions

This investigation undertook a critical review of research work on electrical energy

intensity in machining processes. The literature review revealed that a more robust energy

estimation model was not developed that could account for machine tool characteristics

and process parameters.

A new mathematical model and logic for predicting direct electrical energy requirements in

machine tool paths was developed. The hypothesis that the specific cutting energy in

machining should be dependent on the scale of machining and the thickness of material to

be removed was tested and validated and a new specific energy coefficient model for 3

different workpiece materials was developed which capture size effect phenomenon in

machining, and machinability of materials.

This further led to the research to improve the integrity of specific cutting energy

coefficients for energy demand modelling in which the effect of chip thickness, tool wear,

nose radius and cutting environment on specific tip energy was quantified. Based on this

knowledge, the mechanical machining efficiency was linked to specific energy through the

process mechanisms characteristics.

Chapter 10 Conclusions and recommendations for future work

171

Furthermore, in evaluating the process efficiency applications, a case study of electrical

energy demand for Fused Deposition Modelling was benchmarked to material removal

operation in order to characterise process efficiency and sustainable manufacture of parts

based Fused Deposition Modelling and mechanical machining process.

Finally, the impact of machine tools on the direct electrical energy demand and associated

carbon emissions for a standardized NC toolpaths was evaluated in two different locations

in order to assess the impact on CO2 emission of machining at different geographical

locations.

In this research therefore the following conclusions were drawn from the study:

1. Machine tool electricity consumption can be classified into the ‘Basic’, ‘Ready’

and ‘Cutting’ states. The need for the third ‘ready’ state was proposed and

incorporated into the existing electrical energy model. The machine tool ‘ready’

state brings the cutting tool and workpiece to a proximity state or an about to cut

state.

2. The basic and ready state power consumption varies with machine tools. The

current demand of the basic states at no load was 53%, 72% and 63% on MHP

CNC lathe, Takisawa milling machine and Mikron HSM 400 machining centre

respectively and their corresponding machine ready states was 47%, 28%, and 37%

respectively. This further confirms the need to incorporate the ready state energy

demand into the electrical energy demand model of mechanical machining

processes.

3. The coolant flow contributes 14% and 21% of the total electrical energy demand

during machining on the Takisawa milling machine and Mikron HSM 400


172

respectively. In this case, dry or near dry (MQL) machining will save between 14-

21% energy of the total electrical energy resource.

4. A new generic model for energy demand was developed as is shown by Equation

4.15 and restated here.

where represent tool change power and tool change time respectively.

The cutting time, t2 and Tool life T and material removal rate can be modelled for turning

and milling as a function of cutting velocity variables thus enabling the use of the equation

in process planning.

The advantages of the model are as follows:

The tool change energy demand was characterized into the number of tool

changes needed for machining operations by incorporating the tool life

equation. It is shown that the tool change energy demand increases with

complex component designs that will require a number of tool changes. The

electrical energy demand of tool change depends on the process parameters,

cutting tool geometry and number of machining operation on the workpiece

material.

Modelling energy with an explicit effect of cutting speeds, feed and depth

of cut.

Explicitly modelling the energy required to take a machine tool from the

basic state to a state where the axis and tool is ready for action and about to

cut.


173

Acknowledging the influence of the machinability characteristics of

different workpiece materials.

5. The specific cutting energy, developed for the first time in this thesis, can be

modelled from the following generic relationship.

x

ee hKk


at the required un-deformed chip thickness

and Ke is the specific area energy in Jmm-2

at un-deformed chip thickness of 1 mm, and h is

un-deformed chip thickness in mm while x is the specific energy exponent.

6. A representative average value of specific energy for different workpiece materials

is evaluated at a condition where the un-deformed chip thickness is equal to the tool

edge radius. On this basis the average specific energy in conventional machining

for a positive 5 degree rake angle carbide tools is 1.007, 2.260 and 2.782 Jmm-3

for

aluminium AW6082-T6 alloy, AISI 1045 steel alloy and titanium alloy

respectively.

7. The specific ploughing energy can be estimated with the proposed methodology of

extrapolation of the specific energy curve to the point where h/re is zero.

8. When compared with the alternative machining process, although, the basic energy

demand of Mikron HSM 400 machining centre was 90% higher than the FDM

machine it only needed 6 times lower energy compared to FDM in building the

same part. This was mainly due to the relatively high cycle time for the FDM and

low manufacturing rate.

9. The energy demand in FDM can be modelled as Basic energy demand by the

machine and value adding energy for the extrusion process. A framework can be


174

developed by monitoring current usage and event streaming the activities

performed by the machine.

10. A step change in the build rate of FDM and other layered manufacturing machines

will help to significantly bridge the gap in relation to energy efficiency compared to

material removal processes.

11. While for FDM machines, the long build cycle time is the major challenge that

need to be addressed in order to reduce energy intensity of manufacture, for

mechanical machine tools reducing the Basic Power and energy demand can have

significant impact on the energy efficiency.

12. Considering one 3D printing technology such as FDM, there is a major difference

in energy demand for different machine platforms. This is evident that significant

opportunities exist for system developers to radically improve the energy efficiency

of 3D printing technologies.

13. In Life Cycle Analysis (LCA) it is more accurate to include the fabrication rates if

the environmental impact of layered manufacturing processes is to be captured.

14. When more energy efficient machines are used with a typical 20% lower energy

demand, their carbon emission signature can be significantly increased by moving

the machine from one geographical location to another due to differences in carbon

emission signature between nation states. This may increasingly become a relevant

consideration due to international mobility of capital and manufacturing businesses.

Introduction of carbon emission penalties or quotas will make this even more

critical.

It is therefore clear from this research that the total electrical energy demand and the

specific energy required can be estimated using the new generic models presented to


175

ensure that electrical energy demand is not underestimated. A pre-knowledge of the values

of the specific energy can aid pre-process, resource efficient machining and energy

resource management.

In meeting the research objectives of this study, the electrical energy intensity of

mechanical machine tools was investigated through an event streaming methodology to

record data for actions and tasks involved during turning and milling processes. The

electricity consumption of individual events and/or tasks were recorded with a FLUKE 345

power clamp meter and visualized on the event mapping graph. A new model for

predicting the electrical energy requirements in mechanical machining processes was

developed to include the cutting variables of feed rate, speed and depth of cut. Machining

mechanisms were also analysed with particular focus on specific cutting energy and a new

generic model and specific energy data were derived statistically for cutting three widely

used workpiece materials. The process mechanisms and specific energy evaluation were

used to define new energy efficiency evaluation metrics for machining processes.

9.2 Major Research Contributions to Knowledge

This research provides significant and new and novel contributions to knowledge as

indicated by the following research output:

1. Proposed the new “Ready” state energy demand as the third transition state of the

machine tool. This energy state is a transitive state of the machine tool at a point

just about to cut. The proposed new addition will increase the knowledge and

understanding for electrical energy reduction strategy for product and process

planning and to evaluate resource efficiency of machine tool states.


176

2. Proposed and validated a new and an improved generic electrical energy model for

machining processes. This is to enable an accurate estimation of electrical energy

demand in mechanical machining operations for process planning and life cycle

assessment.

3. Proposed a new generic specific energy model for three different engineering

materials.

4. Introduced novel idea to assess process energy efficiency or effectiveness during

mechanical machining based on specific ploughing energy. This can be the basis

for energy rating of machining toolpaths.

9.3 Recomendations for future work

To apply the knowledge developed in this thesis, there is a need to develop energy demand

software which encompasses all the variables applicable to machine tools as proposed in

this report. These variables for example the machine tool basic state, ready state and

cutting state energy demand, the specific cutting energy demand for different materials,

toolpath and NC g-codes generation from CAD/CAM software, etc will all be incorporated

into the architecture of the software for the analysis of total electrical energy demand and

carbon dioxide evaluations. This will enable engineers, production planners, process

managers and machine tools designers to have a pre-process understanding of electrical

energy resource, CO2 emission and cost implications of their product before engaging in

the manufacturing processes.

It is intended that the models which should be targeted towards the small and medium

scale enterprise (SME) will enhance process planning, energy and cost savings for machine

tool optimization and process control.


177

To support the software, further study should be conducted.

Develop and validate software for estimating the energy intensity in machining and

their associated carbon footprints for CNC programs for selected NC controllers

and CAD/CAM postprocessor.

Develop specific energy data and equations for workpiece materials not covered

here.

Model the effect of workpiece properties such as hardness on specific energy.

Develop a methodology for linking the energy models to CNC codes, APT files,

different controllers and post-processors.

Considers energy reduction in the manufacturing objectives in multiple objective

functions such as optimisation, cost, production rate etc.

178

REFERENCES

[1] Brundtland, G. H., Report of the World Commission on environment and

development: “our common future”, United Nations, 1987.

[2] Pusavec, F., Krajnik, P., Kopac, J., Transitioning to sustainable production - Part I:

application on machining technologies, Journal of Cleaner Production, 2010; 18

(2): 174-184.

[3] UN, World Commission on Environment and Development, Our Common Future,

Oxford University Press, London, 1987.

[4] Park, C. W., Kwon, K. S., Kim, W. B., Min, B. K., Park, S. J., Sung, I. H., Yoon,

Y. S., Lee, K. S., Lee, J. H., Seok, J., Energy consumption reduction technology in

manufacturing - A selective review of policies, standards, and research,

International Journal of Precision Engineering and Manufacturing, 2009; 10: 151-

173.

[5] Dang, H. T. J., Wilkinson, D., New Zealand: Energy in Brief, in, New Zealand

Ministry of Economic Development, Wellington, New Zealand, 2007.

[6] IEA, Key World Energy Statistics 2013,

<http://www.iea.org/publications/freepublications/publication/KeyWorld2013.pdf>

, 2013: [accessed 04-01-2014].

[7] Digest of United Kingdom energy statistics (DUKES), Department of Energy and

Climate Change, TSO 2010,

<www.decc.gov.uk/assets/decc/11/stats/publications/dukes/2303-dukes-2011-

chapter-1-energy.pdf> 2010: [accessed 14-11-2011].

[8] OECD/IEA, International Energy Agency,

<http://www.iea.org/textbase/nppdf/free/2011/key_world_energy_stats.pdf> 2011:

[accessed 08-07-2012].

[9] He, Y., Liu, B., Zhang, X. D., Gao, H., Liu, X. H., A modeling method of task-

oriented energy consumption for machining manufacturing system, Journal of

Cleaner Production, 2012; 23: 167-174.

[10] EPTA, Study for Preparing the First Working Plan of the Eco-design Directive,

Report for tender No. ENTR/06/026 <ec.europa.eu/enterprise/policies/sustainable-

business/files/workingplan_finalreport_en.pdf> 2007: [accessed 30-03-2012].

179

[11] Abele, E., Sielaff, T., Schiffler, A., Rothenbücher, S., Analyzing Energy

Consumption of Machine Tool Spindle Units and Identification of Potential for

Improvements of Efficiency, in: Hesselbach, J., Herrmann, C., (Eds.) Glocalized

Solutions for Sustainability in Manufacturing, Springer Berlin Heidelberg, 2011:

280-285.

[12] Kalpakjian, S., Schmid, S., Manufacturing processes for engineering materials,

Prentice-Hall, Englewood Cliffs, New Jersey, 2003.

[13] Trent, E. M., Wright, P. K., Metal cutting, 4th edition, Butterworth-Heinemann,

Boston, MA 2000.

[14] Dahmus, J., Gutowski, T., An environmental analysis of machining, 2004 ASME

International Mechanical Engineering Congress (IMECE) and R&D Expo,

Anaheim, California, 2004: 1-10.

[15] Thyer, G., Computer numerical control of machine tools, Industrial Press Inc.,

USA, 1991: 318.

[16] Hanafi, I., Khamlichi, A., Cabrera, F. M., Almansa, E., Jabbouri, A., Optimization

of cutting conditions for sustainable machining of peek cf30 using tin tools, Journal

of Cleaner Production, 2012; 33: 1-9.

[17] Helu, M., Behmann, B., Meier, H., Dornfeld, D., Lanza, G., Schulze, V., Total Cost

Analysis of Process Time Reduction as a Green Machining Strategy, Leveraging

Technology for a Sustainable World, 2012: 299-304.

[18] Herrman, C., Bergmann, L., Thiede, S., Zein, A., Energy Labels for Production

Machines – An Approach to Facilitate Energy Efficiency in Production Systems,

Proceedings of 40th CIRP International Seminar on Manufacturing Systems,

Liverpool, UK, 2007.

[19] ISO14955-2, Methods of evaluating energy efficiency of machine tools and

machine components, in: Working draft,

<http://www.iso.org/iso/iso_catalogue/catalogue_tc/catalogue_detail.htm?csnumbe

r=55294>, [accessed 14-04-2012].

[20] Aggarwal, A., Singh, H., Kumar, P., Singh, M., Optimizing power consumption for

CNC turned parts using response surface methodology and Taguchi's technique--A

comparative analysis, Journal of Materials Processing Technology, 2008; 200: 373-

384.

180

[21] Diaz, N., Helu, M., Jarvis, A., Tönissen, S., Dornfeld, D., Schlosser, R., Strategies

for Minimum Energy Operation for Precision Machining, Proc. of MTTRF 2009

Annual Meeting, PRC, Shanghai, 2009: 47-50.

[22] Vijayaraghavan, A., Dornfeld, D., Automated energy monitoring of machine tools,

CIRP Annals - Manufacturing Technology, 2010; 59: 21-24.

[23] Li, Y., He, Y., Wang, Y., Yan, P., Liu, X., A framework for characterising energy

consumption of machining manufacturing systems, International Journal of

Production Research, 2013: 1-12.

[24] Kellens, K., Dewulf, W., Overcash, M., Hauschild, M. Z., Duflou, J. R.,

Methodology for systematic analysis and improvement of manufacturing unit

process life cycle inventory (UPLCI) CO2PE! initiative (cooperative effort on

process emissions in manufacturing). Part 2: case studies, International Journal of

Life Cycle Assessment, 2012; 17: 242-251.

[25] Ostaeyen, J. V., CO2PE! (Cooperative effort on process emissions in

manufacturing) - Taxonomy, <www.mech.kuleuven.be/co2pe!/taxonomy.php>,

[accessed 13-01-2012].

[26] Chapman, P., Energy costs: a review of methods, Energy Policy, 1974; 2: 91-103.

[27] Neugebauer, R., Wabner, M., Rentzsch, H., Ihlenfeldt, S., Structure principles of

energy efficient machine tools, CIRP Journal of Manufacturing Science and

Technology, 2011; 4: 136-147.

[28] Rahimifard, S., Seow, Y., Childs, T., Minimising Embodied Product Energy to

support energy efficient manufacturing, CIRP Annals - Manufacturing Technology,

2010; 59: 25-28.

[29] Mativenga, P. T., Rajemi, M. F., Calculation of optimum cutting parameters based

on minimum energy footprint, CIRP Annals - Manufacturing Technology, 2011;

60: 149-152.

[30] Kordonowy, D. N., 2001. A Power Assessment of Machining Tools, Massachusetts

Institute of Technology, B.S. Thesis, Department of Mech. Eng., Cambridge, MA,

USA, 2001.

[31] Gutowski, T., Dahmus, J., Thiriez, A., 2006. Electrical energy requirements for a

manufacturing process, Proceedings of 13th CIRP International Conference on Life

Cycle Eng, Leuven, Belgium, 2006: 623.

181

[32] Gutowski, T. G., Murphy, C. F., Allen, D. T., Bauer, D. J., Bras, B., Piwonka, T.

S., Sheng, P. S., Sutherland, J. W., Thurston, D. L., Wolff, E. E., “WTEC Panel

Report on Environmentally Benign Manufacturing (EBM),” International

Technology Research Institute, World Technology (WTEC) Division, Baltimore,

Maryland <http://www.wtec.org/loyola/ebm/ebm.pdf>, 2001: [accessed 24-10-

2012].

[33] Behrendt T., Zein, A., Min, S., Development of an energy consumption monitoring

procedure for machine tools, CIRP Annals-Manufacturing Technology, 2012; 61

(1): 43-46.

[34] Mori, M., Fujishima, M., Inamasu, Y., Oda, Y., A study on energy efficiency

improvement for machine tools, CIRP Annals-Manufacturing Technology, 2011;

60: 145-148.

[35] Avram, O. I., Xirouchakis, P., Evaluating the use phase energy requirements of a

machine tool system, Journal of Cleaner Production, 2011; 19: 699-711.

[36] He, Y., Liu, F., Wu, T., Zhong, F. P., Peng, B., Analysis and estimation of energy

consumption for numerical control machining, Proceedings of the Institution of

Mechanical Engineers Part B-Journal of Engineering Manufacture, 2012; 226: 255-

266.

[37] Hesselbach, J., Herrmann, C., Detzer, R., Martin, L., Thiede, S., Lüdemann, B.,

Energy Efficiency through optimized coordination of production and technical

building services, Proceeding of the 15th Conference on Life Cycle Engineering,

Sydney, 2008.

[38] Hu, S., Liu, F., He, Y., Hu, T., An on-line approach for energy efficiency

monitoring of machine tools, Journal of Cleaner Production, 2012; 27: 133-140.

[39] Jingxiang, L., Renzhong, T., Shun, J., Therblig-based energy supply modeling of

computer numerical control machine tools, Journal of Cleaner Production,

Available online 24 October 2013, ISSN 0959-6526,

http://dx.doi.org/10.1016/j.jclepro.2013.09.055.

[40] Salonitis, K., Ball, P., Energy efficient manufacturing from machine tools to

manufacturing systems, Procedia CIRP, 2013; 7: 634-639.

[41] Seow, Y., Rahimifard, S., A framework for modelling energy consumption within

manufacturing systems, CIRP Journal of Manufacturing Science and Technology,

2011; 4 (3): 258-264.

182

[42] Rajemi, M. F., Mativenga, P. T., Aramcharoen, A., Sustainable machining:

selection of optimum turning conditions based on minimum energy considerations,

Journal of Cleaner Production, 2010; 18: 1059-1065.

[43] Sarwar, M., Persson, M., Hellbergh, H., Haider, J., Measurement of specific cutting

energy for evaluating the efficiency of bandsawing different workpiece materials,

International Journal of Machine Tools and Manufacture, 2009; 49: 958-965.

[44] Li, W., Kara, S., An empirical model for predicting energy consumption of

manufacturing processes: a case of turning process, Proceedings of the Institution

of Mechanical Engineers Part B-Journal of Engineering Manufacture, 2011; 225:

1636-1646.

[45] Draganescu, F., Gheorghe, M., Doicin, C. V., Models of machine tool efficiency

and specific consumed energy, Journal of Materials Processing Technology, 2003;

141: 9-15.

[46] Diaz, N., Redelsheimer, E., Dornfeld, D., Energy consumption characterization and

reduction strategies for milling machine tool use, Glocalized Solutions for

Sustainability in Manufacturing, Springer, Berlin-Heidelberg, 2011; 263-267.

[47] Lucca, D., Seo, Y., Komanduri, R., Effect of tool edge geometry on energy

dissipation in ultraprecision machining, CIRP Annals-Manufacturing Technology,

1993; 42: 83-86.

[48] Arsecularatne, J., On tool-chip interface stress distributions, ploughing force and

size effect in machining, International Journal of Machine Tools and Manufacture,

1997; 37: 885-899.

[49] Ghosh, S., Chattopadhyay, A., Paul, S., Modelling of specific energy requirement

during high-efficiency deep grinding, International Journal of Machine Tools and

Manufacture, 2008; 48: 1242-1253.

[50] Pawade, R. S., Sonawane, H. A., Joshi, S. S., An analytical model to predict

specific shear energy in high-speed turning of Inconel 718, International Journal of

Machine Tools and Manufacture, 2009; 49: 979-990.

[51] Guo, Y., Loenders, J., Duflou, J., Lauwers, B., Optimization of Energy

Consumption and Surface Quality in Finish Turning, Procedia CIRP, 2012; 1: 512-

517.

183

[52] Teti, R., Jemielniak, K., O’Donnell, G., Dornfeld, D., Advanced monitoring of

machining operations, CIRP Annals-Manufacturing Technology, 2010; 59: 717-

739.

[53] Shi, D., Gindy, N. N., Development of an online machining process monitoring

system: Application in hard turning, Sensors and Actuators A: Physical, 2007; 135:

405-414.

[54] Atluru, S., Deshpande, A., Data to information: can MTConnect deliver the

promise?, Transactions of NAMRI/SME, 2009; 37: 197-204.

[55] De-Filippi, A., Ippolito, R., Micheletti, G. F., NC Machine Tools as Electric Energy

Users, CIRP Annals - Manufacturing Technology, 1981; 30: 323-326.

[56] Helu, M., Vijayaraghavan, A., Dornfeld, D., Evaluating the relationship between

use phase environmental impacts and manufacturing process precision, CIRP

Annals - Manufacturing Technology, 2011; 60: 49-52.

[57] Li, W., Zein, A., Kara, S., Herrmann, C., An Investigation into Fixed Energy

Consumption of Machine Tools, Glocalized Solutions for Sustainability in

Manufacturing, 2011: 268-273.

[58] Protocol K., United Nations framework convention on climate change, Kyoto

Protocol, Kyoto, 1997.

[59] Devoldere, T., Dewulf, W., Deprez, W., Willems, B., Duflou, J., Improvement

Potential for Energy Consumption in Discrete Part Production Machines Advances

in Life Cycle Engineering for Sustainable Manufacturing Businesses, in: S. Takata,

Y. Umeda (Eds.), Springer London, 2007: 311-316.

[60] Anderberg, S. E., Kara, S., Beno, T., Impact of energy efficiency on computer

numerically controlled machining, Proceedings of the Institution of Mechanical

Engineers Part B-Journal of Engineering Manufacture, 2010; 224: 531-541.

[61] Diaz, N., Redelsheimer, E., Dornfeld, D., Energy Consumption Characterization

and Reduction Strategies for Milling Machine Tool Use, Glocalized Solutions for

Sustainability in Manufacturing, in: J. Hesselbach, C. Herrmann (Eds.) 18th CIRP

International Conference on Life Cycle Engineering, Springer London, Technische

Universität Braunschweig, Braunschweig, Germany, 2011: 263-267.

[62] Balogun, V. A., Mativenga, P. T., Modelling of direct energy requirements in

mechanical machining processes, Journal of Cleaner Production, 2013; 41: 179-

186.

184

[63] Li, L., Yan, J., Xing, Z., Energy requirements evaluation of milling machines based

on thermal equilibrium and empirical modelling, Journal of Cleaner Production,

2013; 52: 113-121.

[64] Wang, Q., Liu, F., Li, C., An Integrated Method for Assessing the Energy

Efficiency of Machining Workshop, Journal of Cleaner Production, 2013; 52: 122-

133.

[65] Boothroyd, G., Knight, W., Fundamentals of machining and machine tools, Marcel

Dekker, New York, 1989.

[66] Kuram, E., Ozcelik, B., Bayramoglu, M., Demirbas, E., Simsek, B. T.,

Optimization of cutting fluids and cutting parameters during end milling by using

D-optimal design of experiments, Journal of Cleaner Production, 2013; 42: 159-

166.

[67] Paul, S., Chattopadhyay, A., A study of effects of cryo-cooling in grinding,


[68] Polini, W., Turchetta, S., Force and specific energy in stone cutting by diamond

mill, International Journal of Machine Tools and Manufacture, 2004; 44: 1189-

1196.

[69] Bayoumi, A. E., Yücesan, G., Hutton, D. V., On the closed form mechanistic

modeling of milling: Specific cutting energy, torque, and power, JMEP, 1994; 3:

151-158.

[70] Zhang, T., Liu, Z. Q., Xu, C. H., He, N., Li, L., Experimental Investigations of Size

Effect on Cutting Force, Specific Cutting Energy, and Surface Integrity during

Micro Cutting, in: Materials Science Forum, Trans Tech Publ, 2012: 371-376.

[71] Zhang, T., Liu, Z., Xu, C., Influence of size effect on burr formation in micro

cutting, The International Journal of Advanced Manufacturing Technology, 2013:

1-7.

[72] Aramcharoen, A., Mativenga, P., Size effect and tool geometry in micromilling of

tool steel, Precision Engineering, 2009; 33: 402-407.

[73] Bissacco, G., Hansen, H. N., De-Chiffre, L., Size effects on surface generation in

micro milling of hardened tool steel, Cirp Annals-Manufacturing Technology,

2006; 55: 593-596.

185

[74] Ducobu, F., Filippi, E., Rivière-Lorphèvre, E., Chip Formation and Minimum Chip

Thickness in Micro-milling, Proceedings of the 12th CIRP Conference on

modelling of machining operations, 2009: 339-346.

[75] Filiz, S., Conley, C. M., Wasserman, M. B., Ozdoganlar, O. B., An experimental

investigation of micro-machinability of copper 101 using tungsten carbide micro-

endmills, International Journal of Machine Tools and Manufacture, 2007; 47: 1088-

1100.

[76] Kronenberg, T., Basic Course of the Machine Cutting Teachings, Springer-Verlag,

Berlin, 1927: (in German).

[77] Schroder, H. J., The importance of small chip thickness in rolling milling cutter,

Mech. Eng. Bus., 1934; 13: 541–548 (in German).

[78] Kienzle, O., The determination of forces and services to metal cutting machine

tools, Assoc. Ger. Eng., 1952; 94: 299–305 (in German).

[79] Hucks, K., Cutting forces and mechanics of materials, Ind. Indicators, 1955; 77:

365–370 (in German).

[80] Sabberwal, A. J. P., Cutting forces in down milling, International Journal of

Machine Tool Design and Research, 1962; 2: 27-41.

[81] Xu, X. L., Wang, H. L., Yu, T. B., Research and Application of NC Machine Tool

Energy Consumption Control Optimization, Key Engineering Materials, 2014; 579:

314-319.

[82] Balogun, V. A., Aramcharoen, A., Mativenga, P. T., Chuan, S. K., Impact of

Machine Tools on the Direct Energy and Associated Carbon Emissions for a

Standardized NC Toolpath, in: Re-engineering Manufacturing for Sustainability,

Springer, 2013: 197-202.

[83] ISO14020:2000, Environmental labels and declarations-general principles, Geneva,

Switzerland, 2000.

[84] International Standard Organisation, Win the energy challenge with

ISO50001:2011, <www.iso.org/iso/iso_50001_energy.pdf>, 2011: [accessed 01-

09-2012].

[85] Weinert, K., Inasaki, I., Sutherland, J., Wakabayashi, T., Dry machining and

minimum quantity lubrication, Cirp Annals-Manufacturing Technology, 2004; 53:

511-537.

186

[86] Oda, Y., Kawamura, Y., Fujishima, M., Energy Consumption Reduction by

Machining Process Improvement, Procedia CIRP, 2012; 4: 120-124.

[87] Zolgharni, M., Jones, B., Bulpett, R., Anson, A., Franks, J., Energy efficiency

improvements in dry drilling with optimised diamond-like carbon coatings,

Diamond and Related Materials, 2008; 17: 1733-1737.

[88] Balogun, V. A., Mativenga, P. T., Impact of un-deformed chip thickness on specific

energy in mechanical machining processes, Journal of Cleaner Production, 2014;

69: 260-268.

[89] Childs T., Friction modelling in metal cutting, Wear, 2006; 260: 310-318.

[90] DeVor, R. E., Kapoor, S. G., On the modeling and analysis of machining

performance in micro-endmilling, Part I: Surface Generation, Journal of

Manufacturing Science and Engineering, Transactions of the ASME, 2004; 126 (4):

685–694.

[91] Gillespie, L., Deburring precision miniature parts, Precision Engineering, 1979; 1:

189-198.

[92] Lee, K., Dornfeld, D. A., Micro-burr formation and minimization through process

control, Precision Engineering, 2005; 29: 246-252.

[93] Lucca, D., Rhorer, R., Komanduri, R., Energy dissipation in the ultraprecision

machining of copper, CIRP Annals-Manufacturing Technology, 1991; 40: 69-72.

[94] Waldorf, D. J., A simplified model for ploughing forces in turning, Journal of

manufacturing processes, 2006; 8: 76-82.

[95] Taminiau, D., Dautzenberg, J., Bluntness of the tool and process forces in high-

precision cutting, CIRP Annals-Manufacturing Technology, 1991; 40: 65-68.

[96] Singh, V., Ghosh, S., Rao, P. V., Comparative study of specific plowing energy for

mild steel and composite ceramics using single grit scratch tests, Materials and

Manufacturing Processes, 2011; 26: 272-281.

[97] Singh, V., Durgumahanti, U. P., Rao, P. V., Ghosh, S., Specific ploughing energy

model using single grit scratch test, International Journal of Abrasive Technology,

2011; 4: 156-173.

[98] Chae, J., Park, S., Freiheit, T., Investigation of micro-cutting operations,


187

[99] Kim, C. J., Mayor, J. R., Ni, J., A static model of chip formation in microscale

milling, Transactions of the ASME-B-Journal of Manufacturing Science and

Engineering, 2004; 126: 710-718.

[100] Guo, Y., Chou, Y., The determination of ploughing force and its influence on

material properties in metal cutting, Journal of Materials Processing Technology,

2004; 148: 368-375.

[101] Campatelli, G., Lorenzini, L., Scippa, A., Optimization of process parameters using

a Response Surface Method for minimizing power consumption in the milling of

carbon steel, Journal of Cleaner Production, available online 26 October 2013,

ISSN 0959-6526, <http://dx.doi.org/10.1016/j.jclepro.2013.10.025>, 2013.

[102] Woon, K. S., Rahman, M., Fang, F. Z., Neo, K. S., Liu, K., Investigations of tool

edge radius effect in micromachining: A FEM simulation approach, Journal of

Materials Processing Technology, 2008; 195: 204-211.

[103] Nasr, M. N. A., Ng, E. G., Elbestawi, M. A., Modelling the effects of tool-edge

radius on residual stresses when orthogonal cutting AISI 316L, International

Journal of Machine Tools and Manufacture, 2007; 47: 401-411.

[104] Mian, A. J., Driver, N., Mativenga, P. T., Identification of factors that dominate

size effect in micro-machining, International Journal of Machine Tools and

Manufacture, 2011; 51: 383-394.

[105] Schaller, T., Bohn, L., Mayer, J., Schubert, K., Microstructure grooves with a width

of less than 50 μm cut with ground hard metal micro end mills, Precision

Engineering, 1999; 23: 229-235.

[106] Mian, A. J., Driver, N., Mativenga, P. T., A comparative study of material phase

effects on micro-machinability of multiphase materials, The International Journal

of Advanced Manufacturing Technology, 2010; 50: 163-174.

[107] Hopkinson, N., Hague, R., Dickens, P., Introduction to rapid manufacturing, Rapid

Manufacturing: An Industrial Revolution for the Digital Age, John Wiley & Sons

Ltd., (ISBN 13-978-0-470-01613-8), 2006: 1–4.

[108] Levy, G. N., Schindel, R., Kruth, J. P., Rapid manufacturing and rapid tooling with

layer manufacturing (LM) technologies, state of the art and future perspectives,

CIRP Annals-Manufacturing Technology, 2003; 52: 589-609.

188

[109] Holmström, J., Partanen, J., Tuomi, J., Walter, M., Rapid manufacturing in the

spare parts supply chain: alternative approaches to capacity deployment, Journal of

Manufacturing Technology Management, 2010; 21: 687-697.

[110] Kruth, J. P., Leu, M. C., Nakagawa, T., Progress in Additive Manufacturing and

Rapid Prototyping, CIRP Annals - Manufacturing Technology, 1998; 47: 525-540.

[111] Santos, E. C., Shiomi, M., Osakada, K., Laoui, T., Rapid manufacturing of metal

components by laser forming, International Journal of Machine Tools and

Manufacture, 2006; 46: 1459-1468.

[112] Mellor, S., Hao, L., Zhang, D., Additive Manufacturing: A Framework for

Implementation, available online 23 July 2013, ISSN 0925-5273,

<http://dx.doi.org/10.1016/j.ijpe.2013.07.008>, 2013.

[113] Hague, R., Reeves, P., Rapid prototyping, tooling and manufacturing, Rapra

Review Report, vol. 10, No. 9, RAPRA Tech. Ltd., Shrewsbury, UK, 2000.

[114] Lan, H., Ding, Y., Hong, J., Huang, H., Lu, B., A web-based manufacturing service

system for rapid product development, Computers in Industry, 2004; 54: 51-67.

189

APPENDIX A

WORKPIECE MATERIALS PROPERTIES

Stainless Steel

T316L

Aluminium

AW6082-T6 Alloy

AISI 1045 steel

alloy

Titanium 6Al-

4V alloy

Chemical

composition

(Max)

0.03%C, 2%Mn,

0.045%P, 0.03%S,

0.75%Si, 16-

18%Cr, 10-14%Ni,

2-3%Mo, 0.1%N,

Balance Fe

1%Mn, 0.5%Fe,

1.2%Mg, 1.3%Si,

0.1%Cu, 0.2%Zn,

0.1%Ti, 0.25%Cr,

Balance Al.

0.46%C,

0.40%Si,

0.65%Mn,

0.40%Cr, 0.10

Mo, 0.40%Ni,

0.63% Others

89.37%Ti,

6%Al, 4%V,

0.08%C,

0.3%Fe,

0.2%O2,

0.05%N

Hardness

(Vickers)

220 HV 104.5 HV 238.2 HV 353.2

190

APPENDIX B

CUTTING TOOL GEOMETRY OF SOMT 060204 INSERT

Geometry Values

Nose radius (µm) 400

Edge radius (measured) (µm) 60

Positive Rake angle (deg.) 5

Rake face primary chip breaker land (µm) 60

Figure B1: Cutting tool geometry for insert SOMT 060204-HQ (ISCAR LTD,

HELIQUAD, 2013).

191

APPENDIX C

PROCESS WINDOW (RECOMMENDED MACHINING

PARAMETERS) ON SOMT 060204-HQ

192

APPENDIX D

TAGUCHI DESIGN OF EXPERIMENTS FOR MILLING

AISI 1045 STEEL ALLOY

vc

(m/min)

N

(rpm)

Feedrates

(mm/min)

fz

(mm/tooth)

ap

(mm)

ae

(mm)

Q

(mm3/s)

Power

(W)

100 2652 265 0.1 0.5 0.6 1.33 3054.90

100 2652 530 0.2 1 0.8 7.07 3074.07

100 2652 796 0.3 1.5 1 19.89 3090.13

120 3183 318 0.1 1 1 5.30 3113.68

120 3183 637 0.2 1.5 0.6 9.55 3135.28

120 3183 955 0.3 0.5 0.8 6.37 3164.69

150 3978 398 0.1 1.5 0.8 7.96 3101.11

150 3978 796 0.2 0.5 1 6.63 3084.31

150 3978 1194 0.3 1 0.6 11.94 3083.66

193

APPENDIX E

EXPERIMENTAL DESIGN TO ANALYZE SPECIFIC

ENERGY FOR AISI 1045 STEEL ALLOY

EXP_1 1 2 3 4

Vc (m/min) 156 156 156 156

N (rpm) 6206 6206 6206 6206

fz (mm/tooth) 0.010 0.010 0.010 0.010

Feed

(mm/min) 62 62 62 62

ap (mm) 3.50 3.50 3.50 3.50

ae (mm) 0.250 0.500 0.750 1.000

Q (mm3/s) 0.905076 1.810153 2.715229 3.620306

EXP_2 1 2 3 4

Vc (m/min) 156 156 156 156

N (rpm) 6206 6206 6206 6206

fz (mm/tooth) 0.100 0.100 0.100 0.100

Feed

(mm/min) 621 621 621 621

ap (mm) 3.50 3.50 3.50 3.50

Appendix E

194

ae (mm) 0.250 0.500 0.750 1.000

Q (mm3/s) 9.050764 18.10153 27.15229 36.20306

EXP_3 1 2 3 4

Vc (m/min) 156 156 156 156

N (rpm) 6206 6206 6206 6206

fz (mm/tooth) 0.190 0.190 0.190 0.190

Feed

(mm/min) 1179 1179 1179 1179

ap (mm) 3.50 3.50 3.50 3.50

ae (mm) 0.250 0.500 0.750 1.000

MRR (mm3/s) 17.19645 34.3929 51.58935 68.78581

EXP_4 1 2 3 4

Vc (m/min) 156 156 156 156

N (rpm) 6206 6206 6206 6206

fz (mm/tooth) 0.280 0.280 0.280 0.280

Feed

(mm/min) 1738 1738 1738 1738

ap (mm) 3.50 3.50 3.50 3.50

ae (mm) 0.250 0.500 0.750 1.000

Q (mm3/s) 25.34214 50.68428 76.02642 101.3686

EXP_5 1 2 3 4

Appendix E

195

Vc (m/min) 156 156 156 156

N (rpm) 6206 6206 6206 6206

fz (mm/tooth) 0.370 0.370 0.370 0.370

Feed

(mm/min) 2296 2296 2296 2296

ap (mm) 3.50 3.50 3.50 3.50

ae (mm) 0.250 0.500 0.750 1.000

Q (mm3/s) 33.48783 66.97565 100.4635 133.9513

EXP_6 1 2 3 4

Vc (m/min) 156 156 156 156

N (rpm) 6206 6206 6206 6206

fz (mm/tooth) 0.460 0.460 0.460 0.460

Feed

(mm/min) 2855 2855 2855 2855

ap (mm) 3.50 3.50 3.50 3.50

ae (mm) 0.200 0.350 0.500 0.650

Q (mm3/s) 33.30681 58.28692 83.26703 108.2471

EXP_7 1 2 3 4

Vc (m/min) 156 156 156 156

N (rpm) 6206 6206 6206 6206

fz (mm/tooth) 0.550 0.550 0.550 0.550

Appendix E

196

Feed

(mm/min) 3413 3413 3413 3413

ap (mm) 3.50 3.50 3.50 3.50

ae (mm) 0.200 0.400 0.600 0.800

Q (mm3/s) 39.82336 79.64672 119.4701 159.2934

where Vc represent cutting velocity, N is the spindle speed, fz is the chip load, ap is the

depth of cut, ae is the radial width of cut and Q is the material removal rate.

197

APPENDIX F

TOOL PATH VIEW ON DEPOCAM SOFTWARE FOR

TEST PIECE

198

APPENDIX G

ISO NC CODE BLOCKS FOR SURFACE CLEANING

GENERATED ON DEPOCAM SOFTWARE

%

O1234

N1( DEPOCAM v7.0.4 )

N2( 23 November 2012 11:00:44 )

N3( C:\Documents and Settings\RP\Desktop\Vincent_test_piece\AISI104501.ncc )

N4( C:\Documents and Settings\RP\Desktop\Vincent_test_piece\AISI1045.dca )

N5( RP )

N6G21

N7G17G40G80G90

N8( Area Clearance Toolpath 1 [12x6, 0] )

N9( Area Clearance Toolpath )

N10( Thickness in XY is: 0.000 )

N11( Thickness in Z is: 0.000 )

N12( Cutter: 12.000x6.000 )

N13( Note: Cutter tip output )

N14G0G91G28Z0

N15T1M06

N16G0G90G54

N17F10000

N18G0G43Z7.000H1

Appendix G

199

N19G0X50.500Y25.418

N20S1000M03

N21M08

N22G1Z6.205

N23F2000

N24X50.462Z5.815

N25X50.348Z5.440

N26X50.163Z5.094

N27X49.914Z4.791

N28X49.611Z4.542

N29X49.265Z4.357

N30X48.890Z4.243

N31X48.500Z4.205

N32G3X42.890Y17.290Z3.882I0.000J-6.000

N33G3X52.479Y14.927Z3.559I5.610J2.128

N34G3X51.288Y24.730Z3.236I-3.979J4.491

N35G3X42.544Y20.141Z2.913I-2.788J-5.313

N36G3X49.936Y13.592Z2.590I5.956J-0.723

N37G3X53.438Y22.826Z2.267I-1.436J5.826

N38G3X43.562Y22.826Z1.943I-4.938J-3.408

N39G3X47.064Y13.592Z1.620I4.938J-3.408

N40G3X54.456Y20.141Z1.297I1.436J5.826

N41G3X45.712Y24.730Z0.974I-5.956J-0.723

N42G3X44.521Y14.927Z0.651I2.788J-5.313

N43G3X54.110Y17.290Z0.328I3.979J4.491

Appendix G

200

N44G3X48.500Y25.418Z0.005I-5.610J2.128

N45F4000

N46G1X25.000

N47G3X24.582Y25.000I0.000J-0.418

N48G3X25.000Y24.582I0.418J0.000

N49G1X75.000

N50G3X75.418Y25.000I0.000J0.418

N51G3X75.000Y25.418I-0.418J0.000

N52G1X50.000

N53X48.500

N54G2X48.117Y25.596I0.000J0.500

N55G1X47.383Y26.469

N56G3X47.000Y26.647I-0.383J-0.322

N57G1X23.788

N58G3X23.353Y26.212I0.000J-0.435

N59G1Y23.788

N60G3X23.788Y23.353I0.435J0.000

N61G1X76.212

N62G3X76.647Y23.788I0.000J0.435

N63G1Y26.212

N64G3X76.212Y26.647I-0.435J0.000

N65G1X50.000

N66X47.000

N67G2X46.617Y26.825I0.000J0.500

N68G1X45.883Y27.698

Appendix G

201

N69G3X45.500Y27.876I-0.383J-0.322

N70G1X22.559

N71G3X22.124Y27.441I0.000J-0.435

N72G1Y22.559

N73G3X22.559Y22.124I0.435J0.000

N74G1X77.441

N75G3X77.876Y22.559I0.000J0.435

N76G1Y27.441

N77G3X77.441Y27.876I-0.435J0.000

N78G1X50.000

N79X45.500

N80G2X45.118Y28.054I0.000J0.500

N81G1X44.382Y28.927

N82G3X44.000Y29.105I-0.382J-0.322

N83G1X21.330

N84G3X20.895Y28.670I0.000J-0.435

N85G1Y21.330

N86G3X21.330Y20.895I0.435J0.000

N87G1X78.670

N88G3X79.105Y21.330I0.000J0.435

N89G1Y28.670

N90G3X78.670Y29.105I-0.435J0.000

N91G1X50.000

N92X44.000

N93G2X43.617Y29.283I0.000J0.500

Appendix G

202

N94G1X42.883Y30.156

N95G3X42.500Y30.334I-0.383J-0.322

N96G1X20.101

N97G3X19.666Y29.899I0.000J-0.435

N98G1Y20.101

N99G3X20.101Y19.666I0.435J0.000

N100G1X79.899

N101G3X80.334Y20.101I0.000J0.435

N102G1Y29.899

N103G3X79.899Y30.334I-0.435J0.000

N104G1X50.000

N105X42.500

N106G2X42.117Y30.512I0.000J0.500

N107G1X41.383Y31.385

N108G3X41.000Y31.563I-0.383J-0.322

N109G1X18.871

N110G3X18.437Y31.129I0.000J-0.435

N111G1Y18.871

N112G3X18.871Y18.437I0.435J0.000

N113G1X81.129

N114G3X81.563Y18.871I0.000J0.435

N115G1Y31.129

N116G3X81.129Y31.563I-0.435J0.000

N117G1X50.000

N118X41.000

Appendix G

203

N119G2X40.618Y31.741I0.000J0.500

N120G1X39.882Y32.614

N121G3X39.500Y32.792I-0.382J-0.322

N122G1X17.642

N123G3X17.208Y32.358I0.000J-0.435

N124G1Y17.642

N125G3X17.642Y17.208I0.435J0.000

N126G1X82.358

N127G3X82.792Y17.642I0.000J0.435

N128G1Y32.358

N129G3X82.358Y32.792I-0.435J0.000

N130G1X50.000

N131X39.500

N132G2X39.117Y32.970I0.000J0.500

N133G1X38.383Y33.843

N134G3X38.000Y34.021I-0.383J-0.322

N135G1X16.413

N136G3X15.979Y33.587I0.000J-0.435

N137G1Y16.413

N138G3X16.413Y15.979I0.435J0.000

N139G1X83.587

N140G3X84.021Y16.413I0.000J0.435

N141G1Y33.587

N142G3X83.587Y34.021I-0.435J0.000

N143G1X50.000

Appendix G

204

N144X38.000

N145G2X37.617Y34.199I0.000J0.500

N146G1X36.883Y35.072

N147G3X36.500Y35.250I-0.383J-0.322

N148G1X15.184

N149G3X14.750Y34.816I0.000J-0.435

N150G1Y15.184

N151G3X15.184Y14.750I0.435J0.000

N152G1X84.816

N153G3X85.250Y15.184I0.000J0.435

N154G1Y34.816

N155G3X84.816Y35.250I-0.435J0.000

N156G1X50.000

N157X36.500

N158G2X36.117Y35.428I0.000J0.500

N159G1X35.383Y36.301

N160G3X35.000Y36.479I-0.383J-0.322

N161G1X13.955

N162G3X13.521Y36.045I0.000J-0.435

N163G1Y13.955

N164G3X13.955Y13.521I0.435J0.000

N165G1X86.045

N166G3X86.479Y13.955I0.000J0.435

N167G1Y36.045

N168G3X86.045Y36.479I-0.435J0.000

Appendix G

205

N169G1X50.000

N170X35.000

N171G2X34.617Y36.658I0.000J0.500

N172G1X33.883Y37.531

N173G3X33.500Y37.709I-0.383J-0.322

N174G1X12.726

N175G3X12.291Y37.274I0.000J-0.435

N176G1Y12.726

N177G3X12.726Y12.291I0.435J0.000

N178G1X87.274

N179G3X87.709Y12.726I0.000J0.435

N180G1Y37.274

N181G3X87.274Y37.709I-0.435J0.000

N182G1X50.000

N183X33.500

N184G2X33.117Y37.887I0.000J0.500

N185G1X32.383Y38.760

N186G3X32.000Y38.938I-0.383J-0.322

N187G1X11.497

N188G3X11.062Y38.503I0.000J-0.435

N189G1Y11.497

N190G3X11.497Y11.062I0.435J0.000

N191G1X88.503

N192G3X88.938Y11.497I0.000J0.435

N193G1Y38.503

Appendix G

206

N194G3X88.503Y38.938I-0.435J0.000

N195G1X50.000

N196X32.000

N197G2X31.617Y39.116I0.000J0.500

N198G1X30.883Y39.989

N199G3X30.500Y40.167I-0.383J-0.322

N200G1X10.268

N201G3X9.833Y39.732I0.000J-0.435

N202G1Y10.268

N203G3X10.268Y9.833I0.435J0.000

N204G1X89.732

N205G3X90.167Y10.268I0.000J0.435

N206G1Y39.732

N207G3X89.732Y40.167I-0.435J0.000

N208G1X50.000

N209X30.500

N210G2X30.117Y40.345I0.000J0.500

N211G1X29.383Y41.218

N212G3X29.000Y41.396I-0.383J-0.322

N213G1X9.038

N214G3X8.604Y40.962I0.000J-0.435

N215G1Y9.038

N216G3X9.038Y8.604I0.435J0.000

N217G1X90.962

N218G3X91.396Y9.038I0.000J0.435

Appendix G

207

N219G1Y40.962

N220G3X90.962Y41.396I-0.435J0.000

N221G1X50.000

N222X29.000

N223G2X28.617Y41.574I0.000J0.500

N224G1X27.883Y42.447

N225G3X27.500Y42.625I-0.383J-0.322

N226G1X7.809

N227G3X7.375Y42.191I0.000J-0.435

N228G1Y7.809

N229G3X7.809Y7.375I0.435J0.000

N230G1X92.191

N231G3X92.625Y7.809I0.000J0.435

N232G1Y42.191

N233G3X92.191Y42.625I-0.435J0.000

N234G1X50.000

N235X27.500

N236G2X27.118Y42.803I0.000J0.500

N237G1X26.382Y43.676

N238G3X26.000Y43.854I-0.382J-0.322

N239G1X6.580

N240G3X6.146Y43.420I0.000J-0.435

N241G1Y6.580

N242G3X6.580Y6.146I0.435J0.000

N243G1X93.420

Appendix G

208

N244G3X93.854Y6.580I0.000J0.435

N245G1Y43.420

N246G3X93.420Y43.854I-0.435J0.000

N247G1X50.000

N248X26.000

N249G2X25.618Y44.032I0.000J0.500

N250G1X24.882Y44.905

N251G3X24.500Y45.083I-0.382J-0.322

N252G1X5.351

N253G3X4.917Y44.649I0.000J-0.435

N254G1Y5.351

N255G3X5.351Y4.917I0.435J0.000

N256G1X94.649

N257G3X95.083Y5.351I0.000J0.435

N258G1Y44.649

N259G3X94.649Y45.083I-0.435J0.000

N260G1X50.000

N261X24.500

N262G2X24.117Y45.261I0.000J0.500

N263G1X23.383Y46.134

N264G3X23.000Y46.312I-0.383J-0.322

N265G1X4.122

N266G3X3.688Y45.878I0.000J-0.435

N267G1Y4.122

N268G3X4.122Y3.688I0.435J0.000

Appendix G

209

N269G1X95.878

N270G3X96.312Y4.122I0.000J0.435

N271G1Y45.878

N272G3X95.878Y46.312I-0.435J0.000

N273G1X50.000

N274X23.000

N275G2X22.617Y46.491I0.000J0.500

N276G1X21.883Y47.364

N277G3X21.500Y47.542I-0.383J-0.322

N278G1X2.893

N279G3X2.458Y47.107I0.000J-0.435

N280G1Y2.893

N281G3X2.893Y2.458I0.435J0.000

N282G1X97.107

N283G3X97.542Y2.893I0.000J0.435

N284G1Y47.107

N285G3X97.107Y47.542I-0.435J0.000

N286G1X50.000

N287X21.500

N288G2X21.117Y47.720I0.000J0.500

N289G1X20.383Y48.593

N290G3X20.000Y48.771I-0.383J-0.322

N291G1X1.664

N292G3X1.229Y48.336I0.000J-0.435

N293G1Y1.664

Appendix G

210

N294G3X1.664Y1.229I0.435J0.000

N295G1X98.336

N296G3X98.771Y1.664I0.000J0.435

N297G1Y48.336

N298G3X98.336Y48.771I-0.435J0.000

N299G1X50.000

N300X20.000

N301G2X19.617Y48.949I0.000J0.500

N302G1X18.883Y49.822

N303G3X18.500Y50.000I-0.383J-0.322

N304G1X0.534

N305G3X0.000Y49.466I0.000J-0.534

N306G1Y0.534

N307G3X0.534Y0.000I0.534J0.000

N308G1X50.000

N309X99.466

N310G3X100.000Y0.534I0.000J0.534

N311G1Y49.466

N312G3X99.466Y50.000I-0.534J0.000

N313G1X50.000

N314X18.500

N315F6000

N316X18.110Z0.043

N317X17.735Z0.157

N318X17.389Z0.342

Appendix G

211

N319X17.086Z0.591

N320X16.837Z0.894

N321X16.652Z1.240

N322X16.538Z1.615

N323X16.500Z2.005

N324F10000

N325Z7.000

N326M09

N327M05

N328M30

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