AN EXPERIMENTAL INVESTIGATION AND MODELLING OF THE

ELECTRICAL DISCHARGE MACHINING PERFORMANCE

ON TITANIUM ALLOY Ti-5Al-2.5Sn

MOHAMMAD ASHIKUR RAHMAN KHAN

Thesis submitted in fulfillment of the requirements

for the award of the degree of

Doctor of Philosophy in Mechanical Engineering

Faculty of Mechanical Engineering

UNIVERSITI MALAYSIA PAHANG

AUGUST 2012

vii

ABSTRACT

This thesis deals with an experimental investigation and modelling of the electrical

discharge machining (EDM) performance on titanium alloy Ti-5Al-2.5Sn. Despite

enormous applications of lightweight and high-strength titanium alloy, a key problem in

machining using conventional machining processes arises. The non-conventional

technique, EDM, can machine difficult-to-cut materials effectively. However, in EDM,

a complete and clear theory has not yet been established. The proper selection of EDM

parameters for the best process performance is still a challenge. Thus, the purpose of the

present work is to develop the mathematical models to predict performance

characteristics (material removal rate, tool wear rate and surface roughness) along with

the optimal parametric set-up of EDM on Ti-5Al-2.5Sn titanium alloy. The peak

current, pulse-on time, pulse-off time, servo-voltage, polarity (positive and negative),

and electrode material (copper, copper-tungsten and graphite) are considered as process

variables. The experimental work was performed based on an experiment design

(central composite design). The mathematical models, using the response surface

method, and the artificial neural network (ANN) model, using the multilayer perception

method, were developed. Analysis of variance (ANOVA) has been performed to verify

the fit and adequacy of the developed mathematical models. A confirmation test was

conducted to obtain the accuracy of the developed models. In addition, the surface

topography of the workpiece was analysed by scanning electron microscopy (SEM).

The results evidence that the developed mathematical model can predict the

performance characteristics of EDM successfully. The average errors of the

mathematical model in predicting material removal rate, surface roughness and tool

wear rate were 4.34%, 4.17% and 4.50% respectively. While, the average errors were

2.61%, 2.77% and 3.05% for the ANN model. Thus, the ANN model is more precise

than the mathematical model. The negative graphite electrode provides the highest

material removal rate. However, it maximizes the tool wear rate, and causes the poorest

surface finish. The positive copper-tungsten electrode becomes the best choice in

respect of all performance characteristics. It was very difficult to achieve single settings

of the process parameters for all the best performance characteristics. In addition, the

multiple objectives were incompatible. The surface topography for negative polarity

demonstrates larger craters, wider and deeper cracks and greater amounts of globules

when compared to positive polarity. The obtained results lead to desirable process

output, and cost-effective machining. Therefore, it becomes a precise tool, making the

EDM process cost-effective and efficient in the die, mould, tool and other industries.

viii

ABSTRAK

Tesis ini berkaitan dengan penyiasatan eksperimen dan permodelan prestasi pemesinan

pelepasan elektrik (EDM) pada aloi Ti-5Al-2.5Sn titanium. Walaupun aplikasi yang

meluas dalam penggunaan titanium, tetapi masalah utama dalam pemesinan

menggunakan proses pemesinan konvensional wujud. Teknik yang bukan tradisional,

EDM, boleh mesin aloi yang sukar dimesin.Walau bagaimanapun, EDM, teori yang

lengkap dan jelas masih belum ada. Pemilihan parameter EDM untuk proses prestasi

terbaik merupakan satu cabaran. Oleh itu, tujuan kerja ini adalah untuk membangunkan

model-model matematik untuk meramalkan ciri-ciri prestasi (kadar penyinkiran bahan,

kadar kehausan alat dan kekasaran permukaan) bersama-sama dengan set up optimum

parametrik-EDM pada aloi Ti-5Al-2.5Sn titanium. Electrik memuncak, nadi pada masa

yang ditetapkan, bahan-denyut masa, servo-voltan, kekutuban (positif dan negatif), dan

elektrod (tembaga, tembaga tungsten dan grafit) dianggap sebagai pembolehubah

proses. Kerja-kerja eksperimen telah dilakukan berdasarkan reka bentuk eksperimen

(reka bentuk komposit berpusat). Model matematik, menggunakan kaedah respon

permukaan, dan model rangkaian neural tiruan (ANN), menggunakan kaedah persepsi

berbilang, telah digunakan. Analisis varians (ANOVA) telah dijalankan untuk

mengesahkan kesesuaian dan kecukupan model matematik yang dibangunkan. Satu

ujian pengesahan telah dijalankan untuk mendapatkan ketepatan model yang

dibangunkan. Di samping itu, topografi permukaan bahan kerja telah dianalisis dengan

menggunakan mikroskopi pengimbasan elektron (SEM). Keputusan terbukti bahawa

model matematik yang maju boleh meramal ciri-ciri prestasi daripada EDM berjaya.

Kesilapan-kesilapan purata model matematik dalam meramalkan kadar penyinkiran

bahan, kekasaran permukaan dan kadar kehausan alat 4.34%, 4.17% dan 4.50% masing-

masing.Sementara itu, kesilapan purata adalah 2.61%, 2.77% dan 3.05% untuk model

ANN. Oleh itu, model ANN adalah lebih tepat daripada model matematik. Elektrod

grafit negatif menyediakan kadar penyinkiran bahan yang tertinggi. Walau

bagaimanapun, ia memaksimumkan kadar kehausan alat, dan menyebabkan permukaan

kemasan tidak memuaskan. Elektrod tungsten tembaga positif menjadi pilihan terbaik

berkenaan dengan semua ciri prestasi. Ia adalah sangat sukar untuk mencapai tetapan

tunggal parameter proses bagi semua ciri-ciri prestasi terbaik. Di samping itu juga,

objektif berganda adalah tidak sesuai. Topografi permukaan untuk polariti negatif

menunjukkan kawah yang lebih besar, retak yang lebih luas dan lebih mendalam dan

lebih banyak daripada dua lapisan apabila dibandingkan dengan kekutuban positif.

Keputusan yang diperolehi membawa kepada output proses wajar, dan pemesinan kos

efektif. Oleh itu, ia menjadi satu alat yang tepat, menjadikan proses EDM kos lebih

efektif dan cekap dalam industri acuan, alatan dan lain-lain.

ix

TABLE OF CONTENTS

Page

THESIS CONFIDENTIAL STATUS i

TITLE PAGE ii

SUPERVISOR’S DECLARATION iii

STUDENT’S DECLARATION iv

DEDICATION v

ACKNOWLEDGEMENTS vi

ABSTRACT vii

ABSTRAK viii

TABLE OF CONTENTS ix

LIST OF TABLES xiii

LIST OF FIGURES xvi

LIST OF SYMBOLS xviii

LIST OF ABBREVIATIONS xxi

CHAPTER 1 INTRODUCTION

1.1 Background 1

1.2 Motivation of the Study 3

1.3 Research Objectives 5

1.4 Scope of the Study 6

1.5 Organization of Thesis 7

CHAPTER 2 LITERATURE SURVEY

2.1 Introduction 8

2.2 Titanium and Titanium Alloys 9

2.2.1 Background 9

2.2.2 Classification and Applications 10

2.2.3 Applications 13

2.2.4 Machining 14

x

2.3 Electrical Discharge Machining 16

2.3.1 Introduction 16

2.3.2 Applications of EDM 21

2.4 EDM Parameters 22

2.5 EDM Performance Characteristics 24

2.5.1 Material Removal Rate 25

2.5.2 Electrode Wear Rate 26

2.5.3 Surface Roughness 28

2.6 Tool Materials on EDM Characteristics 29

2.7 Performance modelling 32

2.7.1 Theoretical Modelling 32

2.7.2 Dimensional Analysis 37

2.7.3 Regression Modelling 37

2.7.4 Artificial Neural Network Modelling 40

2.8 Previous Studies of EDM on Titanium Alloys 42

2.9 Summary 43

CHAPTER 3 RESEARCH METHODOLOGY

3.1 Introduction 45

3.2 Materials 45

3.2.1 Workpiece Materials 46

3.2.2 Electrode Materials 47

3.3 Machining Parameters 48

3.3.1 Performance Characteristics 48

3.3.2 Process Parameters 53

3.4 Experimental Details 56

3.4.1 Parameter Selection 56

3.4.2 Design of Experiments 58

3.4.3 Workpiece Preparation 63

3.4.4 Experimental Set-up 64

3.4.5 Surface Topography 68

3.5 Mathematical Modelling 69

3.5.1 Model Development 70

3.5.2 First-order Model 71

3.5.3 Second-order Model 73

3.5.4 Analysis of Variance 75

3.6 Artificial Neural Network Modelling 77

1 × ENTER (1.5 line spacing)

xi

3.6.1 Artificial Neural Network 77

3.6.2 Model Development 78

3.6.3 Neural Network Model with One Hidden Layer 80

3.6.4 Neural Network Model with Two Hidden Layers 85

3.6.5 Training and Testing 88

3.7 Confirmation Test 91

3.8 Summary 92

CHAPTER 4 RESULTS AND DISCUSSION

4.1 Introduction 93

4.2 Mathematical Model 94

4.2.1 Copper Electrode and Positive Polarity 95

4.2.2 Copper Electrode and Negative Polarity 100

4.2.3 Copper-tungsten and Graphite Electrode 106

4.2.4 Accuracy of the Model 112

4.3 Neural Network Model 114

4.3.1 Configuration 114

4.3.2 Training 117

4.3.3 Testing 119

4.4 Model Validation 124

4.5 Impact of the Parameters on Responses 127

4.5.1 Sensitivity Analysis 127

4.5.2 Effect of Electrical Parameters 130

4.5.3 Electrode and Polarity 137

4.6 Optimal Settings of the Parameters 150

4.7 Surface Topography 152

4.7.1 Cooper Electrode 152

4.7.2 Cooper-Tungsten Electrode 158

4.7.3 Graphite Electrode 161

4.7.4 Effect of Electrode 163

4.8 Summary 167

CHAPTER 5 CONCLUSION AND RECOMMENDATIONS

5.1 Introduction 169

5.2 Conclusions 169

5.2.1 Mathematical Model 170

xii

5.2.2 Optimization 171

5.2.3 Performance Characteristics 172

5.2.4 Characteristics of the Surface Topography 175

5.3 Recommendations for Future Research 176

REFERENCES 177

APPENDICES

A1 Experimental data obtained with positive copper electrode 196

A2 Experimental data obtained with negative copper electrode 197

A3 Experimental data obtained with positive copper-tungsten

electrode

198

A4 Experimental data obtained with negative copper-tungsten

electrode

199

A5 Experimental data obtained with positive graphite electrode 200

A6 Experimental data obtained with negative graphite electrode 201

B List of publications 202

xiii

LIST OF TABLES

Table No. Title Page

2.1 Basic physical properties of pure titanium 10

2.2 Studies in EDM on Ti-6Al-4V 43

3.1 Chemical composition of titanium alloy Ti-5-2.5 46

3.2 Physical properties of titanium alloy Ti-5-2.5 46

3.3 Chemical composition of copper electrode 47

3.4 Chemical compositions of copper-tungsten electrode 48

3.5 Physical properties of copper, copper-tungsten and graphite

electrodes

48

3.6 The process parameters and their ranges 57

3.7 Factors considered in previous studies of EDM on Ti-6-4 alloy 57

3.8 The levels of machining parameters 60

3.9 Set of designed experiments for different parameters as coded

units

61

3.10 Set of designed experiments for different parameters as uncoded

units

62

3.11 Weight of absorbed kerosene, heating temperature, and drying

time for graphite

64

3.12 Experimental settings 67

3.13 Combinations of workpiece and tool with respect to polarity 67

3.14 Set of designed experiments for SEM viewing 69

3.15 Set of designed experiments for neural network testing purposes 91

3.16 Machining conditions for confirmation test 92

4.1 Coefficients of material removal rate model with positive Cu

electrode

96

4.2 ANOVA for model of material removal rate with positive Cu

electrode

97

xiv

4.3 Coefficients of surface roughness model with positive Cu

electrode

98

4.4 ANOVA for model of surface roughness with positive Cu

electrode

99

4.5 Coefficients of tool wear rate model with positive Cu electrode 100

4.6 ANOVA for model of tool wear rate with positive Cu electrode 101

4.7 Coefficients of material removal rate model with negative Cu

electrode

102

4.8 ANOVA for material removal rate model with negative Cu

electrode

102

4.9 Coefficients of surface roughness model with negative Cu

electrode

103

4.10 ANOVA for model of surface roughness with negative Cu

electrode

104

4.11 Coefficients of tool wear rate model with negative Cu electrode 105

4.12 ANOVA for model of tool wear rate with negative Cu electrode 106

4.13 ANOVA for model of material removal rate with Cu-W and Gr 108

4.14 ANOVA for model of surface roughness with Cu-W and Gr 109

4.15 ANOVA for second-order model of SR with positive Cu-W

electrode (After elimination of some terms)

110

4.16 ANOVA for model of tool wear rate with Cu-W and Gr 112

4.17 Error of the developed mathematical models of material removal

rate

113

4.18 Error of the developed mathematical models of surface

roughness

113

4.19 Error of the developed mathematical models of tool wear rate 114

4.20 Performance of neural network with different architectures 115

4.21 Best configurations of neural network models 116

4.22 Error analysis for the neural network models in training 119

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4.23 Best network and MSE 119

4.24 The values of MSE and linear correlation coefficient during

testing

123

4.25 Error analysis for the RSM and NN models of material removal

rate

125

4.26 Error analysis for the RSM and NN models of surface roughness 125

4.27 Error analysis for the RSM and NN models of tool wear rate 126

4.28 Sensitivity analysis values for EDM performance characteristics 129

4.29 Optimum settings of the machining parameters for MRR 151

4.30 Optimum settings of the machining parameters for SR 151

4.31 Optimum settings of the machining parameters for TWR 152

xvi

LIST OF FIGURES

Figure No. Title Page

2.1 Titanium crystal structures: (a) Hexagonal close-packed (hcp)

structure of α-titanium; (b) Body-centred cubic (bcc) structure of

β-titanium

11

2.2 Phase diagram for titanium alloys 12

3.1 Measured and nominal surface profiles 51

3.2 CLA method of surface roughness measurement 51

3.3 Rz and Rmax surface roughness measurement 52

3.4 EDM pulse current, pulse-on time and pulse-off time 53

3.5 Central composite design 60

3.6 Facing operation on an electrode’s end faces in an engine lathe 63

3.7 Experimental setup of electrical discharge machining 66

3.8 Weight measurement of workpiece and electrode 68

3.9 Surface roughness (Ra) measurement using Perthometer S2 68

3.10 A multiple input neuron model 79

3.11 Multilayer perceptron neural network architecture with one hidden

layer

81

3.12 Sigmoid function 82

3.13 Multilayer perceptron neural network model architecture with two

hidden layers

86

4.1 Convergence of training error (MSE) for copper electrode 118

4.2 Comparison between predicted and experimental value for positive

copper

120

4.3 Comparison between predicted and experimental value for negative

copper

121

4.4 Predicted versus experimental output for positive copper electrode

(a, c, and e) and for negative copper electrode (b, d, and f)

122

xvii

4.5 Sensitivity analysis for EDM performance with copper electrode 128

4.6 EDM outputs for varied input peak current 131

4.7 EDM outputs for varied pulse-on time 133

4.8 EDM outputs for varied pulse-off time 135

4.9 EDM outputs for varied servo-voltage 137

4.10 MRR for different electrodes (Cu, Cu-W, and Gr) and polarity 139

4.11 SR for different electrodes (Cu, Cu-W, and Gr) and polarity 142

4.12 TWR for different electrodes (Cu, Cu-W, and Gr) and polarity 146

4.13 EDM performance against various machining parameters for

different polarities of graphite electrode

149

4.14 SEM micrographs of the machined surface with Cu electrode and

positive and negative polarity for different discharge energies

156

4.15 SEM micrographs of the machined surface with Cu-W electrode

and positive and negative polarity for different discharge energy

159

4.16 SEM micrographs of the machined surface with Gr electrode and

positive and negative polarity for different discharge energy

162

4.17 SEM micrographs of the machined surface with positive polarity

of different electrodes for altered discharge energy

165

4.18 SEM micrographs of the machined surface with negative polarity

of different electrodes for altered discharge energy

166

xviii

LIST OF SYMBOLS

A0 Constant

Ai, Aii, Aij Regression coefficient

b Bias

Bi, Bii, Bij Regression coefficient

bj Bias on the first hidden layer

bk Bias on the second hidden layer

bo Bias on the output layer

B0 Constant

β0, βi, βij, βii Regression coefficient

C Capacitance

eC Cohesive energy

Ci, Cii, Cij Regression coefficient

sC Specific heat

C0 Constant

ε Experimental error

f Function

hj Output for first hidden layer

I Average current

Ip Peak current

j Nodes in the first hidden layer

k Thermal conductivity

lm Total measuring length

xix

M Molecular weight

P Resistance to erosion

Density

ρe Density of the electrode material

ρw Density of the workpiece material

Ra Roughness average

Rq Roughness quadratic average

Rz Average peak to valley height

Rmax Maximum profile valley depth

R2 Coefficient of determination

S Standard error of the regression

t Machining time

T Steady temperature of remote points

Tb Boiling temperature

Tf Heating temperature of the graphite inside the furnace

th Time of heating the graphite inside the furnace

Tm Melting point of the metal

tT Target value

Sv Servo-voltage

Toff Pulse-off time

Ton Pulse-on time

ts Time of submersion in kerosene

U Applied voltage

V Volume of metal removed

xx

We Weight loss of the electrode

WGN Weight of standard graphite before submersion in kerosene

WGD Weight of the dry graphite after heating

WGW Weight of wet graphite before heating in the furnace

wi Synaptic weight

wi,j Weight between the input neurons and first hidden neurons

wj,k Weight between first hidden neurons and second hidden neurons

wj,o Weight between the hidden and output neurons

WK Weight of absorbed kerosene by graphite

wk,o Weight between the last hidden neurons and output neurons

Wm Weight of moisture in the graphite

Ww Amount of metal removed from the workpiece

xmax Maximum value of the real variable

xmin Minimum value of the real variable

xN Normalized value of the real variable

Y Response

yo ANN predicted output

Zi Single roughness depths

zk Output for the second hidden layer

Zmax Maximum profile depth

xxi

LIST OF ABBREVIATIONS

AA Arithmetic average

ANFIS Adaptive-network-based fuzzy inference system

ANN Artificial neural network

ANOVA Analysis of variance

bcc Body-centred cubic

CCD Central composite design

CBN Cubic boron nitride

CLA Centre line average

CNC Computer numerical controlled

DF Degree of freedom

DOE Design of experiment

E Error measure

EDM Electrical discharge machining

EW Electrode wear

FEM Finite element method

GA Genetic algorithm

hcp Hexagonal close-packed

ITER International Thermonuclear Experimental Reactor

LOGMLP Logistic sigmoid multilayer perceptron

MAE Mean absolute error

MAPE Mean absolute percentage error

MMC Metal matrix composite

MLP Multilayer perceptron

xxii

MRR Material removal rate

MS Mean square

MSE Mean square error

PCD Polycrystalline diamond

PRESS Prediction sum of squares

RBFN Radial basis functions network

RMSE Root mean square error

R2 Coefficient of determination

RSM Response surface methodology

R&D Research and development

S Standard error of the regression

SEM Scanning electron microscopy

SR Surface roughness

SS Sum of squares

SSE Sum of squares of residuals

SSR Regression sum of squares

TWR Tool wear rate

TANMLP Hyperbolic tangent sigmoid multilayer perceptron

CHAPTER 1

INTRODUCTION

1.1 BACKGROUND

Titanium alloys are comprehensively used in many industries and commercial

applications, including those concerning aerospace, nuclear energy and biomedical

devices and components due to their high strength-weight ratio and excellent corrosion

resistance. In spite of the increased utility of titanium alloys, the capability to produce

parts products with high productivity and superior quality becomes challenging. It is

extremely difficult to machine titanium alloys with the traditional machining process

owing to their poor machinability (Rahman et al., 2006). Titanium is difficult-to-cut

material can be machined effectively by a non-conventional technique, that of electrical

discharge machining (EDM) (Rahman et al., 2006; Yan et al., 2005). However, it seems

that a large number of parameters significantly affect the EDM performance. On the

other hand, complicated mechanisms to the process result in a lag of established theory

(Su et al., 2004). It is exceedingly hard to set up a mathematical formula correlating the

input and output parameters, even though it is feasible. The results are far away from the

reality because of inevitable assumptions and simplified approaches (Wang and Tsai,

2001).

Electrical discharge machining applies successive discharges in a liquid

dielectric to perform machining on the workpiece surface. For more than fifty years, this

has been a well-known machining technique. Nowadays, EDM is a most widely used

2

non-conventional process and has become an essential machining technique in

manufacturing industries such as aerospace, automotive, nuclear, medical, mold and die

industries (Kiyak and Cakir, 2007). This process is non-conventional and does not apply

the traditional tools for metal removal, instead directly using electro-thermal energy. In

this process, the material is removed electro thermally by a series of successive discrete

discharges between electrode and the workpiece (Rajurkar, 1994). This is not restricted

by the physical and mechanical properties of work material and is able to machine any

electrical conductive material, regardless of how hard it is. The materials that are

difficult-to-cut and which introduce considerable challenges in machining operation,

such as high-strength alloys, high-temperature resistant alloys and hardened alloy steel,

can be machined straightforwardly by EDM (Assarzadeh and Ghoreishi, 2008).

In EDM, parameter selection is subject to experience and is time consuming.

Often, it is accomplished on a trial basis which incorporates the necessity of

supplementary materials and repetition of work. Many experimental runs are not

economically justified. In the competitive world of modern manufacturing, it is essential

to save time and to avoid the costs of scrap and reworking in combination with

competitive price (Higuerey, 1998). The selection of appropriate machining conditions

is also carried out by conservative technological data provided by the EDM equipment

manufacturers, which produced inconsistent machining performance (Rao et al., 2009).

In addition, the parameter settings provided by the manufacturers are only applicable for

the common steel grades. The settings for new materials such as titanium alloys,

aluminium alloys, special steels, advanced ceramics, and metal matrix composites

(MMCs) have to be further optimized experimentally.

The problem appeared owing to selection of parameters increases the process

deficiency. Ultimately, the use of electrical discharge machining process becomes

restriction. Hence, it becomes crucial to select an appropriate parameter set at different

machining stages in order to promote efficiency, enhance machining productivity, and

process reliability. Proper combination of the processing parameters can result in

precise and cost-effective machining by minimizing electrode wear and maximizing the

material removal.

3

Modelling the process is an effective way of solving the critical problem of

relating the process parameters to the performance measures. In EDM, modelling can

facilitate the acquisition of a better understanding of a complex process, save the

machining time and make the process economic (Karthikeyan et al., 1999). Although

prior researchers tried to develop a statistical model, it is difficult to develop the

universal mathematical model for predicting the performance measures (Garg et al.,

2010). Hence, it is impossible to use existing models when new and advanced material

has appeared in this field. From this perspective, the development of an efficient

approach has been attempted in order to correlate the EDM machining parameters as

peak current, pulse-on time, pulse-off time, polarity of electrode, servo-voltage and

certain responses. These responses are material removal rate (MRR), surface roughness

(SR) and tool wear rate (TWR). Moreover, determining appropriate machining

parameters that yield optimal output are also an aim. First, the discharge machining on a

titanium alloy is performed in accordance with the design of an experiment obtained

through statistical approach. Henceforth, the response surface methodology (RSM) and

artificial neural network (ANN) approaches are implemented throughout the

experimental data. A back-propagation, multilayer perceptron neural network has been

adopted in this study. The surface topography of the machined surface has been

analysed through scanning electron microscope (SEM).

1.2 MOTIVATION OF THE STUDY

The electrical discharge machining process comprises of the simultaneous

interaction of the thermal, mechanical, chemical and electrical phenomena (Izquierdo et

al., 2009). The growth of the plasma channel, the process of melting and vaporization,

re-solidification and formation of recast layer, the plasma-flushing efficiency, and the

temperature sensitivity of thermal properties of the work and electrode material, all

consolidate the machining process as highly complex and stochastic. The complicated

machining phenomenon, coupled with the interaction between two successive

discharges and the presence of debris particles within the machining gap, made the

process too complex. The complexity and non-linear nature of electrical discharge

machining are serious problems (Mahdavinejad, 2009). EDM process involves a

number of machining parameters, including electrical and non-electrical. A single

4

parameter change influences the process in a complex way. It is exceedingly difficult to

describe mathematically all these complex phenomena consequently, since a clear and

complete theory for this process has not been established yet (Zhao et al., 2004; Panda

and Bhoi, 2005; Sharakhovsky et al., 2006; Rao et al., 2008). The high scattering of the

data published recognises that research work is still needed for a better understanding of

the nature of the phenomena associated with EDM process (Izquierdo et al., 2009). In

EDM, the abnormal discharges like arcing and short-circuiting occur frequently (Pecas

and Henriques, 2003). The ranges of the input parameters and their combination are

supposed to be predictable in order to attain machining stability that avoids such

circumstances.

Selection of appropriate parameters is costly and time consuming. One of the

most significant deficiencies of the EDM process is the high manufacturing time. In this

instance, inadequate selection of machining conditions may introduce high process

times. Typically, this is carried out by relying heavily on the operator’s experience and

conservative technological data provided by the EDM equipment manufacturers, which

produce inconsistent machining performance (Rao et al., 2009). The parameter settings

given by the manufacturers are only applicable to the common steel grades. The choice

of the electrical parameters on the EDM process depends impressively on workpiece-

electrode material combination. On the other hand, EDM manufacturers only supply

these parameters for a limited amount of material combination (Dewangan, 2010).

Proper selection of parameters in EDM is very much essential for achieving better

performance characteristics, and that is still a challenging job. Commonly high material

removal is desired; however, the surface finish of the machined part deteriorates and

often causes more tool-wears. A single set of the input variable cannot optimize

multiple objectives as material removal, tool-wear, as well as surface finish (Marafona

and Wykes, 2000). However, the proper combination of input parameters can aid to

achieve higher MRR with reasonable tool wear maintaining the suitable surface finish

(Mahdavinejad, 2009). Thus, the operator can realise the correct combination of input

parameters.

Numerous researchers have developed the model to predict EDM responses

using different techniques, such as response surface methodology, neural network and

5

so forth. However, the model is developed for the particular material; it cannot be used

for another material. It is proven that materials significantly influence the EDM

performance (Wang and Tsai, 2001). It is observed from previous studies that the

developed models are for silicon infiltrated silicon carbide (SiSiC), SiC/Al, AISI EK2

steel, AISI D2 steel, AISI H13 steel, soft steel (F-1110), copper–steel (EN-8), alumina-

based ceramic composite (Al2O3 / SiCw / TiC), DIN 1.2714 hot work tool steel, Ck60

steel plates, tungsten carbide and cobalt composites (70%WC/30%Co), AISI D2 tool

steel, Al2O3 + TiC mixed ceramic, FW4 steel, SKD11, HE15, 15CDV6, M250, mild

steel (St 37), alloyed steels (C45 and 100Cr6), high strength low alloyed (HSLA) steels

such as a micro-alloyed (Mic/Al 1) steel and dual-phase (DP1) steel, BD3 steel, C40

steel, beryllium-copper alloy, mold-aluminium, mold-steel, and so on. It is obvious that

the models are only valid for the selected work and tool materials with specific polarity.

The development of the model in support of Ti-5Al-2.5Sn work material in EDM

process is still lagging. In this context, an effort has been made to study the performance

characteristics and to correlate them with the process parameters in EDM on Ti-5Al-

2.5Sn titanium alloy.

1.3 RESEARCH OBJECTIVES

In order to achieve the good understanding of EDM process, to diminish the

process weakness and enhance the feasibility of its application, specifically for titanium

alloy the objectives of the present work are as follows:

i. To investigate the effect of machining parameters on the performance

characteristics in terms of material removal rate, surface roughness and tool

wear rate of EDM of titanium alloy Ti-5Al-2.5Sn.

ii. To develop a regression equation and the artificial neural network model for

predicting EDM performance characteristics.

iii. To determine the optimal parametric settings that furnish the best machining

performance as higher material removal, superior surface finish and lowest

tool wear rate at distinct circumstances.

6

iv. To study the surface topography of the machined surface under different

machining conditions by performing scanning electron microscope analysis.

1.4 SCOPE OF THE STUDY

The experimental work is carried out by using an AQ55L model Sodick Die-

sinking EDM, and commercial kerosene is utilized as dielectric fluid. The voltage and

flushing pressures are maintained as 120 V and 0.15 MPa respectively. In the present

study, the electrical discharge machining is conducted for a limited period of 40

minutes. A type-KM225/13 furnace with a maximum temperature of 1280 0C is used in

order to dry up the graphite electrode. High precision digital single panned balance,

model GR-200, AND is used to measure the weight of the workpiece and electrode. It is

limited in measurement to 10 mg - 210 g with a readability of 0.1 mg. Surface

roughness is determined with Perthometer S2, Mahr.

Titanium alloy, Ti-5Al-2.5Sn, is used as the work-piece material, and copper,

copper-tungsten and graphite are utilized as an electrode. The machining parameters as

peak current, pulse-on time, pulse-off time, and servo-voltage are varied during

machining. The ranges are picked as 1-29 A, 10-350 μs, 60-300 μs and 75-115 V

correspondingly for peak current, pulse-on time, pulse-off time and servo-voltage. The

polarity of the electrode is retained as positive and negative. The material removal rate,

surface roughness, and tool (electrode) wear rate are considered as the performance

characteristics. The surface topography is also taken into consideration for investigation.

In the present study, response surface methodology (RSM) is carried out through design

of experiment (DOE) in order to attain the design settings. The techniques, namely

response surface methodology and multilayer perceptron neural network, are

implemented for analysis and modelling purposes. The equipment used for viewing the

surface topography is limited to a scanning electron microscope (SEM), model EVO 50,

Zeiss.

7

1.5 ORGANIZATION OF THESIS

The rest of this dissertation is comprised of four chapters: the literature review,

research methodology, results and discussion, and conclusions. Chapter 2 discusses the

appliances of the titanium alloys along with their machining status. This chapter reviews

past research conducted with regards to searching for important parameters and the

effect of the parameters on EDM performance characteristics. Several attempts adopted

by the prior research for modelling the EDM parameters for different materials, based

on distinct methods, are discussed. Chapter 3 describes the details of EDM parameters

following properties of selected work-piece and electrode materials. This chapter also

deals with the procedure to carry out the experiments, measurements and computation

of the process output. Eventually, Chapter 3 delivers the details of model development

through response surface methodology and artificial neural network technique. Chapter

4 presents the results obtained in favor of RSM model and ANN model, as well as the

impact of the machining parameters on EDM performance. In addition, this chapter

provides the results of model validation, optimal parametric settings and, finally, the

investigation through scanning electronic microscopic analysis. Chapter 5 reports the

conclusions and recommendations for future work.