analysis and optimization of container lifting device … · i declare that the thesis entitled...

ANALYSIS AND OPTIMIZATION OF CONTAINER LIFTING DEVICE USED FOR SOLID WASTE MANAGEMENT

A Thesis submitted to Gujarat Technological University

for the Award of

Doctor of Philosophy

in

Mechanical Engineering

By

UTPAL VINODCHANDRA SHAH Enrollment No. 139997119018

under supervision of

Dr. (Prof.) G.H.Upadhyay

GUJARAT TECHNOLOGICAL UNIVERSITY AHMEDABAD

July 2018

DECLARATION

I declare that the thesis entitled “ANALYSIS AND OPTIMIZATION OF CONTAINER

LIFTING DEVICE USED FOR SOLID WASTE MANAGEMENT” submitted by me

for the degree of Doctor of Philosophy is the record of research work carried out by me

during the period from June 2014 to July 2018 under the supervision of Dr. (Prof.) G. H.

Upadhyay and this has not formed the basis for the award of any degree, diploma,

associateship, fellowship, titles in this or any other University or other institution of higher

learning.

I further declare that the material obtained from other sources has been duly acknowledged

in the thesis. I shall be solely responsible for any plagiarism or other irregularities, if

noticed in the thesis.

Signature of the Research Scholar: …………………………… Date: 25-07-2018

Name of Research Scholar: UTPAL VINODCHANDRA SHAH

Place : Ahmedabad

CERTIFICATE

I certify that the work incorporated in the thesis “ANALYSIS AND OPTIMIZATION

OF CONTAINER LIFTING DEVICE USED FOR SOLID WASTE

MANAGEMENT” submitted by Shri UTPAL VINODCHANDRA SHAH was

carried out by the candidate under my supervision/guidance. To the best of my

knowledge: (i) the candidate has not submitted the same research work to any other

institution for any degree/diploma, Associateship, Fellowship or other similar titles (ii)

the thesis submitted is a record of original research work done by the Research Scholar

during the period of study under my supervision, and (iii) the thesis represents

independent research work on the part of the Research Scholar.

Signature of Supervisor: ……………………………… Date: 25-07-2018

Name of Supervisor: Dr. (Prof.) G. H. Upadhyay

Place: Ahmedabad

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Originality Report Certificate

It is certified that PhD Thesis titled “ANALYSIS AND OPTIMIZATION OF

CONTAINER LIFTING DEVICE USED FOR SOLID WASTE MANAGEMENT”

By UTPAL VINODCHANDRA SHAH has been examined by us. We undertake the

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a. Thesis has significant new work / knowledge as compared already published or are

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originality report attached) and found within limits as per GTU Plagiarism Policy

and instructions issued from time to time (i.e. permitted similarity index <=25%). Signature of the Research Scholar: …………………………… Date: 25 -07-2018

Name of Research Scholar: UTPAL VINODCHANDRA SHAH

Place :Ahmedabad Signature of Supervisor: ……………………………… Date: 25 -07-2018

Name of Supervisor: Dr. (Prof.) G. H. Upadhyay

Place: Ahmedabad

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GUJARAT TECHNOLOGICAL UNIVERSITY

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Signature of the Research Scholar: Name of Research Scholar: UTPAL VINODCHANDRA SHAH Date: 25-07-2018 Place: Ahmedabad Signature of Supervisor: Name of Supervisor:Dr. (Prof.) G. H. Upadhyay Date: 25-07-2018 Place: Ahmedabad Seal:

Thesis Approval Form The viva-voce of the PhD Thesis submitted by Shri UTPAL VINODCHANDRA SHAH

(Enrollment No. 139997119018) entitled “ANALYSIS AND OPTIMIZATION OF

CONTAINER LIFTING DEVICE USED FOR SOLID WASTE MANAGEMENT” was

conducted on 25 – 07 – 2018, Wednesday at Gujarat Technological University.

(Please tick any one of the following option)

The performance of the candidate was satisfactory. We recommend that he/she be

awarded the PhD degree.

Any further modifications in research work recommended by the panel after 3 months

from the date of first viva-voce upon request of the Supervisor or request of

Independent Research Scholar after which viva-voce can be re-conducted by the same

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(briefly specify the modifications suggested by the panel)

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should not be awarded the PhD degree.

(The panel must give justifications for rejecting the research work)

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I

ABSTRACT

In all mega cities and municipal corporations, HCV chassis are used for loading and

unloading the containers having size more than 5.5 cubic meters. But for all towns and

nagarpalikas, these may not be preferred due to space limitations and narrow size roads. So

in the case of towns and nagarpalikas, tractor driven container lifting device may be used,

which will lift up to 4.5 cubic meter containers. They can use containers of up to 4 ‐ 4.5 cubic

meters capacity, which will make optimum use of the tractors. Even for more space

limitations small containers mounted on LCVs chassis can also be used.

It has been observed that mostly all municipal corporations are using 5.5 and 6 cubic meter

containers operated by a truck as the main source of waste collection and transportation.

But at ULBs, small towns and villages, it is very difficult to operate truck operated containers

because of following reasons:

1) Quantity of Solid waste to be handled is less as compare to Municipal Corporation.

2) Cost of truck operated container lifting device is also very high.

3) Narrow size of the roads and space limitations also do not allow using such solid

waste handling systems.

In this work, small container having capacity of 4.5 cubic meters is fabricated so that it can

be operated by tractor operated container lifting device. The idea was to design and

develop new container lifting device that can be used to handle 4.5 cubic meter container

which is to be operated by tractor. As tractor is used to operate container lifting device, in

the spare time it can be used to operate trolleys, water tanker etc. at the Nagarpalikas. In

short, tractor can be used to perform multiple activities at Nagarpalika/ULB level. This

combination can be made at less than half of cost in comparison to the truck operated

container lifting device.

The main focus of the research work is the design and development of container lifting

device that can handle 4.5 cubic meter container with the help of tractor. Work has been

carried out for design, failure analysis, vibration and FEA of various components of CLD, i.e.

hydraulic cylinder, link chain, different joints, cross bars, mechanical jack, leaf spring etc.

Hydraulic cylinder is the most critical components of CLD, which is further optimized

considering single and multi‐objective optimization problem using MATLAB.

II

The optimized design and modifications suggested in this work related to the CLD are

successfully implemented by GUDC and it is used in all ULBs in the state of Gujarat for solid

waste management. This CLD system is used to handle 4.5 cubic meter container with the

help of tractor to collect and transport municipal solid waste by various Nagarpalikas in

Gujarat State.

III

ACKNOWLEDGEMENT

I am indebted to various people and organizations during my doctoral research since I

received this opportunity in 2013. I would like to express my deepest gratitude to Dr. (Prof.)

G. H. Upadhyay, (Professor & Head, Mechanical Engineering Department, L. D. College of

Engineering, Ahmedabad) for his great support and invaluable guidance throughout my

research. I remember during my post graduation study when I had interaction with him. It

was then; I was inspired to work for Ph.D. under his guidance.

I would like to extend my gratitude to Dr.(Prof.) A. B. Dhruv, Dr. (Prof.) J. A. Vadher, DPC

members of research monitoring committee for this research for their valuable suggestions.

Their deep insights into the problem and ability to provide guidance and solutions have

been of immense value in improving the quality of my research work at all stages.

Under the broad vision of Government of Gujarat officials from GUDC, they formed team

called TAG (Technical Approval Group) since year 2008 for the MSWM (Municipal Solid

Waste Management) in the state of Gujarat. In that team I got a chance to work with all

technical experts from different municipal corporations, like Mr. Vijay Mistry, (Joint Director

(Mechanical), Ahmedabad Municipal Corporation) where I learned a lot regarding

equipments used for MSWM (Municipal Solid Waste Management). I recognize that this

research would not have been possible without the support of GUDC, AMC and AUDA

officials from the Government of Gujarat and I express my sincere thanks to them.

I would like to express a deep sense of gratitude to my respected Dr. G. P. Vadodaria

(Principal, L D College of Engineering, Ahmedabad), Dr. M N Patel (Ex – Principal, L D College

of Engineering & Ex – Vice Chancellor Gujarat University), Dr.(Prof.) A. M. Prabhakar

(Principal, GEC, Modasa) for allowing me to utilize the resources of institute and for

continuous support.

I would like to express my deepest sense of gratitude for support, continuous advice,

intelligent approach, constructive criticism, whole hearted and ever available help and

encouragement throughout the Ph.D. program by Dr. (Prof.) B. J. Shah, Prof. G. B. Modha

and Dr. (Prof.) B. C. Khatri.

IV

Special thanks also go to Prof. S. P. Patel, Prof. P.D. Patel and Prof. R. J. Jani for encouraging

and supporting me to complete this research work.

I also acknowledge Shri D. N. Shah and other workshop staff for constant support during

vibration analysis of hydraulic cylinders of CLD using FFT analyser to complete this research

work.

Last but not least I would like to express my deep sense of gratitude to my parents and

family members whose support in all time made me mentally free to concentrate my thesis

work.

UTPAL VINODCHANDRA SHAH

V

TABLE OF CONTENT Chapter Title Page No. No.

Abstract I

Acknowledgement III

Table of content V

List of Abbreviations VIII

List of Symbols IX

List of Figures X

List of Tables XIII

1. Introduction 1

1.1 Introduction for municipal solid waste management 1

1.2 Solid waste management in Indian scenario 2

1.3 MSW – A growing challenge 5

1.4 Transportation of Waste 5

1.5 Transport of Waste in Open Vehicles 6

1.6 Major drawbacks of the SWM transport system 7

1.7 Introduction to Container Lifting Device used for Solid Waste 8

Management

1.8 Objectives of the study 9

1.9 Scope of the work 9

1.10 Description of the research work 9

1.11 Tools to be used 10

2. Review of literature 11

2.1 Introduction to literature review 11

2.2 Literature Review on Solid Waste Management 11

2.3 Literature review on Design and FEA of CLD 13

2.4 Literature review on Optimization of hydraulic cylinders 19

2.5 Performance problem identification through survey 21

2.6 Research Gap 22

3. Design Methodology 23

3.1 Introduction 23

VI

Chapter Title Page No. No.

3.2 Design of Hydraulic Cylinder 23

3.2.1 Material used for Hydraulic Cylinder 24

3.2.2 Theoretical Design of Cylinder Tube 24

3.2.3 Design of Thick Cylinder 25

3.3 Theoretical Design of Piston and Piston Rod 26

3.4 Design of Cross‐Rod 26

3.5 Design of Rear Mechanical Jack of CLD Model 27

3.5.1 Loading location 27

3.5.2 Design of welded joint 29

3.5.3 Design of locking pin 29

3.6 Design of leaf spring 29

3.6.1 Materials for leaf spring 32

4. Design calculations 33

4.1 Design calculations – Anchor pin for 4.5 cubic meter container 33

4.2 Design calculations of hydraulic cylinder 36

4.3 Design for pin joint 38

4.4 Design Calculation of Cross‐Rod 39

4.5 Design calculation for Rear mechanical Jack 40

4.6 Design calculations of leaf spring for CLD model 42

4.7 Selection of hoisting chain link for container lifting 44

4.7.1 Materials for chain link 45

5. Modelling and analysis of container lifting device 47

5.1 3D modelling 47

5.1.1 About SolidWorks 2014 47

5.2 Finite element analysis (FEA) 47

5.3 Transient Structural Analysis of Container Lifter Model in Ansys 48

5.3.1 Steps for transient analysis in Ansys Workbench 49

5.4 Dynamic Analysis of Container Lifting Device 52

5.4.1 Rigid dynamic analysis of CLD model 54

5.4.2 Transient dynamic analysis of CLD model 57

5.5 Static Analysis of Leaf Spring 65

VII

Chapter Title Page No. No.

5.6 Static Analysis of Mechanical Jack 67

5.7 FFT Analyzer used for vibration measurement of hydraulic cylinder 68

5.8 OMNITREND software and its applications 72

5.9 VIBXPERT – FFT (Fast Fourier Transform) data collector and signal 73

Analyzer

6. Optimization in Hydraulic Cylinder 77

6.1 Introduction 77

6.2 Optimization Technique – Genetic Algorithm 78

6.2.1 Outline of genetic algorithm 79

6.3 Optimization in Hydraulic Cylinder Design – A Case Study 80

6.4 Single Objective Optimization Problem – Nonlinear Constrained 82

Minimization

6.5 Multi Objective Optimization using Genetic Algorithm 83

6.6 Difference with Single objective optimization 84

6.6.1 Two goals instead of one 85

6.6.2 Dealing with two search spaces 85

6.6.3 No artificial fix‐ups 86

6.7 Multi – objective optimization 86

6.8 Conclusion of optimization 93

7. Conclusion and future scope of research 94

7.1 Conclusion of research work 94

7.2 Future scope of research work 95

ANNEXURE I 97

ANNEXURE II 100

REFERENCES 102

LIST OF PUBLICATIONS 108

VIII

LIST OF ABBREVIATIONS

Sr. No. Abbreviations Meaning

1 AMC Ahmedabad Municipal Corporation

2 APDL ANSYS Parametric Design Language

3 ASTM American Society for Testing Materials

4 AUDA Ahmedabad Urban Development Authority

5 CFD Computational Fluid Dynamics

6 CLD Container Lifting Device

7 FEA Finite Element Analysis

8 FFT Fast Fourier Transform

9 FOS Factor of safety

10 GUDC Gujarat Urban Development Corporation

11 HCV Heavy Commercial vehicles

12 ISO International Organisation for Standardization

13 LCV Light Commercial Vehicles

14 MSWM Municipal Solid Waste Management

15 SWC Safe Working Capacity

16 ULB Urban Local Bodies

17 WLL Working Load Limit

IX

LIST OF SYMBOLS

Symbol Description

% Percentage

Poisson’s ratio

r Radial stress

t Tensile stress

id Internal diameter

od External diameter

Shear stress

mg milligram

gm gram

mm Millimetres

hr Hour

y Yield stress

[L] Lower Bound

[U] Upper Bound

X

LIST OF FIGURES

Figure No. Title of Figure Page No.

Fig. 1.1 Process of solid waste management 2

Fig. 1.2 Waste transportation in open vehicles 6

Fig. 1.3 Inappropriate secondary storage 6

Fig. 1.4 Manually waste loading in open tractors in small towns 6

Fig. 1.5 Manual loading of waste in open trucks in large cities 6

Fig. 1.6 Truck mounted container lifting device 8

Fig. 2.1 Variation of experimental stress of steel and composite leaf Springs 15

Fig. 2.2 Load‐deflection curves for steel and composite leaf springs 15

Fig. 2.3 The state of loading condition with respect to the installation of 15

crane chain

Fig. 2.4 Tensile stress–strain behaviour of the Grade‐80 steel chain 17

Fig. 2.5 Mating fracture of surfaces at welded portion 17

Fig. 2.6 Different types of eye design used for leaf spring 18

Fig. 3.1 Forces acting on hydraulic cylinder 23

Fig. 3.2 Thick cylindrical shell stress distribution over its thickness 25

Fig. 3.3 Cross‐rod fixed at both ends 26

Fig. 3.4 3D model of mechanical rear jack 27

Fig. 3.5 Eccentric loading of column 28

Fig. 3.6 Resulting stress distribution 28

Fig. 3.7 Column welded to body of CLD 29

Fig. 3.8 Shear of locking pin 29

Fig. 3.9 Semi – elliptical leaf spring 30

Fig. 4.1 Drawing of 4.5 cubic meter container used for CLD 34

Fig. 4.2 Drawing of anchor pin used in 4.5 cubic meter Container 35

Fig. 4.3 Cylindrical pin joints 38

Fig. 4.4 Drawing of Cross‐rod used in CLD 39

Fig. 4.5 Cross – section area of column section 41

Fig. 4.6 Model of chain link 45

Fig. 5.1 SolidWorks 3‐D model of container lifting device 53

XI


Fig. 5.2 Joints between individual components of CLD model 54

Fig. 5.3 Remote force, constrain, joint velocity, acceleration shown in CLD model 55

Fig. 5.4 Force shown on translation joint probe 56

Fig. 5.5 Translation joint force change with time 56

Fig. 5.6 Fine meshing of CLD model 57

Fig. 5.7 Time varying force applied on piston 59

Fig. 5.8 Time varying reaction force applied on cylinder 59

Fig. 5.9 Remote force, constrain, joint velocity, acceleration shown in CLD 59 model for transient analysis

Fig. 5.10 Von‐Mises stress contour generated in CLD model 60

Fig. 5.11 Maximum Von‐Mises stress generate in CLD model 60

Fig. 5.12 Maximum value of Von‐Mises stress change with time 61

Fig. 5.13 Equivalent elastic strain contour generated in CLD model 61

Fig. 5.14 Maximum value of Equivalent elastic strain change with time 61

Fig. 5.15 Total deformation of CLD model 62

Fig. 5.16 Total deformation of CLD model with respect to time 62

Fig. 5.17 Cylinder‐ base total revolute joint probe force 62

Fig. 5.18 Time varying total cylinder‐ base revolute joint force 63

Fig. 5.19 Big link‐ base total revolute joint probe force 63

Fig. 5.20 Time varying total big link – base revolute joint force 63

Fig. 5.21 Big link – piston rod total revolute joint probe force 64

Fig. 5.22 Time varying total big link‐ piston rod revolute joint force 64

Fig. 5.23 Safety factor contour generate in CLD model 64

Fig. 5.24 Minimum time varying safety factor 65

Fig. 5.25 3 – D model of leaf spring 65

Fig. 5.26 Load and constrain applied to model of leaf spring 66

Fig. 5.27 Stress generated in leaf spring 66

Fig. 5.28 Strain generated in leaf spring 66

Fig. 5.29 Load and constrain applied to model of mechanical jack 67

Fig. 5.30 Stress generated in mechanical jack 67

Fig. 5.31 Strain generated in mechanical jack 68

XII


Fig. 5.32 Hydraulic Cylinder 69

Fig. 5.33 Hydraulic cylinder with bush 69

Fig. 5.34 OMNITREND Software 72

Fig. 5.35 FFT spectrum of Hydraulic cylinder 74

Fig. 5.36 Photographs of actual readings taken of CLD by FFT analyzer 76

Fig. 6.1 Optimization using MATLAB for the function: Minimization of 82

force value (f) exerted on piston

Fig. 6.2 Optimization using MATLAB for the function: Minimization of 83

Cross‐sectional area (A) of the Hydraulic Cylinder

Fig. 6.3 Mapping from Parameter Space into Objective Function Space 87

Fig. 6.4 Set of Non‐inferior Solutions 87

Fig. 6.5 Pareto optimization using Genetic Algorithm plot of Stress 89 generated (N/mm2) v/s Force on Piston (N)

Fig. 6.6 Pareto optimization using Genetic Algorithm plot of Force (N) 91 v/s Thickness of Cylinder (mm)

Fig. 6.7 Pareto optimization using Genetic Algorithm plot of Force (N) 93 v/s Cross‐sectional area of Cylinder (mm2)

XIII

LIST OF TABLES

Table No. Title of Table Page No.

Table 2.1 Parameter comparison between standard eye and casted eye 18

Table 4.1 Grade 80 alloy chain standard by national association of 46

chain manufacturer

Table 5.1 Rigid dynamic analysis setup for CLD model 52

Table 5.2 Material and its property for individual component of CLD 53

Table 5.3 Joint applied to different pair of CLD component 54

Table 5.4 Analysis setting in rigid dynamic analysis of CLD model 55

Table 5.5 Value of translation joint force with respect to time 57

Table 5.6 Transient dynamic analysis setting 58

Table 5.7 Transient dynamic analysis setup of CLD model 58

Table 5.8 Typical measurements of RMS values 75

Table 6.1 Comparison of Classical and Genetic Algorithm 79

Table 6.2 Value of each variable Internal diameter (d), Thickness of Cylinder (t) 88

and Internal Pressure (p) after each iteration

Table 6.3 Value of each variable Internal diameter (d), Stress of Cylinder (s) 90


Table 6.4 Value of each variable internal diameter (d), Thickness of Cylinder (t) 92


Table 7.1 Comparison of results with allowable value 94

1

CHAPTER 1

INTRODUCTION

1.1 Introduction for Municipal Solid Waste Management

Municipal solid waste (MSW) includes waste from market waste, yard waste,

agricultural wastes, households waste, non‐hazardous solid waste from industrial, street

sweepings, institutional and commercial establishments (excluding bio‐medical waste in

present context). Community – Industrial hazardous waste, infective waste are not

considered as MSW and should be collected – processed separately. Management and

Handling Rules 2000 defines MSW as “commercial and residential wastes generated in

municipal or notified areas in either solid or semi‐solid form excluding industrial hazardous

wastes but including treated bio‐medical wastes”. MSW related various other definitions are

defined in MSW Rules 2000, which are given in ANNEXURE I. MSW management covers the

functions of collection, transfer – transportation, processing – recycling and final disposal of

solid waste. Safe and cost‐effective management of MSW is a significant environmental

challenge for modern society. Imperfectly managed waste disposal is directly affected to the

human health and environment.

Preferably MSW management shall include the ethics of waste minimization,

recycling, resource recovery as well as an integrated processing – disposal facility, leading to

effective service delivery in a justifiable manner. Solid waste should be managed at all

stages starting from waste generation to the final waste disposal. An integrated solid waste

management plan would consist of:

understanding the present waste management practices

recognizing waste management needs

priorities shall be set for the required actions

budget need should be identified

different stakeholders shall be coordinated

determining progress in terms of targets achieved

as the plan advances priorities shall be modified

communication and coordination with the external/local agencies to achieve the

desired targets.

Introduction

2

For the purpose of EIA (Environmental Impact Assessments) Notification, common

municipal solid waste management facilities may be observed and compare with centralized

MSW facility for any given town, city, region. It is further to mention a common facility need

not have surrounding ULBs included.

1.2 Solid waste management in Indian scenario

The rapid urbanization is changing the nature of solid waste management from a low

priority, localized issue to a general social and environmental problem with risks to public

health and environment. Usually, MSW is disposed of in low‐lying regions without taking

any safety measures or effective controls. Therefore, MSWM is one of the most important

environmental problems of Indian megacities. The MSWM system comprises five activities,

i.e. waste generation, collection, transportation, processing/treatment and disposal.

Fig. 1.1 Process of solid waste management[16]

MSW management is constrained by respective organisation weakness, less funding,

improper management and operational systems, public laziness, lack of municipal staff. This

can be made self – sustained by increasing the income of Municipal Corporation through

taxation. [12]

In Indian towns, MSW storage is to be done at a central place. Persons deposit their

waste in bins – enclosures located at street corners at specific intervals. The waste

containers generally are constructed of metal, concrete, or brickwork. In most of the towns

and cities indiscriminate littering of roads and drains are found. Community storage may

reduce the cost of waste collection, but chances of scattering remains will be the major

issue. Scavenging of the wastes by stray animals and rag pickers may lead to further

scattering of solid waste.

It is often observed by the experienced municipal experts that the lack of public

alertness among city residents is proving to be a major hurdle in maintaining the cleanliness.

This problem is most severe in slums and in areas where the lower and middle income

groups reside. Because of the poor conditions people are not using proper ways of

Solid waste management in Indian scenario

3

temporary storage of wastes. In some of these types of areas, NGOs are involved in making

arrangements for waste collection from households leading to improvement in the street

cleanliness.

For transportation of waste, different types of vehicles, varying from bullock carts to

compactors are used. In large cities open body trucks of 5 to 9 tons capacity are in common

use, but in smaller towns, tractor‐trailers are used despite being slow. In few large cities,

compactor vehicles are also used for transporting the solid waste. The waste is transported

mostly by municipality owned vehicles; though in some big towns, private contractor

vehicles are also hired to expand the working. These vehicles are generally maintained in

the municipal workshop along with other vehicles of the corporation but will get less priority

in maintenance. Most of these municipality workshops have facilities for minor repairs only.

Although preventive maintenance is necessary in these types of vehicles, but this aspect is

often neglected.

In India, thousands of urban residents make their living upon waste processing by

working in small industries, which recycle tin cans, plastics, leather, glass, bottles, bones,

hair, metal etc., recovered from MSW. Most of the material containing metals, unsoiled

paper, plastics, glass, cardboard, etc. are marketable and hence recycled by householders

themselves or by the rag‐pickers. By the time waste reaches the community bins, it contains

only a small portion of recyclable material and consists mainly of vegetables – fruit peelings,

used toiletries, scraps of soiled paper –plastic and inert material such as sand and stones,

etc.

The greater proportion of organic matter in MSW indicates that the biological

processing of waste such as composting. There are several problems found in composting

like transportation, poor recognition by farmers (may be for the quality concerns),

marketing, price etc. There are several efforts are being taken to promote waste

segregation and composting recently. With a majority of Indian successful combustion

reaction cannot be obtained so supplementary fuel will be required to aid waste

combustion.

Generally in all towns, waste is being disposed of in low‐lying areas. Waste disposal

sites are preferred on the basis of their closeness to the collection areas and new dumping

sites are normally identified only when the existing ones are filled. The waste is simply

Introduction

4

dumped at such sites and, except in the major cities. Rarely bulldozers are used for

compaction at the disposal site. In megacities, they are used mainly for leveling of the

deposited waste. In many cities and towns proper weighing, filling and soil layering are not

usual practice. A soil cover is also hardly ever provided, except at the time of final closure of

the site. Most of the disposal sites are unfenced and the waste picking is commonly in

vogue, posing problems in the operation of the sites. The rag‐pickers usually have a

collective practice to light a fire on the dumpsite either to reduce the menacing flies, volume

or odour results in easy way of waste picking.

For the authorities, it is very difficult to acquire land for creating waste management

and dumping facilities so it is essential that the present dumpsites are reformed to collect

current and future wastes. Usually haphazard dumping of waste across the dumpsites has

been a routine practice results in pollution of the surrounding areas, so those dumpsites

need to be reformed and redesigned to recover space for future wastes. These reformation

measures should include actions to be taken pollutant migration. This dumping site shall be

designed in such a way to accept future wastes for a period of 20 years or more and well

planned operations, processes and maintenance.

The human activities create waste, and the way of practice that waste is handled,

stored, collected, and disposed of can create risks to the environment and to human health.

In urban areas like especially rapidly growing cities of the developing world, problems and

issues of municipal solid waste management (MSWM) are of immediate importance.[16]

Most of state governments have acknowledged the importance of MSWM; however, rapid

population growth and changing life style overcomes the capability of most municipal

establishments to provide the most basic services.

Typically one – to two – thirds of the solid waste that is generated is not collected in

major cities. The uncollected waste is dumped in the streets or in drains in open channel,

create problem like flooding, breeding of insect, rodent vectors and spreading of diseases.

Even solid waste that is collected is often disposed of in uncontrolled dumpsites or burned,

polluting water resources and the air. Proper implementation of MSWM practices benefits

directly to both human health and environmental quality.

MSW – A growing challenge

5

1.3 MSW – A growing challenge

Growing urbanization in India will result in a massive increase of waste over the next

two decades. The urban population is expected to represent 41% of the overall population

by the year 2021. During a study conducted by the CPCB on management of MSW in the

country, estimates that waste generation from the present 48 million tons (MT) per year is

expected to increase to 300 MT per year, by the year 2047 (490 g per capita to 945 g per

capita). The estimated requirement of land for disposal would be 169.6 square kilometer

(km2) in 2047 as against 20.2 square kilometer (km2) in 1997 (CPCB 2000a).[14]

Annually in India at present 48.0 MT of MSW is produced.

There is a increase in urban population by 3 – 3.5 % per annum.

In India per capita waste generation is increasing by 1.3 % per annum.

Yearly increase of waste generation in India is approximately 5%.

The urban local bodies are investing around35 – 50% of its available funds to deal

with the waste generated in urban areas. They are spending about Rs. 500 – 1500 per ton

on solid waste management. Hence there is a vital need to increase effectiveness for better

service delivery and optimization.

Land selection for the disposal of solid wastes has been very important criteria for

centuries. Agricultural, municipal, urban and industrial activities produce enormous

amounts of wastes, which require safe, stable and permanent secured disposal. For

extending financial resources, the Central Government has incorporated solid waste

management as one of the components in the Jawaharlal Nehru National Urban Renewal

Mission (JNNURM) programme for the growing challenge of solid waste management in the

country. Many municipal corporations and cities are getting benefit from this enormous

programme.

1.4 Transportation of Waste

The following rules apply to transport of solid waste:

• Transport vehicles used for waste collection and dispose are covered.

• While doing transport waste, it should not be visible to public and exposed to the

environment, thus preventing the strewing of waste on rod.

• For clearing of waste attend to storage facilities daily.

Introduction

6

• Regular empty bins or containers before they start overflowing.

• Waste transport vehicles are designed so that it can eliminate multiple handling of waste

before final disposal.

1.5 Transport of Waste in Open Vehicles

Waste handling in the big cities, ULBs and nagarpalikas is not normal practice.

Unluckily, service of waste management is performed very unproductively and in an

unhealthy method. Open tractor trailers and trucks are used to transport waste and those

are loaded manually. In rainy season the handling of these types of waste by local workers

of the village/town is most dangerous. This inefficient process results in decreasing labour

efficiency and increases the possibility of work‐related health risk to all workers as shown in

following photographs.

Fig.1.2 Waste transportation in open vehicles [16] Fig.1.3 Inappropriate secondary storage[16]

Fig.1.4 Manually waste loading in open Fig.1.5 Manual loading of waste in open tractors in small towns[16] trucks in large cities[16]

Major drawbacks of the SWM transport system

7

1.6 Major drawbacks of the SWM transport system:

Major drawbacks of the SWM transportation system are summarized as follows:

• While transporting the solid waste in open trucks and tractor trailer, it falls and thereby

causing nuisance.

• Manual handling of waste like loading or unloading from vehicles without use of any

protection is hazardous to the health of workers.

• The waste transportation system is not properly matched with the secondary storage

system. Problems faced even a transport taskforce is modernized, because waste at the

secondary storage station is still dumped on the ground. If the secondary storage system

is restructured without a proper number of recent vehicles, similar problems may occur.

• Multiple handling of waste results in lower down labour and equipment productivity.

• Over flowing secondary waste storage depots result in irregular and untimely transport of

waste.

• All the areas cannot be given service properly because of an inadequate number of

vehicles.

• Vehicles are not properly maintained because of inadequate workshop facilities and

lengthy maintenance procedures. This problem leads to frequent breakdowns resulting

vehicles shall be kept out of service for longer periods.

• Spare parts are not easily available, because of the procurement system which is

cumbersome and very slow.

• Vehicle movement is not observed properly in terms of quantity of waste carried, number

of trips made and optimum use of workers.

• Due to unplanned routing of vehicles, ineffectiveness transport logistics is found.

The process of MSWM is divided into majorly five stages like generation, collection,

transportation, processing and disposal. In the present study major focus is given on

collection and transportation stage. The drawbacks of the MSWM described above are more

or less related to the collection and transportation of solid waste. Hence in the present

study the focus is made on the modifications in design of CLD with lower capacity of

containers which can be used in small towns, ULBs and villages.

Introduction

8

1.7 Introduction to Container Lifting Device used for Solid Waste Management

For transport of solid waste the concerned authority authorities need to decide the

type of vehicles to be procured and the system of transportation to be adopted. If they use

a containerized system the vehicles needed may be calculated according to the number of

containers that will become full each day and the number of containers each container

lifting device will be able to take to the transfer station, treatment plant or disposal site.

Normally, one vehicle will be able to lift seven or eight containers if the distances to

be travelled are within 5 kilometers. The number of trips may be reduced to five or six if the

distance is between 5 and 10 kilometers, and it may be further reduced depending on the

distance travelled. In addition, 25 to 30 percent additional spare vehicles will be needed to

maintain reliability of service during breakdowns and during preventive maintenance of

vehicles.

Possibly vehicles are to be utilized in two shifts to ensure maximum benefits, better

results and optimum use of investment made.

Fig. 1.6 Truck mounted container lifting device[14]

In all mega cities and municipal corporations' hcv chassis are used for loading and

unloading the containers having size more than 5.5 cubic meters. But for all towns and

nagarpalikas these may not be preferred due to space limitations and narrow size roads. So

here tractor driven container lifting device may be used which will lift up to 4.5 cubic meter

containers. They can use containers of up to 4 to 4.5 cubic meters capacity, which will make

optimum use of the tractors. Even for more space limitations small containers mounted on

LCVs chassis can also be used.

Objectives of the study

9

1.8 Objectives of the study

The main Objective of this study is to design the container lifting device operated

with tractors or LCVs used for small towns.

To determine the feasibility of improving performance of container lifting device for

solid waste management.

To identify the problems/difficulties faced with the existing design/specifications of

the container lifting device.

To identify significant parameters affecting performance of the container lifting

device.

To review existing design/specifications of container lifting device.

To establish the improved design/specifications of the container lifting device that

will overcome most of the problems related the performance parameters.

1.9 Scope of the work

Feasibility assessment through survey based on questionnaires from end users and

manufacturers of container lifting device.

Problem identifications based on survey conducted.

Identification of critical parameters will be assessed based on literatures available,

technical talk with experts and self‐assessment of the actual operations cycle of the

existing systems.

Analysis tool will be used to finalize the most favorable design.

1.10 Description of the research work

The detailed survey work for the different container lifting device will be done in the

first phase. It covers study and analysis of existing container lifting device used for

the solid waste management.

In second phase problems during operations and maintenance will be identified from

the all data collected.

Introduction

10

Actual need base analysis is done according to the requirement of solid waste

management.

Based on the above analysis new improved optimum utilization and convenient

design will be suggested for easy operation and less maintenance.

The same design will be checked using software as well as it will be checked

experimentally under AMC, AUDA and GUDC.

1.11 Tools to be used

Iterative design procedure and calculation – MATLAB/SCILAB

CAD model – Solidworks

Design analysis – Hypermesh, Hyperworks, ANSYS

Design Optimization

Design testing and validation

11

CHAPTER 2

REVIEW OF LITERATURE

2.1 Introduction to literature review

Initially literature review had been done in the field of solid waste management.

There is always a scope of improvement possible at the every stage of SWM in our country.

It had been observed that mostly all municipal corporations are using 5.5 and 6 cubic meter

containers operated by a truck as the main source of waste collection and transportation.

But at ULBs, small towns and villages, it is very difficult to operate truck operated containers

because of following reasons:

1) Quantity of Solid waste to be handled is less as compare to Municipal Corporation.

2) Cost of truck operated container lifting device is also very high.

3) Narrow size of the roads and space limitations are also not allowing us to use such

solid waste handling systems.

After that focused in the design of small containers having capacity of 4.5 cubic

meter and it can be operated by tractor operated container lifting device. So the idea was to

design and develop new container lifting device to be used to handle 4.5 cubic meter

container and which is to be operated by tractor. If tractor is used to operate container

lifting device, in spare time the same tractor can be used to operate trolleys, water tanker

etc. at the Nagarpalikas. In short tractor can be used to perform multiple activities at ULB

level. This combination can be made at less than half of cost if it is compare with truck

operated container lifting device.

In second phase of literature review had been carried out for design, failure analysis,

vibration and FEA related papers of hydraulic cylinder, link chain etc. used in container

lifting device. After designing, FEA and vibration analysis of container lifting device its most

critical component i.e. hydraulic cylinders needs to be optimised. So in third phase of

literature review, optimizations of hydraulic cylinders papers were reviewed. Here main

focus was given to single and multi‐objective optimization of hydraulic cylinders.

2.2 Literature Review on Solid Waste Management

(1) Mufeed Sharholy & etc. at Elsevier (2008) present that Municipal solid waste

management (MSWM) was one of the major environmental problems of Indian cities.

Review of literature

12

Improper management of municipal solid waste (MSW) causes hazards to inhabitants.

Several studies reveal that about 90% of MSW was disposed of unscientifically in open

garbage dump and landfills, creating problems to public health and the environment.[58]

Generally transfer stations are not using the same vehicle, which collects refuse from

individual dustbins, precedes it to the processing or disposal site (Colon and Fawcett, 2006;

Khan, 1994). The MSW collected from the garbage bins at collection points are transported

to the processing or disposal sites using a variety of vehicles. In rural towns, bullock carts,

tractor‐trailers, tricycles, hand carts etc., are mainly used for the transportation of solid

waste. Light motor vehicles (LMV) and trucks (HCV) are generally used in big towns or cities

for transportation of MSW. The trucks used for transportation of MSW are generally of an

open body type and are usually kept uncovered; thus during transportation, the waste tends

to fall on the roads resulting in unhealthy environments. In some cities, modern hydraulic

vehicles are gradually being introduced (Bhide and Shekdar, 1998; Reddy and Galab,

1998).[58]

Author briefly describe present trend in transportation of MSWM in small and big

town. According to author Collection and transportation activities constitute almost 80–95%

of the total budget of MSWM. Hence, it forms a important component in determining the

economics of the entire MSWM system. Municipal agencies use their own vehicles for MSW

transportation although in some cities these may be hired from private service providers.

(2) Somnath Debnath & S.K.Bose at Elsevier (2013)[86]reflects the present stage of the

MSW services in India and explores full cost accounting (FCA) structure in its aptitude

towards generation of information on cost related aspects and supportable distribution of

capitals.

Authors also quoted the case study of SWM costs of Municipal Corporation of

Greater Mumbai. The findings from the study indicates that the feasibility of having FCA as a

temporary arrangement to use accounting data. It also generates the information that

would be relevant for improvements in decision making. The classification and gathering of

data is somewhat arbitrary in the selected example of MCGM – Municipal Corporation of

Greater Mumbai. So the purpose of this study is not to establish the precision of data or its

explanation.[86]

The aim of the study was to invite the consideration toward the transparency of FCA

framework. This will also support the municipal authorities with identification,

Literature Review on Solid Waste Management

13

ascertainment and cost control to steer towards strategies that should support optimal

distribution of resources.

(3) R. K. Kaushal & others at I.I.T., Delhi (2012) reviews the current practices of

assessing and projecting of MSW and highlights their limitations.

In the said paper, there was an effort has been made for the study of the changing

trends of quantity and characteristics of MSW. It also suggests finding its impact on the

performance and capacity planning of recovery – recycle, compost, incineration and landfill

facilities. The altering pattern of waste composition emphasises the importance of

segregation for successful operation of waste management facilities.[72]

Municipal authorities shall maintain the waste management services in such a

manner that unhygienic and unsanitary conditions will not be created. The altering need for

the suitable waste management technologies with respect to the changing pattern of the

waste generation shall be highlighted. This may help the urban local bodies accountable for

MSW management in preparing more efficient plans.[72]A new survey shall be carried out on

the generation and characterization of MSW in India. A large number of MSW samples are

to be collected and analysed to obtain more reliable results as MSW is heterogeneous in

nature.

2.3 Literature review on Design and FEA of CLD

(1) M Osman Abdalla & Others, Malasiyaat IJERA (2013)[50]suggested that the small

sized opening ports of the cylinders resist the flow of discharged oil and because of that the

piston motion is slowed down. This will lead to a lot of heat generation and energy loss

within the actuators. This study had investigated and analyzed the potential of reducing the

hydraulic resistance and rising efficiency of the hydraulic actuator.

Conventional hydraulic cylinders are simulated in FLUENT. The projected system is a

four ports hydraulic cylinder fitted with a novel flow control valve. Results show that the

small outlet ports are the sources of energy loss in hydraulic cylinders.[50]

. An innovative hydraulic system was projected as a solution to relieve the hydraulic

resistance in the actuators. The proposed system is of four ports cylinder and simulated.

Results show that the hydraulic resistance is extensively reduced and the proposed cylinders

run faster than the conventional cylinders. It gives better performance regarding the piston


14

speed and energy savings. This hydraulic system can be used in all engineering and portable

applications.

(2) P.J. Gamez – Montero& etc. at Elsevier (2009) presented an analytical method

based on misalignment to calculate the load capacity of an actuator by determining the

critical load (buckling) and limit load (yield stress). [26]

Results of maximum load capacity, imperfection angle and experimental work are also

presented for a specific unit of actuator.

From the result author found that Imperfection angle and a 5% of wear on the guide

ring reduce the cylinder load capacity by approximately 10% for the tested actuator.Results

of imperfection angle show that the effect of fluid compressibility reduces the imperfection

angle between rod and cylinder tube. [26] Moreover, wear of the guide rings has a major

influence on the imperfection angle than that of the radial deformation owing to the

internal fluid pressure.

(3) Mouleeswaran SENTHIL KUMAR & Sabapathy VIJAYARANGAN (2007)[57]

represented static and fatigue analysis of steel leaf spring – composite multiple leaf spring

made up of glass fibre reinforced polymer using life data analysis.

The dimensions of a present conventional steel leaf spring of a light commercial

vehicle are taken in to consideration and are verified by design calculations. Static analysis

of 2‐D model of conventional leaf spring is also performed using ANSYS 7.1 and matched

with the experimental results. [57]

Using E glass – Epoxy unidirectional laminates, same dimensions of conventional leaf

spring are used to manufacture a composite multiple leaf spring. The stiffness, load carrying

capacity and weight of composite leaf spring are compared with that of steel leaf spring

analytically and experimentally.

Stresses and deflections are the design constraints. Finite element analysis with full

bump load on 3‐D model of composite multi leaf spring is done using ANSYS 7.1.After that

the analytical results are matched with experimental results. Fatigue life of steel leaf spring

and composite leaf is also predicted.[57]

Literature review on Design and FEA of CLD

15

Fig.2.1 Variation of experimental stress of steel Fig.2.2 Load‐deflection curves for steel and composite leaf Springs [57] and composite leaf springs[57]

If composite leaf spring is compared with the conventional steel spring then, the

composite leaf spring is found to have 64.95 % higher stiffness, 67.35 % lesser stress, 126.98

% higher natural frequency and68.15 % weight reduction than that of existing steel leaf

spring. It is also concluded by the researcher that fatigue life of composite is more than that

of conventional steel leaf spring.

(4) Tae – GuKIM and others (2010) performed 3D modelling and FE analysis to classify

the correct and incorrect installation procedures of link chain. In the same context factors

are also identified which can lead to disastrous failures because of the incorrect installation.

This study utilized 3D modelling and finite element analysis of the chains which are used to

operate the crane. Based on the analysis, assessed the effect of different load cases on

structural stiffness and rigidity. Three dimensional interactive application programs (CATIA)

was used for 3D modelling of the chain and commercial finite element analysis software,

ANSYS was used for this analysis.[91]

(A) Correct installation: tensile load[91] (B) Incorrect installation: bending load[91]

Fig.2.3 The state of loading condition with respect to the installation of crane chain.


16

Using the ANSYS FEA software, the stress distribution was calculated under the condition of

axial tensile load acts on the chain. The stress distribution was also analyzed when the

bending load was applied to the chain. The results show that the maximum Von‐Mises stress

is 455 MPa from ANSYS under axial tension. While the bending moment is applied, the

stress is 1,236 MPa. Allowing for the case of 5 tons of tensile load, the analysis result

computed from FE analysis is approximately 460 MPa, which is lesser than that of the yield

strength of the material. On the contrary, when 5 tons of bending load is applied, the result

amounts to approximately 1,180 MPa, nearly reaching the ultimate strength (1,204 MPa).

Also the result shows that the stress magnitude occurred by a bending force is 2.5 times

greater than that of a plain tensile load. From the results, it was found that the magnitude

of stress is severely dependent on the stress state acting on the fracture surface.[91]

(5) Khaled Al – Fadhalahand others at Elsevier (2010) studied the failure of grade 80

alloy steel chain links at the weld during hauling operations of heavy – weight. Optical

emission spectrometry is the process of chemical analysis performed on the chain. The

results of this analysis indicate that some of the alloying elements such as Ni, Cr and Mo are

present with lesser concentrations than usually required by ASTM standard. Because of that

there was a decrease in the hardness and hardenability of the base metal and the weld, as it

was indicated by hardness test. The base metal and the weld were also inspected by tensile

testing, showing strength of nearly 800 MPa. Unlike the ductile base metal sample, the weld

sample failed in a brittle manner at a very low strain value of 0.05. The weld brittleness is

explained by metallographic examination, which indicated that welding defects are present

in the weld. It is also consisting of internal and external cracks, where the latter are found in

the weld fins near the weld interface. Weld inhomogeneity was confirmed by the presence

of two modes of final fracture; namely ductile fracture at the weld centre, and brittle

behaviour at the outer circumference of the weld specifying improper post welding heat

treatment.[38]

Literature review on Design and FEA of CLD

17

Fig.2.4 Tensile stress–strain behaviour of the Fig.2.5 Mating fracture of surfaces at Grade-80 steel chain[38] welded portion[38]

It is also found that daily – repetitive heavy towing loads had produced high cyclic

stresses in the chains. Fatigue primarily nucleated at the exterior cracks of the weld fins, and

later propagated to the inside of the links until sudden fracture occurred.[38] In addition to

the manufacturing defects of the weld, simple fatigue analysis and evidence of large

fracture area in the failed link indicate that failure by overloading was a major cause to the

short fatigue life. In general, the results provide a confirmation of lack of some significant

alloying elements, welding defects and improper post weld heat treatment of the chain

links. This leads to partial contentment of the ASTM standard requirements for Grade–80

steel chains.

(6) Vinkel Arora and others (2011): In the work presented, single leaf spring is

modelled using dedicated modelling software CATIA. Here the author has also considered

various eye design and the stresses induced in the leaf spring. As eye end plays active role

during application of leaf spring i.e. eyes have the critical areas where the most stresses

induced in a leaf spring. So by changing the eye design, stresses may be reduced. So two

different types of eye design for leaf spring analysis were considered. These two types eye

design are (1) Standard eye and (2) Casted eye.[98]


18

1) Standard eye 2) Casted eye

Fig.2.6 Different types of eye design used for leaf spring [98]

This work comprises of design and analysis of single leaf spring under static loading

conditions. The 3D model is prepared in CATIA and then CAE analysis is performed using

ANSYS – 11. From the results obtained from ANSYS, discussions have been made and it will

be concluded that:

Table.2.1 Parameter comparison between standard eye and casted eye[98]

Parameter Standard Eye Casted Eye Variation

Load 50 N 50 N Nil

Deflection 109.62 mm 115.61 mm 5.4%

Von Mises Stress 773.34MPa 750.08MPa 3.0%

Normal Stress 654.12MPa 783.51MPa 19.08%

Factor of Safety Maximum – 15 Maximum – 15 Nil

Minimum‐0.381 Minimum‐0.331 13.1%

1) As shown in above table 2.1, when similar load is applied to standard and casted leaf

spring equivalent stress reduction of 3.0% and increase in deflection of 5.4% is achieved. As

maximum stress induced in the spring material is below the yield stress therefore it is

concluded that casted eye is also safe under given loading conditions.

2) It is also observed that as the minimum factor of safety is reduced by 13.1% in case of

casted eye, the area under minimum factor of safety will fail earlier in case of casted eye.

Hence casted eye is not recommended for the use. [98]

Literature review on Optimization of hydraulic cylinders

19

2.4 Literature review on Optimization of hydraulic cylinders

(1) D. J. Wilde in the subject of “Monotonicity and dominance in optimal hydraulic

cylinder” at ASME (1975) stated that monotonicity and dominance were used to find

general principles for designing hydraulic cylinders optimal for a wide class of objective

functions and stress conditions.[102] The design method, although guaranteed to give the

optimum design, requires no knowledge of optimization theory by the designer. Optimal

cylinders should be designed for minimum force. Only two designs can be optimal—one

with maximum pressure and minimum wall thickness; the other with maximum stress.In the

former case, the design is retained if and only if the stress is less than allowable. Otherwise,

a one‐variable search in a restricted interval is needed. The results suggest the potential

importance of monotonicity and dominance in identifying the critical constraints in a design.

(2) Nestor F. Michelena and Alice M. Agogino in the subject of “Multiobjective

Hydraulic Cylinder” at ASME (1988) states that The limitations of the repeated calculations

in Pareto optimization have been countered by the complexity reductions possible by

application of monotonicity analysis and the Karush‐Kuhn‐Tucker optimality conditions. The

analysis applied to a multiobjective formulation of a hydraulic cylinder optimal design

problem allows parametric curves for tradeoffs between three objectives representing

material costs, lifetime and operating costs. The author had also stated that “If one wish to

minimize cross‐sectional area, a hydraulic cylinder designer should never design for a force

above the minimum required and should always design for the minimum allowable

thickness.”[62]

(3) D. J. Wilde in the subject of “THE MONOTONICITY TABLE IN OPTIMAL

ENGINEERING DESIGN” (2007) states that monotonicity analysis has been established helpful for

identifying which combinations of constraints might be active at an optimum. To make the

Monotonicity Table, detailed modeling is not required. The results are valid for any objective or

constraints having the same monotonicities even though coefficients and exponents may be

different. Consequent calculations have fewer degrees of freedom than it may be possible in

the original formulation. Since all local optima are generated, finding the global optimum is

guaranteed.[103]

In every example a simple design procedure involves evaluating at most two cases,

was found without information of the functions other than their monotonicities. Even when

the method does not solve the problem entirely, it can lead to simplifications significant

enough to justify its use on any optimal engineering design problem.


20

(4) M. BALACHANDRAN& J. S. GERO in the subject of “A COMPARISON OF THREE

METHODS FOR GENERATINGTHE PARETO OPTIMAL SET” (2007) describes and compares

three approaches for solving design optimization problems with multiple conflicting

objectives. The three techniques are described in detail and then applied to an example

which demonstrates how information is accumulated which leads to a logical and efficient

multi criteria optimal design. The techniques employed (weighting, Non inferior set

estimation and constraint methods) are compared to each other by considering their

computational efficiencies and their abilities to produce an approximation of the Pareto

optimal set.[9]

The constraint method shows a precise and appealing feature that it will always

create the shape of the whole Pareto set. By distinction, the weighting and NISE method will

extent only the rounded portion of the Pareto set.[9] Furthermore, the constraint method

provides absolute control of the spacing and exposure of the Pareto set.

(5) ALICE M. AGOGINO & ANN S. ALMGREN at Taylor & Francis (2015) states The

results of the qualitative and functional levels of computation in the *SYMON‐SYMFUNE

programs give strong insights into the mathematical structure of optimization problems and

can eliminate the need for further numerical analysis. The symbolic results provide useful

assistance at the numerical level in generating parametric curves for applications in multi‐

objective optimization, sensitivity analysis, and parametric, probabilistic or fuzzy design.

These powerful techniques for integrating qualitative reasoning and symbolic computation

in SYMON and SYMFUNE typically outperform human reasoning in reducing the complexity

of optimal design problems.[6]

The results of the qualitative and functional levels of computation in the SYMONSYMFUNE

programs give strong insights into the mathematical structure of optimization problems and

can eliminate the need for further numerical analysis. The symbolic results provide useful

assistance at the numerical level in generating parametric curves for applications in multi‐

objective optimization, sensitivity analysis and parametric, probabilistic or fuzzy design.

*SYMON – SymbolicMONotonicity Analysis,

SYMFUNE – SYMbolicFUNctionalEvaluation [6]

(6) P. PAPALAMBROS & D. J. Wilde at ASME publication (1979) states that

Monotonicity analysis can assist in both modeling correctly and solving for global optimality.

It can guard the designer against the pitfalls of numerical optimization methods and guide

Literature review on Optimization of hydraulic cylinders

21

the improvement of the original model. It leads to strikingly simple design procedures

guaranteed to give the global optimum without complicated iterative calculations. Since it

relies on the interplay of the engineering with the mathematical properties of a proposed

design, it is well suited to problems of engineering design.[67]

2.5 Performance Problem Identification through Survey

Gujarat Urban Development Corporation (GUDC) working under Government of

Gujarat had carried survey at different 161 ULBs across the state for smooth functioning and

improvement in different aspects of solid waste management system. This survey and

detailed analysis of reports were carried out by the team of technical experts. Format for

the survey of Container Lifting Device (CLD) is kept in ANNEXURE – II, it covers various

parameters which affects on the performance of CLD. It was prepared in consultation with

technical approval group formed by GUDC under Government of Gujarat. This survey

reports focused on various critical parameters like quality of hydraulic hoses used, type of

hydraulic cylinders assembly used, proper fittings of hydraulic oil pipe and joints fittings of

hydraulic lines etc. related to CLD.

After the detailed survey carried out for CLD with manufacturers, contractors,

operators, drivers, technicians, technical staff of ULBs, supervisors following problems are

found while using CLD.

While transportation of container in container lifting device due to immediate braking

and vibration due to road obstacle, there are chances to movement of container from

its position. This may damage the other parts of CLD.

During loading and unloading, container only supported by hoist chain. So it is free to

oscillate and chances to strike with hydraulic cylinder, hydraulic oil supply pipes etc. and

may damage the same.

Generally during operation of container lifting device there is no proper ground level at

storage depot station and dumping site. So there may be chances of unstable or

pitching of vehicle due to lack of supporting structure.

Hydraulic oil supply system from hydraulic motor to cylinder through pipe network is

not properly placed, fixed and positioned. Due to that in short running time it may be

damaged and leaked.

Due to improper design of CLD without proper side and front wall, waste from overfilled

container may fall on the road during travelling.


22

2.6 Research Gap

After studying many research papers regarding the work to be carried out, some

research gap found out and some of things were found out this has been less focused.

Mainly three types of research papers were studied like review papers regarding solid waste

management, design, vibration and FEA of different components used in CLD and

optimization of hydraulic cylinders as most critical part of CLD.

Solid waste management in India has every chance of improvement at all stages like

collection, transportation, processing and disposal. Out of these here collection and

transportation stage were selected.

There is also lots of scope of technical improvements required to be adopted for the

existing container lifting device used with tractor for SWM work.

Identified the problems/difficulties faced with the existing design / specifications of the

container lifting device.

Identified its important components and significant parameters affecting performance of

the container lifting device (CLD).

Design of critical engineering parts used in CLD is to be done & check i.e. hydraulic

cylinder assembly, cross‐rod with two arms, different pin joints, rear mechanical jack

assembly, leaf springs and chains.

Studied dynamic behaviour of moving parts and analyse its performance.

Establish the improved design or optimize the current design of the container lifting

device that will overcome most of the problems related to the performance parameters.

Identified the most critical engineering component of container lifting device i.e.

hydraulic cylinders.

Using FFT analyzer vibrations of hydraulic cylinder are to be measured during actual

working conditions.

Optimization hydraulic cylinder is to be carried out using both single and multi‐objective

optimizations based upon the requirements.

23

CHAPTER 3

DESIGN METHODOLOGY

3.1 Introduction

Present research work focused on analysis and design of Container lifting device (CLD) used

for lifting and lowering 4.5 cubic meter container operated by tractor. Following are the

components used in CLD.

1. Hydraulic cylinder assembly

2. Pin joints

3. Cross‐ rod

4. Rear mechanical jack

5. Welded joints

6. Locking pins

7. Leaf springs

8. Hoisting chains

This chapter describes the design philosophy of above mentioned components. It was

observed after analysis of survey reports and literature review that the modifications are

required in the design of CLD to make it compatible with tractor driven instead of truck

mounted. Hence the design procedure for various components is outline in this chapter.

3.2 Design of Hydraulic Cylinder

Hydraulic cylinders are actuation devices that convert the hydraulic energy of

pressurized fluids into the mechanical energy needed to control the movement of machine

linkages and attachments.

Hydraulic cylinders are used at elevated pressures and create large forces and

accurate movement. For this reason they are made of strong materials such as steel and

designed to withstand huge forces. The fluid pushes against the face of the piston and

produces a force.

Fig. 3.1 Forces acting on hydraulic cylinder

Design methodology

24

The force produced is given by the formula:

F = P A ………..Eq. (3.2.1)

Where, P is the pressure in N/m2

Let ‘A’ be the full area of the piston and ‘a’ be the cross sectional area of the rod. If

the pressure is acting on the rod side, then the area on which the pressure acts is as (A ‐ a).

3.2.1 Material used for Hydraulic Cylinder

To cope up with higher stress either wall thickness has to be increased or material

with higher allowable stress has to be selected. To reduce overall size mostly material with

higher allowable stress are selected such as plain carbon steel or low or medium alloy steel.

But these materials are difficult to weld. If working environment of hydraulic cylinder is of

corrosive in nature, then use brass or stainless steel seamless pipe. If there is no limitation

about bore size, and hydraulic fluid is also not corrosive, then for such condition seamless

pipe of low to medium carbon steel with carbon percentage in between 0.15 to 0.35% is

used. [48]

Mostly ground and hard‐chrome plated rod of C40, SAE 1045, DIN CK 45 and JIS S45C

grade material and in standard size is available in market and widely used for piston‐rod.

Piston‐rod can also be made from mild steel, alloy steel, stainless steel, cast‐iron (for larger

size and short length of piston‐rod under compressive load) etc. depends on application.

Now a days medium carbon steel SAE 1045 grade in ground and coated condition are also

available, which are much better than C40 grade piston rod. [49]

3.2.2 Theoretical Design of Cylinder Tube

Cylinder tubes are divided in to two categories.

1) Thin Cylinder Tube

2) Thick Cylinder Tube

When ratio of cylinder bore diameter to wall thickness is bigger than 10 then it is

called thin cylinder and when it is equal to or less than 10 then it is called thick cylinder.

Equations to calculate wall thickness, and assumption made to use them are different. But

in high pressure and load application only thick cylinder tube is used, which is able to sustain

high pressure.

Design of Hydraulic Cylinder

25

3.2.3 Design of Thick Cylinder

In case of thin cylinder, stress assumed to be uniformly distributed over the section

of wall, but In case of thick cylinder stress distribution are as follow.

Fig. 3.2 Thick cylindrical shell stress distribution over its thickness[48]

Maximum radial stresses are generally equal to the internal pressure, and it is

maximum at inner surface of cylinder.

σr (max) = ‐P

For calculating tangential stress following four equations are used.

1) Lame's equation

12

i t

t

d Pt

P

………………………Eq. (3.1.1)

,

t

where t thicknessof cylinder

Tensile stressof thecylinder material

Lame's equation is used for designing cylinder of brittle material and it depends on

maximum‐ stress theory of failure, and could be used for open as well as closed cylinder.

2) Brinies’ equation:

Brinies’ equation depends upon the maximum strain theory of failure. That is failure

will occur when the strain reaches a limiting value. According to this theory the wall

thickness of cylinder is.

1

12 1

ti

t

Pdt

P

……………...........Eq. (3.1.2)

Where, 'Poisson s ratio

This equation is generally used for open‐end cylinder made of ductile material, such as gun‐

barrels.

Design methodology

26

3) Clavarino's equation:

This equation is similar to Brinies’ equation, but applies to closed‐end cylinder made of

ductile material. According to this equation the thickness of a cylinder,

1 2

12 1

ti

t

Pdt

P

…………….…….Eq. (3.1.3)

4) Barlow's equation:

This equation is generally used for high pressure oil and gas pipes. According to this

equation the thickness of a cylinder,

2

i

t

Pdt

……………….………………….Eq. (3.1.4)

3.3 Theoretical Design of Piston and Piston Rod

Piston rod transfers the force developed at piston to work piece. This force may be pushing

or pulling. Piston rod is designed to transfer these forces along its central axis. It is not

designed or expected to take any side bending load. Then the cross‐section required to

transfer force within safe stress limit can be calculated using basic simple formula such as.

2

4 tFt

d ……………………………..…......Eq. (3.3.1)

And for piston design equation is given like…

2 2

4 t

i

FP

D d

……………….……….…Eq. (3.3.2)

3.4 Design of Cross‐Rod

In container lifting device (CLD) cross‐rod is used to support the load by container loading

and transmit motion from the cylinder to container for loading and unloading. In design of

cross‐rod, it is consider as a fixed beam supported at two ends with two point load at a

same distance from ends. Here beam is considered as a hollow pipe section.

Fig. 3.3 Cross‐rod fixed at both ends

W = Ap

C = Rati

L = Tota

Now, d

like belo

Flexure

Shear st

Maximu

3.5 Des

Mechan

load at

jack is u

contain

station

not onl

proper

simply

column

3.5.1 Lo

Centric

allowab

Eccentr

the bea

ply load, do

io of inner t

al length of

ue to applie

ow…

bending st

tress

um principa

sign of Rea

nical Jack is

some heig

used to sup

ner is not do

and dump

y support t

design of r

consider a

n is less than

oading Loca

loading: T

ble stress is

ric loading:

am load will

o = Outer D

to outer dia

Beam, a = D

ed load W a

ress

2 2

4

1

W

D C

al stress 1

r Mechanic

s a device

ght. In tract

pport the lo

one with pr

ing site. So

the load bu

rear jack is

as a short

n 50 with ec

ation

The load is

determined

The load is

proceed in

iameter, di

ameter, M =

Distance be

at distance ‘

3 4

32

1

M

D C

………

2 2

cal Jack of C

which can

tor driven c

ad. Genera

oper groun

o, at that ti

ut also stab

necessary.

column be

ccentric loa

s applied a

d from stre

s offset from

n the colum

27

i = Inner Dia

= Bending m

etween fixed

‘a’ from bot

……………

……………………

22

……

CLD Model

support an

container li

ally loading

nd level like

me mechan

ilize the ve

For design

ecause slen

ding condit

at the cent

ngth (P/A) o

m the cent

n. This offse

ameter

moment

d end and a

th end gene

……..Eq. (3.4

………Eq. (3.4

……..Eq. (3.4

nd also rais

ifting devic

and unload

at storage

nical jack is

hicle. That’

of rear jac

nderness ra

ion.

Fig.3.4 3D

troid of th

or buckling.

roid of the

et causes be

applied poin

eral stress g

4.1)

4.2)

4.3)

se the

e rear

ding of

depot

s used

’s why

k, it is

atio of

model of m

e cross sec

.

cross sectio

ending alon

Design of C

nt load

generated is

mechanical r

ction. The

on because

ng with axia

ross‐Rod

s given

rear jack

limiting

e of how

l stress.

Design methodology

28

Fig. 3.5 Eccentric loading of column

The eccentricity causes bending stresses by a moment of value equal to P x e. Within the

elastic range (linear stresses) it can be superposition or add up the normal and bending

stresses:

x a b

p Myf f f

A I ……………………Eq. (3.5.1)

(A)

The resulting stress distribution is still linear and the N.A. moves (if there is one).

(B)

Fig.3.6 Resulting stress distribution (A) without N.A (B) with N.A[49]

The value of e (or location of P) that causes the stress at an edge to become zero is at the

edge of the kern. As long as P stays within the kern, there will not be any tensile stress.

Limit Criteria

1

. .a b

a b

f f

F F F O S Interaction formula…………..Eq. (3.5.2)

A) Eccentric loading of column with eccentricity (e)

B) Eccentric load right with Tension in left

3.5.2 De

Two pa

body o

welded

require

followin

2L = len

3.5.3 De

Under

with do

d = diam

2

p

A

3.6 Des

wheele

semi el

steel of

tie hole

road ir

energy

energy

esign of we

arallel fillet

f CLD mod

portion is

d to supp

ng calculatio

ngth of weld

esign of loc

loading co

ouble shear

meter of pin

2

2 p

d

sign of Leaf

A leaf spri

d vehicles.

liptical leaf

f rectangula

es are provid

The leaf sp

regularities

is stored i

storage ca

elded joint

welds is p

del. In load

shear stre

ort and su

on.

d, S = weld

cking pin

ndition pin

area.

n

(∵A =πd

f Spring

ing is a sim

It is also on

f spring (SE

ar cross‐sec

ded at eithe

pring is be s

s by means

in spring a

apacity of

rovided to

ing conditio

ess. So, we

ustain the

size

τ = shear

0.707

2SL

n is subject

d /4)…….Eq

mple form

ne of the ol

LS) it takes

tion. The ce

er end for a

supposed to

s of variatio

s strain en

a leaf sprin

Desi

29

join the re

on stress p

lded size a

load is de

r stress,

7 p

L………….E

ted to shea

q. (3.5.4)

of spring,

dest forms

s the form

entre of the

ttaching to

o absorb th

ons in the

ergy and t

ng ensures

ign of Rear

ear jack to

produce in

and length

erive from

Fig.3.7 Colu

Eq. (3.5.3)

ar load

Fig

commonly

of springin

of a slende

e arc provid

the vehicle

he vertical v

spring def

then releas

s a more a

Mechanical

umn welded

g.3.8 Shear

y used for

ng. Sometim

er arc‐shape

des location

e body.

vibrations a

flection so

ed slowly.

amenable s

l Jack of CLD

d to body o

r of locking

the suspen

mes referred

ed length o

n for the axl

and impacts

that the p

So, increas

suspension

D Model

of CLD

pin

nsion in

d to as a

of spring

le, while

s due to

potential

sing the

system.

Design methodology

30

According to the results of the studies made the material with maximum strength and

minimum modulus of elasticity in the longitudinal direction is the most appropriate material

for a leaf spring. Fatigue failure is the predominant mode of in‐service failure of many

automobile components. This is due to the fact that the automobile components are

subjected to variety of fatigue loads like shocks caused due to road irregularities traced by

the road wheels, the sudden loads due to the wheel travelling over the bumps etc. The leaf

springs are more affected due to fatigue loads, as they are a part of the unspring mass of

the automobile.

The leaf does the following functions:

Supports the chassis weight.

Controls chassis roll more efficiently‐‐high rear moment centre and wide spring base.

Controls rear end wrap‐up.

Controls axle damping.

Controls lateral forces much the same way a hard bar does.

Controls braking forces.

Regulates wheelbase lengths (rear steers) under acceleration and braking.

Fig.3.9 Semi‐elliptical leaf spring [21]

fn = number of extra full‐length leaves

gn = number of graduated‐length leaves including master leaf

n= total number of leaves

b = width of each leaf (mm)

t = thickness of each leaf (mm)

12L = Length of span or overall length of the spring

Design of Leaf Spring

31

l = Width of band or distance between centres of U‐bolts. (It is the ineffective length of the

spring)

L = length of the cantilever or half the length of semi‐ elliptic leaf spring (mm)

F = force applied at the end of the leaf spring (N)

fF = portion of F taken by the extra full‐length leaves of the leaf spring (N)

gF = portion of F taken by the graduated‐length leaves of the leaf spring (N)

E = Modulus of Elasticity of Material

Maximum Bending Moment in the Centre (M) = FL

Effective length of the spring is…

2L = 12L ‐ l (When the band is used) ……………………….Eq. (3.6.1)

2L= 12L ‐ 2

3l (When U‐bolts are used) ……………………Eq. (3.6.2)

When there is only one full‐length leaf (i.e. master leaf only), then the number of leaves to

be cut will be n and when there are two full length leaves (including one master leaf), then

the number of leaves to be cut will be (n‐1) if a leaf spring has two full‐length leaves, then

the length of leaves is obtained as follows:

Length of smallest leaf of the leaf spring = effective length

1n + Ineffective Length …Eq. (3.6.3)

Length of next leaf of the leaf spring = effective length

1n 2 + Ineffective Length ..Eq. (3.6.4)

Similarly,

Length of (n‐1) leaf of the leaf spring = effective length

1n n 1 + Ineffective Length

…...Eq. (3.6.5)

The nth leaf will be the master leaf and it is of full length. Since the master leaf has eyes on

both sides, therefore

Length of Master Leaf of the leaf spring = 12L + 2d t …………………………………Eq. (3.6.6)

Where d = Inside diameter of eye

t = Thickness of master leaf

The relation between the radius of curvature(R) and the camber (y) of the spring is given by:

212y R y L

………………………………..Eq. (3.6.7)

Where L1 = Half span of the leaf spring.

Design methodology

32

The maximum deflection (δ) of the spring is equal to camber (y) of the spring.

We know,

fF + gF = F ……………………….......................Eq. (3.6.8)

Ff =f

f g

3n F

3n 2n …. (Force taken by no. of nf master leaf) ..............Eq. (3.6.9)

Fg= g

f g

2n F

3n 2n …. (Force taken by no. of ng graduated leaf) …….Eq. (3.6.10)

bg = 2

f g

12FL

bt 3n 2n ….(stress induce in ng graduated leaf) …….Eq. (3.6.11)

bf = 2

f g

18FL

bt 3n 2n ….(stress induce in nf master leaf) ………..Eq. (3.6.12)

y = 3

3f g

12FL

Ebt 3n 2n …. (Deflection at the end of the spring) ……Eq. (3.6.13)

Multi‐leaf springs are designed using load stress and load deflection equations. The

standard dimensions for the width and thickness of the leaf section are as follows:

Nominal thickness (mm): 3.2, 4.5, 5, 6, 6.5, 7, 7.5 8, 9, 10,11,12,14, and 16.

Nominal width (mm): 32, 40, 45, 50, 55, 60, 65, 70, 75, 80, 90, 100 and 125.

3.6.1 Materials for leaf spring

Plain carbon steel, Chromium vanadium steel, Chromium‐ Nickel‐ Molybdenum steel,

Silicon‐ manganese steel, are the typical materials that are used in the design of leaf springs.

Like steel conforming to 55si7, 60si7, 65si7, ASTM grade A689. Other spring steel having

similar hardenability, toughness and physical property are considered for manufacture of

leaf spring.

33

CHAPTER 4

DESIGN CALCULATIONS

4.1 Design calculations – Anchor pin for 4.5 cubic meter container

Selection of container to be used with tractor operated container lifting device is

based on volumetric capacity. Size of container is minimized based on operation and

performance criteria.

After detailed study and analysis 4.5 cubic meter container is selected for the

operation.

It is to be fabricate with Mild Steel sheets with four top openings and one rear

lockable tailgate with heavy‐duty hinges as per below mentioned technical

specifications and drawing.

The lifting hooks/anchor pins shall be integrated within the frame and be capable of

taking the specified load.

Design calculations

34

Fig. 4.1 Drawing of 4.5 cubic meter container used for CLD

Design calculations – Anchor pin for 4.5 cubic meter container

35

Fig. 4.2 Drawing of anchor pin used in 4.5 cubic meter Container

Bending moment equation in this case

,

b

Where

Bending stress

M Bending moment

Y Perpendicular distance between point of force and neutral axis

I Moment of inertia for circular cross section

2

250 /

2yt

d Diameter of pin

S Yield strength of material N mm

FOS Factor of safety

Since there are four hook in the container for lifting purpose, so we divide the

maximum load of 5 ton by four to get the maximum load at single hook of container as

1.25 ton (12500 N).

Thu

Usin

Wh

4.2 Des

used fo

anneale

machin

(BK+S),

tube en

and tra

mechan

hydraul

us total forc

ng equation

ere,

sign Calcula

ST52 is low

r Hydrau

ed or norma

ability and

with yield

nds capped

ST52 Hyd

nsport lifti

nical tools

lic pressure

e acting at s

n (4.1.1)

ations of Hy

w carbon ste

lic Cylind

alized. Hydr

weldability

strength ov

for protect

raulic Cylin

ing equipm

and equip

e, pneumati

single hook

ydraulic Cyl

eel grade, e

er Tubes

raulic Cylind

y property.

ver 520 MP

ion. [93]

der Tubes

ment, wast

pment, com

c cylinder, o

36

k = 12500 N

linder

xcellent we

. Normall

der Tubes S

Supplied

Pa. The insid

s, applicab

te disposal

mpressors

oil pump ba

= 1.25 Ton

eldable, the

ly supplie

ST52 and ST

seamless,

de diamete

ble to mac

transport,

and earth

arrel.

e most wide

ed stresse

T52.3 are fo

cold drawn

er is honed

chinery, h

food proc

moving e

Design calc

ely used ste

ed relieve

ound with e

n & stress

and oiled w

oists, auto

cessing equ

quipment

culations

el grade

ed and

excellent

relieved

with the

omotive

uipment,

etc. like

Design Calculations of Hydraulic Cylinder

37

2

2 2

2

Pr 160 200 /

200 9.81Pr , / 19.62 /

100, , Pr ,

5000 9.81 19.624

56.41 63

, 63 .

i

i

essure Range Considered to kg cm

essure p N mm N mm or MPa

Force F Area A essure p

d

d mm mm

So thediameter of piston rod is taken mm standard

The inside diameter is honed and oiled with the tube ends capped for protection. ISO

standard cylinder bore size is 32, 40, 50, 63, 80, and 100 mm.

93

.

Hydraulic Cylinder Tube ‐ ST52

Compressive/ Tensile yield strength=250MPa

Ultimate tensile strength, = 520MPa

520104

5

ult

ultt

ForHydraulic cylinder tubematerial is steel

MPaFOS

' : .(3.1.1)

63 104 19.621 1

2 2 104 19.62

6.63 8

,

, 1.75.

1.75 1.75 6

t ii

t i

O

i

O i

By Lame s equation asper Eq

pdt

p

t mm

By taking the ratio of externaldiameter of cylinder d to

the internaldiameter of cylinder d is

d d

3 110 .mm

The piston rod size available with cylinder bore 110 mm is 63 mm.

Design calculations

38

2

cr 2

cr

2

Buckling calculation for piston rod :

According to Euler 's equation,

n. .E.AP ........Eq.(4.2.1)

LK

where, P Critical load (N) 5 ton 5000kg 49050 N

n end fixitycoefficient

n 1 if both ends hinged

E 207000 N / mm

L 29

4

2

00mm

.dI d64K

.dA 44

By putting all these values, weget d 45mm,

but in our caseint ernaldiameter is63mm,hencesafein buckling also.

4.3 Design for pin joint

Cylindrical pin joints are used in container lifting device mainly at three interconnections

between CLD base to hydraulic cylinders, hydraulic piston rod to big link and big link to

trailer base. Cylindrical pin joints are used to connect different component of CLD and help

to transmit motion between them. To sustain load and for proper functioning of CLD model

it is necessary to design cylindrical pin joint.

(A) (B) (C)

Fig. 4.3 Cylindrical pin joints (A) base to hydraulic cylinders (B) hydraulic piston rod to big link (C) big link to trailer base.

For the design of the cylindrical pin, it can be fail in to double shear in loading condition.

Material selected for design of cylindrical pin is medium carbon steel.

Design for pin joint

39

P = 168000 N, y = 400 MPa, y = 400 * 0.58 = 232 MPa, F.O.S = 3

2

2

2

P P

A d

(∵A =πd /4)

2d = 2P

π y

FOS

d= 37.2 ≈ 40 mm

4.4 Design Calculation of Cross‐Rod

Fig. 4.4 Drawing of Cross‐rod used in CLD

3 4

,

,

32....

.. .(3.4

.1)1

bb

o

i

o

i

o

b

For hollow circular cross section

Where

Inside diameter of hollow shaft

Outside diameter of hollow shaft

Masper Eq

d C

dC

d

d

d

M Bending moment

24525 N

150 .

24525 150 24525 150 ( )

7357500 .

(

2

)

50

b

b

Assuming downward Load on pipe at a distance

of mm from fixed end from both side

We get force distance

M

N mm

Yield strength of mild steel MP and

M

a

...... .(4.4.1)

25083.3

,

33

3.3.1 4.4.1

0.8

115 =

ytb

i o

Eq

After solving equation and we get

Outer diameter of hollow circular shaft is

Inner diameter of hollow circular shaf

SMPa

FOS

d Ct dis

115 mm

92mm

Design calculations

40

For the design of cross‐rod material selected is structure steel St 30 with property as given

below...

y (Yield stress) = 170 N/mm2

W = 25000 N, L = 1830 mm, a = 115 mm

D = 115 mm, F.O.S = 3,

M = W a

Now critical point need to be consider for inner diameter calculation is fixe end, load point

and at centre point. But in this loading condition maximum bending moment is generated at

fixe end. By considering combine effect of bending stress and shear stress at fixe end,

maximum principal stress at fixe end should be less than yield stress.

As per Eq. (3.4.1), (3.4.2) and (3.4.3)

2 2

1 3 4 3 4 2 2

16 16 4

1 1 1

M M W

D C D C D C

(σ1= . .

y

f o s

)

By trial and error method final found value of ratio of diameter is…

C ≈ 0.8

So, inner diameter d = C D

= 0.8 115

≈ 90 mm

Therefore material thickness of cross‐rod is tc = 12 mm.

4.5 Design Calculation for Rear Mechanical Jack

Here, for design of the jack column structure steel plate of grade st 30 is selected with

following specification.

Ultimate strength = 340 MPa

Yield strength Fa or Fb = 170 MPa

p = 5 104 N

Design Calculation for Rear Mechanical Jack

41

Fig. 4.5 Cross‐section area of column section

Plate thickness t = 5 mm

Column Square size = a

Area of cross‐section A = 20a – 100

F.O.S = 3

Moment of Inertia, I = 44a a 10

12

By putting above value in Eq. (3.5.1) and eq. (3.5.2) we get following…

24

44

1 35.6 10

20 100 10

a

a a a

By trial and error method, we got value of a ≈ 100 mm.

For design of welded joint, size of weld ‘S’ is taken as 3 mm for plate thickness around 5 mm

and design stress is around 79 MPa for fillet welds from design data book. So, according to

eq. (3.5.3)

L = wel

0.707 p f .o.s

2τSη

= 280 mm

≈ 300 mm

By using eq. (3.5.4) diameter of locking key and swinging plate mounting pin is determine.

Here material for pin is medium carbon steel.

y 400 MPa

Design calculations

42

y 400 0.58 = 230 MPa

d 2 f.o.s

πτ

p= 20.38 mm

≈ 20 mm

4.6 Design Calculation of Leaf Spring for CLD Model

Here tractor driven lifting device may be used which will lift up to 4.5 cubic meter

containers. They can use containers of up to 4 to 4.5 cubic meters capacity, which will make

optimum use of the tractors. For the smooth and jerk free motion of CLD model while

transportation, it is necessary to design leaf spring. Here the weight of container when fully

loaded is around 2000kg and weight of CLD body is around 2000kg without considering

container.

So,

Total force on leaf spring = 4000*10 (take g= 10 N/mm2)

= 40,000 N

Because of road irregularity and bump instant jerk produces and stress induce at same load

is higher than normal and in this condition force acting on leaf spring is more than normal

so, while design we consider higher value of force is..

Total force on leaf spring = 40,000*1.5

= 60,000 N

Force at leaf spring eye F = 60,000/4

= 15,000 N

Material of the spring is 50 Cr 1 V 23 spring steel.

Yield stress y 180 kgf / mm2 (1800 MPa)

Young modulus E = 200 GPa

F.O.S = 2

Number of graduated‐length leaves including master leaf ng = 6

Number of extra full‐length leaves fn = 1

Total number of leaves n = 7

Width of each leaf b = 70 mm

Length of span or overall length of the spring 2L1 = 1000 mm

Distance between centres of U‐bolts l = 100 mm

Design Calculation of Leaf Spring for CLD Model

43

Effective length of semi‐ elliptic spring 2L = 933 mm [from Eq. (3.6.2)]

By using Eq. (3.6.12), thickness of leaf (t) is...

ft = f f g

18FL

bσ 3n 2n

yσσ

f .o.sf

= 11.55 ≈ 12 mm

By using eq. (3.6.11), thickness of leaf (t) is...

gt = g f g

12FL

bσ 3n 2n

yg

σσ

f .o.s

= 9.43 ≈ 10 mm

Here, tf > tg

Therefore t = tf = 12 mm

By using eq. (3.6.13), deflection of leaf spring is...

y = 3

3f g

12FL

Ebt 3n 2n

= 61.48 ≈ 62 mm

so, camber of leaf spring is taken more than deflection y because it prevent strike the upper

face of leaf to the vehicle chassis on over loading.

Camber δ = 80 mm

By using eq. (3.6.3, 3.6.4, 3.6.5), we can compute length of each leaf spring like…

Length of smallest leaf of the leaf spring L7 = (933/7) + 67

= 222 mm

Length of next leaf L6 = 378 mm

L5 = 538 mm

L4 = 690 mm

L3 = 844 mm

L2 = 1000 mm

By using eq. (3.6.6), Length of Master Leaf is…

= 1000 + π (20 + 12) × 2 (∵eye dia. d = 20 mm)

= 1201 mm

By using eq. (3.6.7), the radius of curvature(R) is…

R =

21L

δδ

2

= 1522 mm

Design calculations

44

4.7 Selection of Hoisting Chain Link for Container Lifting

The chain is one of the most familiar for hoist as well as one of the most useful of

mechanical device. It is made up of a series of links fastened through each other. Each link is

made of a rod of wire bent into an oval shape and welded at one or two points. The weld

ordinarily causes a slight bulge on the side or end of the link. The chain size refers to the

diameter in millimeter (mm) of the rod used to make the link. Simple terms used with chain

are given below.

Working Load Limit (WLL)

The "Working Load Limit" (rated capability) is the highest load that shall be applied in direct

tension to an undamaged straight length of chain.

Proof Test

The "Proof Test" (manufacturing test force) is a term designating the minimum tensile force

which shall be applied to a chain under a continuously increasing force in direct tension

during the manufacturing process. These loads are manufacturing integrity tests and shall

not be used as criterion for examination or design purpose.

Minimum Breaking Force

The "Minimum Breaking Force" is the least force at which the chain during the

manufacturing has been found by testing to break when a continuously rising force is

applied in direct tension. This test is a manufacturer's attribute acceptance test and shall

not be used as a criterion for service or design purposes.

Overhead Lifting

That process of lifting that would elevate a freely suspended load to such a position that

dropping the load would present a possibility of bodily injury or property damage.

Overload

Any static or dynamic load is in excess of "Working Load Limit."

Selection of Hoisting Chain Link for Container Lifting

45

Fig.4.6 Model of chain link

4.7.1 Materials for chain link

Carbon chain

The selection of the base steel is left to the verdict of the individual chain manufacturer

provided the steel meets the following criteria: Carbon, 0.35% max.; Phosphorous,

0.040% max.; and Sulphur, 0.050% max.

Alloy chain

The selection of chain and amounts of the alloying elements present in the steel are left

on the opinion of the individual chain manufacturer. Generally the steel used for chain

manufacturing meets the following composition: Carbon, 0.35% max.; Phosphorous,

0.035% max.; Sulphur, 0.040% max. Nickel must be present as an alloying amount (0.40%

min.), and at least one of the following elements must also be present as an alloying

amount: Chromium (0.40% min.) or Molybdenum (0.15% min.).

Stainless steel chain

The material used in manufacturing this chain shall be a 300 series austenitic stainless

steel.

Now, selection of an open‐link chain by using the following rule of thumb

SWC = 28 D

SWC = Safe working capacity in tons

D = chain link diameter/thickness in inches

Design calculations

46

For a 5/8 “diameter chain link by using above equation,

SWC = 8× (5/8)2 = 3.125 tons

Table 4.1 Grade 80 alloy chain standard by national association of chain manufacturer [100]

Now according to loading situation of container while operation, chain link size

should be selected within working load limit of 8200 kg. So, as per the standard of National

association of chain manufactures for welded steel chain

Inside length (max) L = 51 mm

Inside width (min) E = 24 mm

Material diameter D = 16 mm = 5/8”

Working load limit (max) = 8200 kg

Min breaking force = 322 kN = 32834.86 kg

So we select the chain link from above table 4.1 for grade 80 alloy materials with 16 mm

diameter, inside width 24 mm and inside length of 51.2 mm.

47

CHAPTER 5

MODELLING AND ANALYSIS OF CONTAINER LIFTING DEVICE

5.1 3D Modelling

The modelling has been performed on the Solid Works 2014 and then after the

analysis work has been performed on the ANSYS 15.0 version.

5.1.1 Salient Features of Modelling Software

Solid Works is a parametric and feature based solid modelling tool. It allows

converting the basic 2D drawing into a solid model using very easy modelling tools.

It creates a digital prototyping as opposite to the physical prototyping by integrating

2D drawings and 3D data into a single digital model.

It can quickly and easily create stunning renderings, animations and presentations,

which improve communication.

It can easily create and contribute production ready drawings for manufacturing

teams.

The automatic updating characteristic allows doing easy changes in model.

It also helps in improving visualization of the solid model and understanding its

drawing.

It also gives more flexibility in editing the solid model to imagine the effect of the

changes.

Define, group, and apply properties such as loads, constraints, and materials—

making it easier to accurately predict product performance.

5.2 Finite Element Analysis (FEA)

It is widely accepted method of accessing product performance without the need for

physical building and testing. It also shortens prototype development cycle times &

facilitates quicker product launch. FEA consists of a model of a material or design that is

loaded and analysed for specific results. It is used in new product design, and existing

product refinement.

Steps Required For Development of FEA Model

Steps required for development of finite element model are as under:

Modelling and Analysis of container lifting device

48

Assigning material and its properties to various parts.

Discretize and choose element types.

Choose a displacement function.

Derive the element stiffness matrix and equations.

Generate global or total equations from the element equations and introduce loads

and boundary conditions.

Solving for elemental strains and stresses and interpretation of the model

5.3 Transient Structural Analysis of Container Lifter Model in ANSYS

Transient structural analysis provides users with the ability to decide the dynamic

response of the system under any type of time‐varying loads. Unlike rigid dynamic analyses,

bodies can be either rigid or flexible. For flexible bodies, nonlinear materials can be

included, and stresses and strains can be output. Transient structural analysis is also known

as time‐history analysis or transient structural analysis. Transient structural analysis

encompasses static structural analysis and rigid dynamic analysis, and it allows for all types

of Connections, Loads, and Supports.

In a transient structural analysis, Workbench Mechanical solves the general equation

of motion:

[M]{Ẍ}+[C]{Ẋ} +[K]{X}={F(t)} ………Eq. (5.3.1)

Some points of interest:

Applied loads and joint conditions may be a function of time and space.

As seen above, inertial and damping effects are now included. Hence, the user

should include density and damping in the model.

Nonlinear effects, such as geometric, material, and/or contact nonlinearities, are

included by updating the stiffness matrix.

Transient structural analysis can be both linear and nonlinear. All types of

nonlinearities are allowed like large deformations, plasticity, contact, hyper elasticity and so

on. The transient structural analysis that specifically uses the ANSYS Mechanical APDL solver

also ANSYS Workbench offers an additional solution method of Mode Superposition to

perform linear transient structural analysis.

Transient Structural Analysis of Container Lifter Model in ANSYS

49

5.3.1 Steps for transient analysis in ANSYS Workbench

Create Analysis System: Select any dynamic analysis template like Transient Structural,

harmonic analysis etc. and drag it in to Project Schematic.

Define Engineering Data: According to types of material used in analysis, select material and

its property. Like linear material, isotropic material, temperature dependent material etc.

Attach Geometry: Virtual 3‐D model from any solid modelling software, for calling of this

solid model in ANSYS system it is need to convert in to IGES. OR STEP. File.

Define Part Behaviour: In a transient structural analysis, rigid parts are allow to use model

mechanisms that have particular motion, movement and relocation of loads between parts,

but stress and strain distribution is not of attention. The output of any rigid part is the

overall motion of that part and any force transferred via that part to the rest of the

structure. A point mass connected to the rest of the structure through joints is known as

“rigid” part. Hence in a transient structural analysis the only appropriate loads on a rigid

part are the acceleration and rotational velocity loads. Additional load may be applied to a

rigid part via joint loads.

Define Connections: All Contact, joints and springs are applicable in a transient structural

analysis. In a transient structural analysis, user can give number of joints and contacts

between parts according to application.

Apply Mesh Controls/Preview Mesh: An enough mesh concentration shall be provided on

contact surfaces to permit contact stresses to be distributed in a soft manner. Similarly,

adequate mesh density shall be provided for resolving stresses particularly at the areas

where stresses or strains are of concern. At these areas comparatively fine mesh are

required as compared to displacement or nonlinearity resolution. The mesh should be such

that it shall be able to capture the effects of the nonlinearities, if they are included. For

example, plasticity requires a sensible integration point density and consequently a fine

element mesh in areas with high plastic deformation gradients. To represent the peak mode

shape of interest in a dynamic analysis, the mesh shall be fine enough.


50

Establish Analysis Settings: For transient structural analyses, the basic controls are:

Usually slender structures are subjected to maximum deflection. If the transverse

displacement in a slender is more than 10% of the thickness that will be shown by the

maximum deflection.

Output controls allows specifying the time points at which results shall be available

for post processing. It is necessary to perform lots of solutions at midway time in a transient

nonlinear analysis. However as the size of all intermediate results file is so bulky and if all

the intermediate results are not required then it should be modified as per users’

requirement except for stress and strain.

Convergence criteria and other specific solution controls are modified using

nonlinear controls. There is no need to change the default values for using this control.

These can be modified as step basis.

Damping for the structure in a transient analysis shall be specified by damping

controls. The following damping controls are available for transient analysis:

1) Beta damping and

2) Numerical damping

In addition to these elements based damping for spring elements and material based

damping are also accessible for the transient structural analysis.

Data management setting allows saving specific solution files from the transient

structural analysis to be used in other analyses. The default behaviour is to keep the files

required for post processing. These controls can be used to keep all files created throughout

the solution or to create and save the Mechanical APDL application database (db file).

Define Initial Conditions:

A transient analysis always involves all the loads; those are the functions of the time.

The foremost step to apply transient loads for setting up initial conditions (i.e. the condition

at Time = 0).

The default first condition for a transient structural analysis is that the structure is

“at rest”, i.e. both initial displacement and velocity are zero. A transient structural analysis is

at rest, by default. The Initial Condition of the object allows specifying velocity.

Transient Structural Analysis of Container Lifter Model in ANSYS

51

In several analyses one or more parts will have an initial known velocity such as in a

drop test, metal forming analysis or kinematic analysis. If required constant velocity at initial

condition shall be specified in these types of analyses. This constant velocity shall be scoped

to one or more parts of the structure. The remaining parts of the structure which are not

part of the scoping will retain the “at rest” in the initial condition.

Initial condition using steps: Initial conditions are specified using step controls. It can

be processed by specifying multiple steps in a transient analysis. It will also control the time

integration effects along with activation or deactivation of loads. This will be very useful for

different parts of model which are having different initial velocities and more critical initial

conditions.

Apply Loads and Supports: All inertial, structural loads and structural supports joint loads

are used to drive the joints kinematically in transient structural analysis for applicable loads

or supports.

In the analysis, load’s magnitude shall be a stable or could fluctuate with time as defined in

a table or using the function. The particulars of applying tabular or function load are

described in specifying load values. In addition to this the apply loads and supports section

shows more information about ramped loads and time stepping.

It is also suggested that for the solver to converge, inclined joint load angles and positions

from zero to the actual original condition in only single step.

Solve: While performing nonlinear analysis, there will be convergence difficulties because of

number of reasons. In few examples initially open contact surfaces causing rigid body

motions, large load increments causing non‐convergence, material instabilities or large

deformations. This may cause mesh distortion and that will result in element shape errors.

Some tools are accessible under result information object detail view to recognize the

possible problem areas.

Solution Output: During the analysis it will continuously updates all listing output from the

solver. These will also provide precious information on the behavior of the structure. As

explained in the Solution Information section, all convergence data output is graphically

displayed in the printout.


52

Review Results: As a result of a transient structural analysis, all structural results are

available except frequencies. Solution Information can be used to track, check or analyze

problems that occur during the solution. After the accessibility of solution, the results can

be animated to review the response of the structure. In nonlinear static analysis, the

solutions are available at some time points. Here probe shall be used to show the variation

of a result as the load increases.

Loading and Boundary Conditions:

A load of 5 ton (50000 N) is equally shared by both hydraulic cylinder assemblies as dynamic

load condition described in Table 5.1 and transient structural analysis is carried out.

Table 5.1 Rigid dynamic analysis setup for CLD model

As base is rested on CLD bottom surface hence ground to base is taken as fixed boundary

conditions. Hydraulic cylinder assembly, piston to big link and big link to base are displaced

in planer angular direction with respect to base. Hence revolute boundary conditions are

considered. There is sliding motion of piston rod inside the hydraulic cylinder hence the

translation type joint is considered. Cross rod to big link is having rigid joint hence fixed

boundary condition is considered.

5.4 Dynamic Analysis of Container Lifting Device

Step 1) 3‐D model of existing container lifting device build and assemble in Solid Works 14.0

Analysis setup Object name Value

Acceleration All body 9.8 m/s2 in global C. S. +y axis

Remote force Cross‐ rod 25000 N in global C. S. –y axis

Joint velocity Cylinder‐ piston pair 20 mm/s in reference C. S. +x axis

Remote

displacement

Cylinder, piston, big link 1) x displacement is 0 mm

2) rotation in y and z axis is 00

Dynamic Analysis of Container Lifting Device

53

Fig. 5.1 Solid Works 3‐D model of container lifting device

Step 2) For perform ANSYS dynamic analysis convert SolidWorks assembly file .SLDASM in to

ANSYS .STEP file.

Step 3) Assign material property to each and every individual parts of container lifting

device.

Table 5.2 Material and its property for individual component of CLD

Material and it’s property

Component of CLD modal

Hydraulic

cylinder

Piston

rod

Cross‐rod Big link Base

Material ST 52 SAE 1045 S 355

Density (Kg/m3) 7800 7872 7800

Tensile Yield stress(Pa) 520 310 355

Compressive yield stress (Pa) 520 310 355

Ultimate stress (Pa) 900 565 470 ‐ 630

Young’s Modulus (GPa) 200 200 210

Poisson's Ratio 0.3 0.29 0.3

Bulk Modulus (GPa) 167 158 175

Shear Modulus (GPa) 77 77.5 80.7


54

Step 4) Generate geometry in ANSYS and give adequate connection between parts to

allowed motion like body to body or body to ground. In CLD model connection or joint

applied between parts are as follows…

Table 5.3 Joint applied to different pair of CLD component

Object name Type of joint Type of connection

Ground to base Fixed Body to ground

base to cylinder Revolute Body to body

cylinder to piston Translation Body to body

piston to big link Revolute Body to body

big link to base Revolute Body to body

cross rod to big link Fixed Body to body

Fig. 5.2 Joints between individual components of CLD model

5.4.1 Rigid dynamic analysis of CLD model

For rigid parts, the following conditions apply:

Line bodies cannot be set to rigid.

Multibody parts must have all bodies set to rigid.

Density is the only material property needed to calculate mass properties. All other

material specifications will be ignored.


55

An “Inertial Coordinate System” will automatically be defined at the centroid of the

part

Rigid bodies are rigid, so no stresses, strains, or relative deformation is calculated.

Hence, no mesh is required

Step 5) In Analysis setting number of step taken is one with end time is 60s.

Table 5.4 Analysis setting in rigid dynamic analysis of CLD model

Number Of Steps 1

Current Step Number 1

Step End Time 60 s

Auto Time Stepping on

Initial Time Step 1.e‐002 s

Minimum Time Step 1.e‐007 s

Maximum Time Step 5.e‐002 s

Time Integration Type Runge‐Kutta 4

Relative Assembly Tolerance on

Store Results At All Time Points

Step 6) In Rigid dynamic analysis CLD model setup like load (Equally shared by both

hydraulic cylinder assembly, 25000 N), velocity, displacement etc. are given below.

Fig. 5.3 Remote force, constrain, joint velocity, acceleration shown in CLD model


56

Step7) Aim to perform rigid dynamic analysis is to find out force generate at different joint

probe and working behaviour of model in 3‐D space. After completion of solution result are

found are as follows.

Fig. 5.4 Force shown on translation joint probe

Fig. 5.5 Translation joint force change with time

‐2.00E+05

‐1.50E+05

‐1.00E+05

‐5.00E+04

0.00E+00

5.00E+04

1.00E+05

1.50E+05

0 10 20 30 40 50 60 70

Force (N)

Time (Sec.)


57

Table 5.5 Value of translation joint force with respect to time

Second Force(N) Second Force(N) Second Force(N)

0 1.12 510 21 ‐7547.7 41 ‐60160

1 90991 22 ‐10081 42 ‐63323

2 76687 23 ‐12589 43 ‐66595

3 65865 24 ‐15079 44 ‐69992

4 57219 25 ‐17557 45 ‐73526

5 50034 26 ‐20029 46 ‐77218

6 43884 27 ‐22625 47 ‐81088

7 38497 28 ‐24978 48 ‐85161

8 33688 29 ‐27467 49 ‐89467

9 29330 30 ‐29971 50 ‐94043

10 25327 31 ‐32496 51 ‐98932

11 21611 32 ‐35047 52 ‐1.04 510

12 18128 33 ‐37630 53 ‐1.10 510

13 14837 34 ‐40250 54 ‐1.16 510

14 11704 35 ‐42913 55 ‐1.23 510

15 8703 36 ‐45624 56 ‐1.31 510

16 5811.6 37 ‐48391 57 ‐1.39 510

17 3011.7 38 ‐51220 58 ‐1.50 510

18 288.1 39 ‐54118 59 ‐1.61 510

19 ‐2372.4 40 ‐57095 60 ‐1.76 510

20 ‐4981

5.4.2 Transient dynamic analysis of CLD model

Step5) Meshing is to be done by

automatic generate mesh option with

element shape used was tetrahedral.

Total numbers of element is 33790

with numbers of node is 69041.

Fig. 5.6 Fine meshing of CLD model


58

Step 6) In Analysis setting number of step taken is one with end time is 60s.

Table 5.6 Transient dynamic analysis setting

Number Of Steps 1

Current Step Number 1

Step End Time 60. s

Auto Time Stepping on

Initial Time Step 0.1 s

Minimum Time Step 0.1 s

Maximum Time Step 0.1 s

Time Integration on

Newton Raphson option Program control

Store Results At All Time Points

Table 5.7 Transient dynamic analysis setup of CLD model

Step 7) In transient dynamic analysis CLD model setup like load, velocity, displacement etc.

are given below.

Analysis setup Object name Value

Acceleration All body 9.8 m/s2 in global C. S. +y

Remote force Cross‐ rod 25000 N in global C. S. ‐y

Joint velocity Cylinder‐ piston pair 20 mm/s in reference C. S. +x


59

Fig. 5.7 Time varying force applied on piston

Fig. 5.8 Time varying reaction force applied on cylinder

Fig. 5.9 Remote force, constrain, joint velocity, acceleration shown in CLD model for

transient analysis

‐200000

‐150000

‐100000

‐50000

0

50000

100000

150000

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61

‐150000

‐100000

‐50000

0

50000

100000

150000

200000

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61


60

Step 8) Aim to perform transient dynamic analysis is to find out time‐history charts to

understand the transient response of the system. Like time varying stress, strain,

acceleration, joint force and moment etc. After completion of solution result are found are

as follows.

Fig. 5.10 Von‐Mises stress contour generated in CLD model

Fig. 5.11 Maximum Von‐Mises stress generate in CLD model


61

Fig. 5.12 Maximum value of Von‐Mises stress change with time

Fig. 5.13 Equivalent elastic strain contour generated in CLD model

Fig. 5.14 Maximum value of Equivalent elastic strain change with time

0.00E+00

2.00E+07

4.00E+07

6.00E+07

8.00E+07

1.00E+08

1.20E+08

1.40E+08

1.60E+08

1.80E+08

0 10 20 30 40 50 60 70

VON‐MISES STR

ESS (PA)

TIME (SEC.)

0.00E+00

1.00E‐04

2.00E‐04

3.00E‐04

4.00E‐04

5.00E‐04

6.00E‐04

7.00E‐04

8.00E‐04

9.00E‐04

1.00E‐03

0 10 20 30 40 50 60 70

Von‐mises Strain (m/m

)

Time (Sec.)


62

Fig. 5.15 Total deformation of CLD model

Fig. 5.16 Total deformation of CLD model with respect to time

Fig. 5.17 Cylinder‐ base total revolute joint probe force

0.00E+00

5.00E‐01

1.00E+00

1.50E+00

2.00E+00

2.50E+00

3.00E+00

3.50E+00

4.00E+00

4.50E+00

0 10 20 30 40 50 60 70

DISPLA

CEM

ENT (M

)

TIME (SEC.)


63

Fig. 5.18 Time varying total cylinder‐ base revolute joint force

Fig. 5.19 Big link‐ base total revolute joint probe force

Fig. 5.20 Time varying total big link‐ base revolute joint force

0.00E+00

2.00E+04

4.00E+04

6.00E+04

8.00E+04

1.00E+05

1.20E+05

1.40E+05

1.60E+05

1.80E+05

0 10 20 30 40 50 60 70

Total Force on Joint (N)

Time (Sec.)

0.00E+00

2.00E+04

4.00E+04

6.00E+04

8.00E+04

1.00E+05

1.20E+05

1.40E+05

1.60E+05

1.80E+05

0 10 20 30 40 50 60 70


Time (Sec.)


64

Fig. 5.21 Big link‐ piston rod total revolute joint probe force

Fig. 5.22 Time varying total big link‐ piston rod revolute joint force

Fig. 5.23 Safety factor contour generate in CLD model

0.00E+00

2.00E+04

4.00E+04

6.00E+04

8.00E+04

1.00E+05

1.20E+05

1.40E+05

1.60E+05

1.80E+05

0 10 20 30 40 50 60 70


Time (Sec.)


65

Fig. 5.24 Minimum time varying safety factor

5.5 Static Analysis of Leaf Spring

Step 1) 3–D solid model of leaf spring with help of SolidWorks 14.0

Fig. 5.25 3‐D model of leaf spring

Step 2) convert SolidWorks .SLDPRT file in to .STEP file and import in to ANSYS workbench

15.0.

Step 3) Apply no separation contact between pair of each leaf.

Step 4) Generate mesh in leaf spring model

Step 5) Applied load and constrain to model

0

1

2

3

4

5

6

0 10 20 30 40 50 60 70

Saftey Factor

Time (Sec.)


66

Fig. 5.26 Load and constrain applied to model of leaf spring

Steps 6) after completion solution, result are found like below.

Fig. 5.27 Stress generated in leaf spring

Fig. 5.28 Strain generated in leaf spring

Static Analysis of Mechanical Jack

67

5.6 Static Analysis of Mechanical Jack

Step 1) 3–D solid model of mechanical jack with help of SolidWorks 14.0 Step 2) convert SolidWorks .SLDPRT file in to .STEP file and import in to ANSYS workbench

15.0. Step 3) Apply no separation contact to pair of box column and revolute joints at hole for

locking pin and inner column to swinging base.

Step 4) Generate the mesh in mechanical jack.

Step 5) Applied load and constrain to model

Fig. 5.29 Load and constrain applied to model of mechanical jack

Step 6) after completion solution, results are found like below.

Fig. 5.30 Stress generated in mechanical jack


68

Fig. 5.31 Strain generated in mechanical jack

5.7 FFT Analyzer used for vibration measurement of hydraulic cylinder

Hydraulic cylinders are actuating devices converting the hydraulic energy of the

pressurized fluids into the mechanical energy needed to control the movement of machine

linkages and attachments. Hydraulic cylinders are used at high pressures and create huge

forces and precise movements. For this reason they are constructed of strong materials

such as steel and designed to withstand heavy forces. The fluid pushes the face of the piston

and produces a force.

Hydraulic cylinders are one of the most important components of the hydraulic

systems used in a different industrial application. Design of a hydraulic cylinder consists of a

different loading and boundary conditions. Vibration of the cylinder during working

condition is one of the most crucial elements of the failure criterion. In the container lifting

device operated by tractor, the most critical part is two hydraulic cylinders of 5 ton capacity.

It would be very important to optimize the dynamic characteristics of hydraulic cylinders,

because on the case of they will have to carry out the operation with huge weight inertia.

During the analysis, the dynamic characteristics of hydraulic cylinders are obtained by using

the Time Capture Analysis and Real‐time FFT, which is equipped on the container lifting

device with tractor used for solid waste management.

In the CLD operated by a tractor, the diesel engine would be pointed out as the main

source of power that creates the noise and vibration problems. The major requirements of

hydraul

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69

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70

spectrum that was recorded at that particular measurement point when the machine was

known to be in good condition.

The fast Fourier transform (FFT) is an efficient algorithm used to compute a discrete

Fourier transform (DFT). This Fourier transforms outputs vibration amplitude as a function

of frequency so that the analyzer can understand what is causing the vibration. The

frequency resolution in an FFT is directly proportional to the signal length and sample

rate. To improve the resolution the time of the recording must be extended; but be careful

of a changing vibration environment.

A spectrogram takes a series of FFTs and overlaps them to illustrate how the

spectrum (frequency domain) changes with time. If vibration analysis is being done on a

changing environment, a spectrogram can be a powerful tool to illustrate exactly how that

spectrum of the vibration changes.

A power spectral density (PSD) takes the amplitude of the FFT, multiplies it by its

complex conjugate and normalizes it to the frequency bin width. This allows for accurate

comparison of random vibration signals that have different signal lengths. For this reason,

PSDs are typically used to describe random vibration environments like those specified in

military and commercial test standards.

FFT Analyzer used for vibration measurement of hydraulic cylinder

71

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OMNITREND software and its applications

73

or the predefined ISO alarms as per DU ISO 10316 can be easily added to keep track of

any machine condition. A large bearing fault frequency database comes along within

OMNITREND freely which helps to identify specific failures with their root cause.

With OMNITREND Web Report, a separate reporting module is also available. Because

of the same quickly visualization of failures reported. OMNITREND will also integrate

all readings, alarms and other important parameters in CMMS systems. That becomes

ideal for the users of SAP, Maximo or other CMMS programs.

5.9 VIBXPERT – FFT (Fast Fourier Transform) data collector and signal analyzer

• As the double acting hydraulic cylinder is most important element for container

lifting device.

• We had taken a trial run for measurement of vibrations for the same element at

different 14 stages of lifting and lowering the empty and loaded 4.5 cubic meter

container with container lifting device.

• Thus we had measured the vibrations of double acting hydraulic cylinder taking the

different 14 stages readings. Out of that four samples are shown as under:

1) Empty container lifting acceleration spectrum


74

2) Loaded container lifting position 3 acceleration spectrum

3) Loaded container lifting position 4 displacement spectrum

4) Loaded container emptying position 8 velocity spectrum

Fig. 5.35 FFT spectrum of Hydraulic cylinder

VIBXPERT – FFT (Fast Fourier Transform) data collector and signal analyzer

75

Table 5.8 Typical measurements of RMS values

RMS Values

Sr. No.

Description Displacement (µm)

Velocity (mm/sec)

Acceleration (m/sec2)

1 Empty container lowering rest 10.05 1.01 0.193

2 Empty container lowering Vertical

24.38 2.42 0.271

3 Empty container lowering down 54.74 5.86 0.581

4 Empty container lifting 31.31 3.18 0.298

5 Loaded container lifting 1 26.15 3.20 0.349




9 Loaded Container Emptying 5 41.04 4.43 0.476






During actual working on CLD with FFT analyzer, vibration analysis of the hydraulic

cylinder under variable loading application and different boundary conditions is obtained.

The main cause of the vibration in hydraulic cylinder is stick‐slip phenomenon between

piston and cylinder which is also responsible for the failure of a sealing material and reduces

the fatigue life as well the performance of the hydraulic system. Vibration analysis can be

done by static, dynamic and transient way. The Mode Superposition Method of a transient

dynamic analysis is one of the most important methods to predict mode shapes. All readings

taken are checked with the standard given and they are all falls within the limit hence it is

proved that hydraulic cylinders performance is safe in vibration.

Fig. 5.336 Photograaphs of actu

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76

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77

CHAPTER 6

OPTIMIZATION OF HYDRAULIC CYLINDER

6.1 Introduction

The success of optimum design is naturally a gorgeous objective to the designer.

Apart from possible economic advantages, there is the fulfillment of producing the ‘best’

design possible confirming to a stated ‘objective’. In the past, considerable effort has been

devoted to the associated mathematical and computational backgrounds, and the methods

explored have been wide‐ranging and often mathematically complex. The concept of

‘gradient’ has been central and forms the basis of a class of methods suitably described as

‘hill‐climbing’. The gradient concept is not required in the genetic algorithm.

Many methods are in use for design optimization of various mechanical elements. All

use mathematical programming techniques to come into view at optimum solution. In most

of the practical problems in engineering design, the design variable is distinct. This is due to

the accessibility of components in standard sizes and constraints due to production and

manufacturing practices. A few algorithms have been developed to handle the distinct

nature of design variables. This issue is of the enormous importance in solving sensible

problems of design optimization. Lots of scientific linear and nonlinear programming

methods have been advanced for solving optimization problems during the last few years.

However, no single method has been found to be totally capable and strong for all different

kinds of engineering optimization problems. Some methods, such as the penalty‐function

method, conjugate gradient method and Lagrangian method, look for a local optimum in

which by moving in a way related to the local gradient. Some additional methods may apply

the first and second order with essential conditions to search for a local smallest by cracking

a set of nonlinear equations.

Usually these methods search for a solution in the region from the starting point.

Here the global optimum cannot be assured because the outcome will depend on the

selection of the initial point, if there is more than single local optimum existing in the

problem. Moreover gradient search becomes complicated and unsteady, when the objective

function and constraints have many sharp peaks.

Optimization of Hydraulic cylinder

78

6.2 Optimization Technique ‐ Genetic Algorithm

Over the last few years, genetic algorithms (GAs) have been widely used as a search

and optimization tools in various problem domains, including the sciences, commerce and

engineering. The main reasons for their success are their large applicability, ease of use and

global perspective.

The genetic algorithm is a typical model of machine learning, which develops its

behaviour from a symbol of the procedures of development in nature. The individual

characters in the population go through a process of evolution. According to Darwin, this is

made up of the principles of mutation and selection. However, the recent genetic evolution

theory also knows crossover and isolation mechanisms to increase the adaptiveness of the

living beings to their environments.

With genetic algorithms, elements are swapped between individuals as if by sexual

grouping and reproduction (crossover), others are altered at random (mutation). New

generations appear from clones of the current population, in proportion to their fitness. A

single objective functions of the parameters that return a numerical value, to distinguish

between good and bad solutions. Then Fitness is used to apply selection process to the

population in a ‘Darwin’ method (existence of the fittest).[17]

GAs combine the concept of artificial survival of the fittest with genetic operators

abstracted from nature to form a robust search mechanism. GAs differ from traditional

optimization algorithms in many ways.

GAs always searches from population of points, not from a single point.

GAs work with a coding of the parameter set, not the parameters themselves.

GAs use objective function information, not derivatives, calculus or other supporting

knowledge.

GAs use probabilistic alteration rules, not deterministic rules.

GAs use arbitrary based on the previous information to guide the search, instead of

gradient search, so that the derivative information and step size calculation are not

essential.

GAs must work must work in a restricted space for coding of the parameters. Genetic

Algorithms are not hill climbing algorithms. So called local hill climbing problems are

eliminated in these algorithms. Therefore, the probability of getting entrapped in a local

minimum is reduced.

Optimization Technique ‐ Genetic Algorithm

79

The genetic algorithm is a method for resolving both constrained and unconstrained

optimization problems that is based on usual selection. This process drives biological

evolution. The genetic algorithm frequently modifies a population of individual resolutions.

At each step, the genetic algorithm selects persons at random from the current population

to be parents and uses them to create the children for the following generation. Over the

consecutive generations, the population "evolves" toward an optimal solution. Anyone can

apply the genetic algorithm to solve a diversity of optimization problems that are not well

suited for standard optimization algorithms. This may include the problems in which the

objective function is intermittent, non‐differentiable, stochastic, or extremely nonlinear.

The genetic algorithm can be used for the problems of mixed integer programming, where

some components are restricted to be integer‐valued.[17]

During the optimization using Genetic Algorithm mainly depends on three types of

different rules. At the end of each step which helps to produce the next generation from

present population.

Assortment of rules with select the individuals, who called parents that, are

contributing to the population at the next generation.

Secondly, with the crossover rules which compress two parents to form children for the

subsequent generation.

Mutation rules are related with arbitrary changes to distinct parents to form children.

The genetic algorithm varies from a classical, derivative‐based, optimization

algorithm in two main ways, as summarized in the following table 6.1.

Table 6.1 Comparison of Classical and Genetic Algorithm

Classical Algorithm Genetic Algorithm

Generation of single point at every iteration.

The progression of points approaches to an

optimal solution.

Generation of population of points at every

iteration. The best point in the population

approaches to an optimal solution.

Selects the subsequent point in the

progression by a deterministic computation.

Selects the next population by calculation

which uses arbitrary number generators.

6.2.1 Outline of Genetic Algorithm

This section describes fractural analysis of outline of genetic algorithm. The following

stepwise description shows that the genetic algorithm continues:


80

(1) In the beginning, algorithm generating arbitrary initial population.

(2) Afterwards, algorithm creates a progression of novel populations. At every step, the

algorithm uses the individuals in the recent generation to create the next population.

To generate the novel population, the algorithm performs the following steps:

By computing the fitness value, it scores each member of the present population.

Scales the raw suitability marks to alter them into a more functional range of values.

Selection of members, which called parents, depends on their fitness measurement.

Some of the personalities in the present population that have lesser fitness which are

chosen as elite. Such elite individuals are conceded to the subsequent population.

Creation of the children is from the parents. Children are created either by framing

arbitrary deviations towards a single parent to mutation or by uniting the vector entries

of a pair of parents to crossover.

Change the present population with the children to form the subsequent generation.

The algorithm terminate when one of the ending criteria is met.

6.3 Optimization in Hydraulic Cylinder Design – A Case Study

Monotonicity and dominance were used to find general principles for designing

hydraulic cylinders optimal for a wide class of objective functions and stress conditions. The

design method, although guaranteed to give the optimum design.

Optimal cylinders should be designed for minimum force. Only two designs can be

optimal—one with maximum pressure and minimum wall thickness; the other with

maximum stress.

In the former case, the design is retained if and only if the stress is less than

allowable. Otherwise, a one‐variable search in a restricted interval is needed. The results

suggest the potential importance of monotonicity and dominance in identifying the critical

constraints in a design.

Optimization in Hydraulic Cylinder Design – A Case Study

81

Here we have taken five design variables,

(1) Inside diameter, d x(1) (2) Wall thickness, t x(2) (3) Material Stress, s (4) Force, f (5) Oil Pressure, p x(3) First Optimum design will be with maximum pressure and minimum wall thickness, second with maximum stress. Subject to

2 2

, 7

, 5 5000 49050

, 200 / 19.62 /

Wall thickness t mm

Force f ton so kg N

Pressure p kg cm say N mm

There are three physical relations: First relates force, pressure and area.

2 ....... .(6.3.1)4

f d p Eq

The second gives the wall stress,

......... .(6.3.2)2

p ds Eq

t

Also to find Cross‐sectional area of hydraulic cylinder:

Cross‐sectional area,

2

2

. . .

. ........ .(6.3.3)

A d t t

A d t t Eq

Genetic algorithm is most suitably used for multi modal functions because it always

searches from population of points instead of a single point, which is usually followed in

classical optimization techniques.

In design of hydraulic cylinder there are five variables taken namely internal diameter,

thickness of cylinder, material stress, internal pressure and force on the piston. Objectives

are maximization of stress, minimization of force, minimization of cross sectional area of

cylinder. All these objectives are conflicting in nature hence the problem can be solved

using multi‐objective optimization techniques. Classical optimization techniques are not

much suitable for multi‐objective optimization problem so heuristic method such as genetic

algorithm is used for solving such problems.


82

6.4 Single Objective Optimization Problem – Nonlinear Constrained Minimization

Optimization Toolbox provides widely used algorithms for standard and large‐scale

optimization. These algorithms solve constrained and unconstrained continuous and

discrete problems. The toolbox includes functions for linear programming, quadratic

programming, binary integer programming, nonlinear optimization, nonlinear least squares,

systems of nonlinear equations, and multi‐objective optimization. We can use them to find

optimal solutions, perform tradeoff analyses, balance multiple design alternatives, and

incorporate optimization methods into algorithms and models.

Using MATLAB 2012 following eight examples were created and solved related to

optimization and design hydraulic cylinder to be used for container lifting device, out of

these results for two examples are shown below:

Example 6.1 app2 ‐‐ Minimize the force, f

The iteration table in the command window shows how MATLAB searched for the minimum

value of force function in the unit disk. This table is the same whether to be used as

Optimization Tool or the command line. MATLAB reports the value of three variables (i.e.

internal diameter (d), cylinder wall thickness (t), pressure (p) and minimization of force, (f)

as below:

Output

x = 57.0000 7.0000 19.6200

fval = 5.0040e+04

Fig.6.1 Optimization using MATLAB for the function : Minimization of force value (f)

exerted on piston

Single Objective Optimization Problem – Nonlinear Constrained Minimization

83

Example 6.2

app16 – Minimization of cross‐sectional area

Again the iteration table in the command window shows how MATLAB searched for

the minimum value of cross‐sectional area function in the unit disk. This table is the same

whether to be used as Optimization Tool or the command line. MATLAB reports the value of

three variables (i.e. internal diameter (d), cylinder wall thickness (t), pressure (p) and

minimization of cross‐sectional area, (A) as below:

Output

x = 50.0000 7.0000 16.0707

fval = 1.2529e+03

Fig.6.2 Optimization using MATLAB for the function: Minimization of Cross‐sectional area (A)

of the Hydraulic Cylinder

6.5 Multi Objective Optimization using Genetic Algorithm

A multi‐objective optimization problem (MOOP) tool deals with more than one

objective function. There are fundamental differences between the working principles of

single and multi‐objective optimization algorithms. However, in a single objective

optimization, the task is to find one solution which optimizes the sole objective function.

A multi objective optimization problem has a number of objective functions which

are to be maximized or minimized. As in the single – objective optimization problem, here

also the problem usually has a number of constraints which may have any feasible solution

(including the optimal solution).


84

The general form of Multi‐objective optimization problem (MOOP) is stated as follows:

[ ] [ ]

/ , 1, 2,......., ;

0, 1,2,........, ;

0, 1,2,........, ;

, 1, 2,........., .

m

j

k

L Ui i i

Minimize Maximize f x m M

subject to g x j J

h x k K

x x x i n

……….6.5.1

A solution x is a vector of number of decision variables (n): 1 2, ,.........,T

nx x x x .

The last sets of constraints are called variable bounds, restricting each decision variable ix to

take a value within a lower [ ]Lix (Lower bound) and upper [ ]U

ix (Upper bound) bound. These

confines to establish a decision variable space (D).

In many engineering disciplines we need to find solutions in the presence of

conflicting objectives. In such cases, solutions are chosen such that there are reasonable

trade‐offs among different objectives. In certain problems, it may not be obvious that the

objectives are not conflicting to each other. In such combinations of objectives, the resulting

Pareto‐optimal set will contain only one optimal solution. Pareto search is an approach for

handling such situations. Instead of generating a single optimal solution, many solutions are

generated that satisfy Pareto Optimality Criterion. According to this criterion, a solution

point P is accepted only if there are no solutions better than P with respect to all the

objectives. For example, even if P is worse than another solution P1 with respect to one

objective, P is accepted provided that it is better than P1 in at least one objective. Thus each

Pareto optimal solution is good in some respect. The set of all Pareto optimal solutions form

a surface known as a Pareto front. The Pareto front helps engineers understand the nature

of trade‐offs that need to be made in order to select good solutions. Visualizing the front

helps engineers make good decisions.

6.6 Difference with Single‐Objective Optimization:

Besides having multiple objectives there are number of fundamental differences between

single‐objective and multi‐objective optimization, as follows:[33]

two goals instead of one;

dealing with two search spaces;

no artificial fix‐ups.

Difference with Single‐Objective Optimization

85

6.6.1 Two goals Instead of one:

In a single‐objective optimization, there is one goal – the search for an optimum

solution. In the case multi‐modal optimization, the goal is to find a number of local and

global optimal solutions, instead of finding one optimum solution. However, most single‐

objective optimization algorithms target to find one optimum solution, even though number

of optimal solutions exists.

However, in multi‐objective optimization, there are clearly two goals. Progressing

towards the Pareto‐optimal front is certainly an important goal. However, maintaining a

diverse set of solutions in the non‐dominated front is also essential. An algorithm that finds

a closely packed set of solutions on the Pareto‐optimal front satisfies the first goal of

convergence to the Pareto‐optimal front, but does not satisfy maintenance of a diverse set

of solutions. Since all objectives are important in a multi – objective optimization, a diverse

set of obtained results near to the Pareto‐optimal front provides a variety of optimal results,

trading objectives differently. A multi‐objective optimization algorithm that cannot find a

diverse set of solutions in a problem is as good as a single‐objective optimization algorithm.

Since both goals are important, an efficient multi‐objective optimization algorithm must

work on satisfying both of them. Looking to these dual tasks, multi – objective optimization

is more challenging than single – objective optimization. [33]

6.6.2 Dealing with two search spaces

Another difficulty is that multi‐objective optimization involves two search spaces,

instead of one. In a single‐objective optimization, there is only one search space – the

decision variable space. An algorithm works in this space by accepting and rejecting

solutions based on their objective function values. Here in addition to the decision variable

space, there is also the objective function space. When this occurs, the measures in both

spaces must be synchronized in such a way that the formation of new results in the decision

variable space is complimentary to the variety needed in the objective space. This by no

means, is an easy task and more importantly is dependent on the mapping between the

decision variables and objective function values. [33]


86

6.6.3 No Artificial Fix‐Ups

The most real world optimization problems are naturally posed as a multi – objective

optimization problem. Multi‐objective optimization for finding multiple Pareto‐optimal

solutions eliminates all such fix‐ups and can, in principle, find a set of optimal solutions

corresponding to different weight and e‐vectors. It is true that in general a multi – objective

optimization is more complex than a single‐objective optimization. But the avoidance of

multiple simulation runs, no artificial fix‐ups and availability of effective population based

optimization algorithms, and above all, the conception of dominance helps to overcome

some of the difficulties and give a user the real means to handle many objectives, a matter

which was not possible to achieve in the past. [33]

6.7 Multi‐objective Optimization

Multi – objective optimization is apprehensive with the minimization of a vector for

objectives F(x) that can be the matter of a number of constraints or bounds:

min ,

0, 1,....., ; 0, 1,....., ; .

n

i e i e

F x subjected tox R

G x i k G x i k k l x u

Note that because F(x) is a vector, if any of the constituents of F(x) are contending,

there is no unique solution to this problem. So instead of that the concept of non –

inferiority (also known as Pareto optimality) must be used to describe the objectives. A non

– inferior solution is one in which an improvement in one objective requires a degradation

of another. To define this concept more precisely, consider a feasible region, , in the

parameter space. X is an element of the n‐dimensional real numbers nx R that satisfies all

the constraints, i.e.

nx R ,

Subject to

0, 1,....., ,

0, 1,....., ,

i e

i e

G x i k

G x i k k

l x u

This permits description of the corresponding possible region for the objective

function space :

: , .my R y F x x

The presentation vector F(x) maps parameter space into objective function space, as

represented in two dimensions in the Figure 6.3.

Multi‐objective Optimization

87

Fig.6.3 Mapping from Parameter Space into Objective Function Space

A non – inferior solution point can now be defined.

Definition: Point x is a non – inferior solution if for some neighborhood of x there

does not exist a x such that x x and

, 1,...., ,

.

i i

j j

F x x F x i m and

F x x F x for at least one j

In the two – dimensional representation of Figure 6.4 the set of non – inferior

solutions lies on the curve between C and D. Points A and B represent specific non – inferior

points.

Fig.6.4 Set of Non – inferior Solutions

A and B are obviously non – inferior solution points because an enhancement in one

objective, F1, requires a degradation in the other objective, F2, i.e. , 1 1 2 2, .B A B AF F F F


88

Since any point in that is an inferior point in which improvement can be attained in all

the objectives, it is clear that such a point is of no value.

Multi‐objective optimization is, therefore, concerned with the generation and selection of

non – inferior solution points.

Non – inferior results are also called Pareto optima. An overall objective of multi‐

objective optimization is making the Pareto optima.

Using MATLAB 2012 ten different examples were created and solved related to

multi‐objective optimization and design hydraulic cylinder to be used for container lifting

device, out of these results for following three examples are shown.

Example 6.3 app9 ‐‐ Multi objective Optimization, Pareto Optimization, Maximize the stress, s & Minimization of force, f linked with mymulti4.m. [d = x(1), t = x(2) and p = x(3)] App9 options = gaoptimset('PopulationSize',60,... 'ParetoFraction',0.7,'PlotFcns',@gaplotpareto); [xfval flag output population] = gamultiobj(@mymulti4,3,... [],[],[],[],[55,7,15.696],[70,15,19.62],options) mymulti4.m function f = mymulti4(x) f(1) = -x(3)*x(1)/(2.0*x(2)); f(2) = 0.785*x(1)^2*x(3); Table 6.2 Value of each variable Internal diameter (d), Thickness of Cylinder (t) and Internal

Pressure (p) after each iteration

Sr. No.

Internal Diameter, d, x(1) in mm

Thickness of

Cylinder, t, x(2) in mm

Internal Pressure, p, x(3) in N/mm2

Sr. No.


Thickness of Cylinder, t, x(2) in mm


1 55.0000 7.0000 15.6960 31 55.0000 7.0000 15.6960

2 66.2678 7.0059 19.5723 32 58.3710 7.0075 19.4684

3 55.0000 7.0000 15.6960 33 55.8222 7.0116 19.4376

4 55.0000 7.0000 15.6960 34 64.7664 7.0126 19.4935

5 62.4450 7.0580 19.4929 35 62.5167 7.0069 19.5045

6 60.4970 7.0069 19.5850 36 55.7202 7.0073 18.6119

7 60.1910 7.0055 19.4753 37 63.0392 7.0155 19.5742

8 64.0234 7.0124 19.5256 38 65.2111 7.0204 19.5760

9 55.0613 7.0047 18.4560 39 64.4622 7.0227 19.5644

10 55.1285 7.0795 16.5295 40 59.3725 7.0063 19.4064

11 55.1080 7.0361 16.1410 41 66.3172 7.0970 19.4580

12 61.5908 7.0063 19.4276 42 60.1721 7.0188 19.1510

13 55.5851 7.0042 17.1733 43 56.3911 7.0056 19.4793

14 58.2347 7.0028 19.5183 44 59.6663 7.0058 19.0352

15 55.1813 7.0154 18.4295 45 55.1911 7.0041 16.6825

16 57.9182 7.0066 19.5054 46 61.1807 7.0069 19.5383

17 64.9375 7.0077 19.5621 47 61.8250 7.0066 19.5524


89

18 66.0774 7.0193 19.4926 48 65.9791 7.0154 19.5238

19 65.4093 7.0061 19.5791 49 56.3966 7.0052 19.5736

20 55.3754 7.0041 18.4095 50 55.0618 7.0129 18.2643

21 58.5363 7.0245 19.5747 51 55.4177 7.0050 19.1041

22 62.7189 7.0064 19.5771 52 57.7819 7.0059 19.4920

23 57.4923 7.0119 19.4306 53 55.0702 7.0042 16.4860

24 63.2921 7.0069 19.4777 54 66.2678 7.0059 19.5723

25 55.7036 7.0036 16.5999 55 55.1476 7.0629 17.2362

26 56.2273 7.0055 19.4126 56 55.1774 7.0132 17.7655

27 61.5269 7.0458 19.3891 57 55.0156 7.0000 15.6960

28 55.7098 7.0088 19.3550 58 55.0963 7.0109 19.4399

29 63.4252 7.0314 19.5727 59 61.5894 7.1083 19.5141

30 55.3446 7.0098 17.8110 60 60.5595 7.1319 19.5850

Fig.6.5 Pareto optimization using Genetic Algorithm plot of Stress generated (N/mm2) v/s

Force on Piston (N)

Example 6.4

app6 ‐‐ Multiobjective Optimization, Pareto Optimization, Maximize the force, f& Minimization of thickness, t linked with mymulti1.m. [d = x(1), s = x(2) and p = x(3)]

App6

options = gaoptimset('PopulationSize',60,... 'ParetoFraction',0.7,'PlotFcns',@gaplotpareto);


90

[xfval flag output population] = gamultiobj(@mymulti1,3,... [],[],[],[],[55,80,15],[80,92,19.62],options) mymulti1.m

function f = mymulti1(x) f(1) = -0.785*x(1)^2*x(3); f(2) = x(3)*x(1)/(2.0*x(2));

Table 6.3 Value of each variable Internal diameter (d), Stress of Cylinder (s) and Internal


Sr. No.


Stress of Cylinder, s, x(2) in N/mm2


Sr. No.


Stress of Cylinder, s, x(2) in N/mm2


1 55.0078 91.6168 15.0018 31 69.1143 91.7195 15.1666

2 79.9919 91.7703 19.6176 32 59.2841 91.8107 15.5315

3 79.9919 91.7547 19.6176 33 79.7091 91.7845 16.0754

4 79.2017 91.7932 16.5609 34 79.8247 91.7922 17.2598

5 55.0078 91.6480 15.0018 35 65.0814 91.7806 15.2036

6 55.0078 91.6793 15.0018 36 79.6472 91.7693 16.2253

7 79.9694 91.8333 19.3448 37 57.2698 91.7542 15.1802

8 79.8608 91.7727 15.8229 38 79.6972 91.7660 18.5507

9 66.3763 91.7202 15.1005 39 61.4632 91.7095 15.1355

10 76.0792 91.7928 15.0875 40 62.8751 91.6910 15.1317

11 79.4116 91.7394 17.0860 41 55.0078 91.6793 15.0018

12 79.8643 91.8274 19.0110 42 57.5984 91.7088 15.0473

13 63.2734 91.7015 15.0096 43 78.8803 91.7897 15.8159

14 79.8288 91.7790 17.8853 44 68.5697 91.7179 15.2937

15 65.0628 91.7493 15.2036 45 79.1919 91.7576 17.0069

16 64.9417 91.6681 15.0416 46 59.2251 91.6635 15.1759

17 77.6260 91.7956 15.2363 47 78.6092 91.7721 17.4948

18 67.0868 91.7160 15.3411 48 73.1853 91.7286 15.5022

19 70.5506 91.6568 15.0636 49 76.8068 91.7216 15.4737

20 75.3182 91.7606 15.3205 50 75.4550 91.8006 15.3853

21 67.3269 91.7229 15.0905 51 68.5267 91.7862 15.0148

22 79.3568 91.7997 16.7358 52 78.1924 91.7886 15.5452

23 79.6428 91.7405 18.5703 53 79.9295 91.7737 19.3981

24 79.9240 91.8879 17.5826 54 72.4331 91.7462 15.4561

25 79.9256 91.8502 18.3170 55 79.7663 91.7790 17.8853

26 77.3328 91.8284 15.1115 56 70.4219 91.7718 15.4908

27 72.7814 91.7847 15.0560 57 79.9240 91.8254 17.5826

28 79.8332 91.8116 18.7950 58 63.2734 91.6390 15.0643

29 59.9086 91.7026 15.4638 59 55.0586 91.7418 15.0018

30 56.6810 91.7107 15.0201 60 79.5994 91.7578 15.6826


91

Fig.6.6 Pareto optimization using Genetic Algorithm plot of Force (N) v/s Thickness of

Cylinder (mm)

Example 6.5

app17 ‐‐ Multiobjective Optimization, Pareto Optimization, Maximize the force, f & Minimization of cross‐sectional area, A linked with mymulti17.m. [d = x(1), t = x(2) and p = x(3)] options = gaoptimset('PopulationSize',60,... 'ParetoFraction',0.7,'PlotFcns',@gaplotpareto); [xfval flag output population] = gamultiobj(@mymulti17,3,... [],[],[],[],[55,07,15.696],[70,16,19.62],options) mymulti17.m

function f = mymulti17(x) f(1) = -0.785*x(1)^2*x(3); f(2) = 3.14*((x(1)*x(2)+x(2)^2));


92

Table 6.4 Value of each variable internal diameter (d), Thickness of Cylinder (t) and Internal


Sr. No.


Thickness of

Cylinder, t, x(2) in mm


Sr. No.


Thickness of Cylinder, t, x(2) in mm


1 55.0000 7.0000 16.0085 31 55.4372 7.0396 19.2665

2 69.9987 7.0283 19.6200 32 65.5574 7.0109 19.6028

3 69.9987 7.0127 19.6200 33 55.9352 7.0152 19.5074

4 57.6986 7.0018 19.5913 34 55.3623 7.0012 18.9455

5 55.0031 7.0002 18.8725 35 63.8330 7.0108 19.3648

6 63.5562 7.0312 19.5854 36 69.6822 7.0121 19.6186

7 55.0000 7.0000 17.5416 37 64.9928 7.0043 19.5858

8 55.0000 7.0000 16.0085 38 65.8195 7.0137 19.6042

9 55.0000 7.0000 16.0241 39 60.3892 7.0092 19.5858

10 55.0000 7.0000 16.9024 40 59.5785 7.0034 19.2825

11 55.0031 7.0012 18.8725 41 61.5670 7.0146 19.6042

12 56.7591 7.0100 19.6194 42 57.3518 7.0055 19.4248

13 69.2413 7.0112 19.6099 43 65.6511 7.1359 19.6028

14 68.5541 7.0106 19.5913 44 55.1262 7.0001 18.3711

15 56.3311 7.0160 19.5552 45 59.4147 7.0100 19.4629

16 55.0000 7.0000 16.3537 46 58.3674 7.0041 19.2737

17 69.9987 7.0225 19.6200 47 55.2397 7.0133 19.3382

18 61.9767 7.0049 19.6109 48 62.2097 7.0236 19.4095

19 69.9987 7.0127 19.6200 49 63.2301 7.0185 19.6084

20 66.5631 7.0163 19.6021 50 60.8089 7.0126 19.6011

21 59.7798 7.0104 19.6011 51 57.7745 7.0087 19.3398

22 55.0012 7.0001 18.3711 52 59.3529 7.0099 19.3541

23 62.3718 7.0077 19.4629 53 68.9767 7.0144 19.5854

24 63.9051 7.0162 19.6023 54 56.8451 7.0144 19.5399

25 67.5167 7.0110 19.4657 55 67.1126 7.0062 19.5764

26 60.9584 7.0099 19.3632 56 67.1009 7.0032 19.5319

27 55.0000 7.0000 17.0923 57 55.7167 7.0280 19.4999

28 67.5035 7.0199 19.6129 58 62.8502 7.0158 19.6097

29 68.0485 7.0104 19.6050 59 57.6018 7.0055 19.4248

30 58.8005 7.0098 19.2952 60 66.5631 7.0163 19.3521


93

Fig. 6.7 Pareto optimization using Genetic Algorithm plot of Force (N) v/s Cross‐sectional

area of Cylinder (mm2)

6.8 Conclusion of optimization

Most real‐world search and optimization problems are naturally posed as multi‐

objective optimization problems. However, due to the complexities involved in solving

multi‐objective optimization problems and due to the lack of suitable and efficient solution

techniques, they have been transformed and solved as single objective optimization

problems. Moreover, because of the presence of conflicting multiple objectives, a multi‐

objective optimization problem results in a number of optimal solutions, known as Pareto‐

optimal solutions. One major drawback of developing Pareto‐optimal plots is the extensive

calculation required to obtain the complete curve. Hence MATLAB is used to overcome such

problems.

Pareto optimization is a methodology for solving multi criteria decision problems.

This methodology provides a systematic approach towards design problems with multiple

conflicting objectives. In Pareto optimal design situations, the designer has more than one

performance found to be measure of interest. An optimal solution is generally defined as

the finest solution. However, with multi criteria problems, the "best" is often dependent

upon a designer's inclinations. The Pareto optimization methodology generally generates a

huge number of alternatives of which the designer evaluates in order to arrive at his best

solution frequently termed the best compromise solution.

94

CHAPTER 7

CONCLUSION AND FUTURE SCOPE OF RESEARCH

7.1 Conclusion of Research Work

Container lifting device for maximum load carrying capacity about 5000 Kg and that

can handle container about 4 to 4.5 m3 is designed. To find its behavior in dynamic

environment rigid and transient structure dynamic analysis has been carried out. Results of

analysis are discussed and suggested modifications in CLD to eliminate operational issues

and improving its performance. From the above work final conclusions are derived as under:

1) As per the dynamic analysis of CLD model, results of Von – Mises stress and strain, safety

factor etc. are found much below then allowable values. Comparisons are given below.

Table 7.1 Comparison of results with allowable value

Component Result Maximum value

generated

Allowable

value

Safety factor

during design

Safety factor

in ANSYS result

Piston rod Stress 64.5 MPa 310 MPa 3 4.7

Cross‐ rod Stress 52 MPa 355 MPa 3 6.8

Big link Stress 169 MPa 355 MPa ‐ 2.2

Base Stress 129 MPa 355 MPa ‐ 2.8

Piston Stress 30 MPa 310 MPa 3 10

All body Strain 0.00086 0.002

So, from the table it is found that maximum stress and strain generated during operation

of CLD model at maximum load is within prescribed limit.

2) As per the static analysis of leaf spring, simulated in ANSYS 15, at design load maximum

stress generated is found 813 MPa, which is less than 1800 MPa (yield stress) with safety

factor of 2 considered in design. That indicate design is safe under working condition.

3) As per the static analysis of mechanical jack, simulated in ANSYS 15, at design load

maximum stress generated is found nearly 90 MPa which is less than 170 MPa (yield

stress) with safety factor of 3 considered in design. That indicate design is safe under

working condition.

4) In single objective optimization problem, objective function is minimizing the force or

cross‐sectional area or thickness of cylinder, in which inside diameter (d) is the only

variable. The objective function is monotonic with respect to its variable (i.e. inside

Conclusion of Research Work

95

diameter, d). Hence single objective optimization is not much suitable in this case, in

addition to the variable inside diameter (d), pressure (p) and thickness (t) are also active,

so multi‐objective optimization is carried out.

Multi‐objective optimization problem results in a number of optimal solutions, known as

Pareto‐optimal solutions. The Pareto‐optimal curves of (a) Maximization of stress &

minimization of force (variables are d, t, p) (b) Maximization of force & minimization of

thickness (design variables are d, s, p) and (c) maximization of force & minimization of

cross sectional area of cylinder (variables are d, t, p) are obtained. These Pareto‐optimal

curves are useful to obtain different values of design variables for different cross

sectional area of cylinder, force on piston and stress generated.

5) After review of existing design/specifications of container lifting device, significant

parameters affecting performance of the container lifting device were identified.

Following modifications are also suggested on existing device which can eliminate major

difficulties faced during operation.

Two front support added to container lifting device for proper positioning and

placing of container and remains at its position while transportation.

Four side supports is provided to prevent the damage of hydraulic cylinder and oil

pipes.

Two rear mechanical jacks added to container lifting device for proper support and

stabilized the vehicle.

Side wall added to container lifting device to prevent the fall of waste directly from

container to ground.

Proper positioning and placing of hydraulic pipes.

7.2 Future scope of research work

After doing this research work, there are also some future work may possible for

improvement in performance of container lifting device.

Further performance investigation of CLD model by changing different hydraulic fluid

during operation.

Computational fluid dynamic (CFD) analysis of double acting hydraulic cylinder to

analyse the performance of CLD model at different hydraulic fluid.

Conclusion and future scope of Research Work

96

Effects of Vibration on fatigue life of hydraulic cylinders and seals can be evaluated.

Optimization of Cycle time by providing external hydraulic power pack may be

evaluated.

97

ANNEXURE I Definitions – MSW (Management and Handling) Rules 2000

Definitions: MSW (Management and Handling) rules 2000 i. "Anaerobic digestion" means a controlled process involving microbial decomposition of

organic matter in the absence of oxygen;

ii. "Authorization" means the consent given by the Board or Committee to the "operator of

a facility”;

iii. "Biodegradable substance" means a substance that can be degraded by microorganisms;

iv. "Biomethanation" means a process which entails enzymatic decomposition of the

organic matter by microbial action to produce methane rich biogas;

v. "Collection" means lifting and removal of solid wastes from collection points or any other

location;

vi. "Composting" means a controlled process involving microbial decomposition of organic

matter;

vii. "Demolition and construction waste" means wastes from building materials debris and

rubble resulting from construction, re‐modeling, repair and demolition operation;

viii. "Disposal" means final disposal of municipal solid wastes in terms of the specified

measures to prevent contamination of ground‐water, surface water and ambient air

quality;

ix."Form" means a Form appended to these rules;

x. "Generator of wastes" means persons or establishments generating municipal solid

wastes;

xi. "Land filling" means disposal of residual solid wastes on land in a facility designed with

protective measures against pollution of ground water, surface water and air fugitive

dust, wind‐blown litter, bad odour, fire hazard, bird menace, pests or rodents,

greenhouse gas emissions, slope instability and erosion;

xii. "Leachate" means liquid that seeps through solid wastes or other medium and has

extracts of dissolved or suspended material from it;

Annexure ‐ I

98

xiii. "Lysimeter" is a device used to measure rate of movement of water through or from a

soil layer or is used to collect percolated water for quality analysis;

xiv. "Municipal authority" means Municipal Corporation, Municipality, Nagar Palika, Nagar

Nigam, Nagar Panchayat, Municipal Council including notified area committee(NAC) or

any other local body constituted under the relevant statutes and, where the

management and handling of municipal solid waste is entrusted to such agency;

xv. "Municipal solid waste" includes commercial and residential wastes generated in a

municipal or notified areas in either solid or semi‐solid form excluding industrial

hazardous wastes but including treated bio‐medical wastes;

xvi. "Operator of a facility" means a person who owns or operates a facility for collection,

segregation, storage, transportation, processing and disposal of municipal solid wastes

and also includes any other agency appointed as such by the municipal authority for

the management and handling of municipal solid wastes in the respective areas;

xvii. "Pelletisation" means a process whereby pellets are prepared which are small cubes or

a cylindrical piece made out of solid wastes and includes fuel pellets which are also

referred as refuse derived fuel;

xviii. "Processing" means the process by which solid wastes are transformed into new or

recycled products;

xix. "Recycling" means the process of transforming segregated solid wastes into raw

materials for producing new products, which may or may not be similar to the original

products;

xx. "Schedule" means a Schedule appended to these rules;

xxi. "Segregation" means to separate the municipal solid wastes into the groups of organic,

inorganic, recyclables and hazardous wastes;

xxii. "State Board or the Committee" means the State Pollution Control Board of a State, or

as the case may be, the Pollution Control Committee of a Union territory;

xxiii. "Storage" means the temporary containment of municipal solid wastes in a manner so

as to prevent littering, attraction to vectors, stray animals and excessive foul odour;

Annexure ‐ I

99

xxiv. "Transportation " means conveyance of municipal solid wastes from place to place

hygienically through specially designed transport system so as to prevent foul odour,

littering, unsightly conditions and accessibility to vectors;

xxv. "Vadose water" water which occurs between the ground, surface and the water table

that is the unsaturated zone;

xxvi. "Vermicomposting" is a process of using earthworms for conversion of biodegradable

wastes into compost.

100

ANNEXURE II

Survey Report of Equipments at ULBs

Client: GUJARAT URBAN DEVELOPMENT COMPANY LIMITED

Name of Project: Municipal Solid Waste Management Project

Tender/Contract No.: ________________________________________________

Name of Municipality: _________________________________________________

Name of District: _________________________________________________

Name of Consultant: _________________________________________________

Name of Supplier: _________________________________________________

Sr. No.

List of Check Point Remarks & Suggestions

A Container Lifting Device

B Total Number Required By ULB

C Coding/Punching on Equipment

1 CLD Chassis No.

2 CLD RTO Registration No.

3 Overall dimension

4 Rear axle size (75 x 75)

5 Toe hook size (30 mm)

6 No. of bottom supports

7 Lifting arm plate thickness

8 Cross shaft (boom) diameter

9 Hydraulic cylinder make

10 Hydraulic cylinder dimension (63 x110)

Annexure II

101

11 Lift chain thickness (15 mm)

12 Parallel lift function

13 Tyre (make and size)

14 DC valve make and working

15 Stabilizer jack working

16 Hydraulic hoses (make and quality)

17 Any leakages observed in hydraulic system?

18 Whether tool kit is provided

19 Any transit damage found?

20 Lifter operating performance.

21 Whether brake provided and working properly?

22 Whether greasing/lubrication facility is provided?

23 Problems raised by ULB, if any.

Sign Sign

S. I. (Consultant)

Name: Name:

Seal

102

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List of Publications

108

LIST OF PUBLICATIONS

1 Prof. U. V. Shah, Himanshu Raiyani, Vijay Mistry "Municipal solid waste management in Indian cities – A review" in 2nd national conference on emerging trends in engineering technology & management at Indus institute of technology & engineering

2 Prof. U. V. Shah, Himanshu Raiyani, Vijay Mistry "Scenario of Municipal Solid Waste Management in India – A Review" in National Conference on ‘Transportation and Water resource Engineering” (NCTWE – 2015) at L. D. College of Engineering, Ahmedabad

3 Prof. U. V. Shah, Himanshu D. Raiyani, Prof. G. H. Upadhyay "Design and Dynamic analysis of existing container lifting device used for Solid Waste Management" in International Journal for Scientific Research and Development (IJSRD),Vol. 3, Issue 03, 2015, ISSN: 2321‐0613

4 Prof. U. V. Shah, Dr. G. H. Upadhyay, "Optimization of Hydraulic Cylinder Design used for container lifting device using Genetic Algorithm" in International Journal of Engineering and Techniques (IJET), ISSN: 2395‐1303, Volume 4 Issue 1 – 2018.

analysis and optimization of container lifting device … · i declare that the thesis entitled...

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