i

SIMULATION AND OPTIMIZATION OF ELECTRICAL DISCHARGE

MACHINING PROCESS USING MATLAB

ELNAZ KARIMPOUR

UNIVERSITI TEKNOLOGI MALAYSIA

1

SIMULATION AND OPTIMIZATION OF ELECTRICAL DISCHARGE

MACHINING PROCESS USING MATLAB

ELNAZ KARIMPOUR

A project report submitted in partial fulfilment

of the requirements for the award of the degree of

Master of Engineering (Electrical – Mechatronics & Automatic Control)

Faculty of Electrical Engineering

Universiti Teknologi Malaysia

JUNE 2014

iii

I dedicate this dissertation to three beloved people. To my adored husband helped me

find my way and a big part of my success in life. He always respire me to try for

bright future. To my precious parents who are the foundation to my life. I am really

honored to have them. Everything that I am now is because of them.

iv

ACKNOWLEDGEMENT

First and foremost I would like to thank God giving me the power to believe

in my passion and pursue my dreams. I could never have done this without the faith I

have in God, the Almighty. I have taken efforts in this project. However, it would not

have been possible without the kind support and help of many individuals. I would

like to extend my sincere thanks to all of them. First of all, I greatly appreciative of

my two supervisors, Dr Sophan Wahudi Nawawi and Dr Azli Yahya, who have

supported me to finish my master project. I am highly indebted to them for their

guidance and constant supervision. My thanks and appreciations also go to Mr Trias

Andromeda, PHD student in EDM research group for providing necessary

information regarding the project and also for his support in completing the project.

Apart from that, I would like to express my sincere gratitude to my husbands for his

inspiration, moral support and motivation. Last but not least, I would like to thank

my parents for their unconditional support, both financially and emotionally

throughout my degree. It became the vital encouragement for me to accomplish this

project.

v

ABSTRACT

Electrical discharge machining (EDM) is a nonconventional milling in which

holes or shape can be created without any contact between electrode and work piece

during the material removal process. Throughout the machining process a series of

stochastic sparks are produced when the gap between electrode and work piece is

very narrow just about 10 to 50 microns. Controlling this gap in micro dimension not

only depends on electrode position but also on work piece surface. So a combination

of PID controller and EDM process is used to monitor the gap. In this study an

indirect measurement of surface position is carried out by implementing voltage

average gap model. And Simulink models have been done to simulate the dynamic

behavior of EDM system. Finally Particle Swarm Optimization technique is applied

to find optimal performance parameters to have maximum Material Removal Rate.

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ABSTRAK

Pemesinan nyahcas elektrik (EDM) adalah proses pemesinan secara bukan

konvensional di mana lubang atau bentuk akan dihasilkan tanpa sebarang sentuhan

antara elektrod dan bahan yang ingin dimesin (bahan kerja). Sepanjang proses

pemesinan, satu siri bunga api yang dihasilkan secara stokastik dihasilkan dimana

jurang antara elektrod dan benda kerja adalah sangat kecil, iaitu hanya kira-kira 10

hingga 50 mikron. Mengawal jurang ini dalam dimensi mikro bukan sahaja

bergantung kepada kedudukan elektrod tetapi juga bergantung kepada kedudukan

bahan yang dimesin tersebut. Jadi, gabungan di antara Pengawal Kamiran

Berkadaran (PID) dan proses EDM digunakan untuk memantau jarak di antara

elektrod dan bahan kerja. Dalam penyelidikan ini, pengukuran tidak langsung

kedudukan permukaan bahan kerja telah digunakan dalam model voltan purata

jurang. Model telah dilakukan untuk membuat simulasi pergerakan dinamik sistem

EDM dengan menggunakan SIMULINK. Seterusnya, kaedah Pengoptimuman Zarah

Berkelompok (Particle Swarm Optimization) digunakan bagi mencari parameter

yang optimum untuk mencapai Kadar Pembuangan Bahan dengan kadar yang

maksimum.

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TABLE OF CONTENTS

CHAPTER TITLE PAGE

DECLARATION ii

DEDICATION iii

ACKNOWLEDGEMENT iv

ABSTRACT v

ABSTRAK vi

TABLE OF CONTENTS vii

LIST OF TABLES x

LIST OF FIGURES xi

LIST OF ABBREVIATIONS xiv

LIST OF APPENDICES xvi

1 INTRODUCTION 1

1.1 Background of Study 1

1.2 Problem Statement 6

1.3 Objectives of Project 7

1.4 Scope and Limitations of Study 7

2 LITERATURE REVIEW 9

2.1 Introduction 9

viii

2.2 Optimization of Pulse Power Generator 9

2.3 Related Research Work 11

3 METHODOLOGY 24

3.1 Introduction 24

3.2 Flowchart of Methodology 25

3.3 Description of The EDM Process 26

3.3.1 Description of The EDM Model 27

3.4 Description of The EDM System 31

3.5 Description of The Servo System 31

3.5.1 DC Motor 32

3.5.2 Derivation of DC Motor Transfer Function 36

3.5.3 Total Friction Torque 40

3.5.4 PID Controller 43

3.5.5 The Characteristics of P, I, and D Controllers 45

3.5.6 Simulation of Servo System Considering DC

Motor and PID controllers 46

3.6 Description of The EDM Process Block 47

3.7 Description of The Break Down Model 48

3.8 Description of The Average Voltage Gap Model 49

3.9 Particle Swarm Optimization (PSO) 50

3.9.1 Basic PSO Algorithm 51

3.9.2 Optimization Problem 54

3.9.3 Steps for PSO Algorithm 54

3.9.4 Search Space 56

3.9.5 Fitness Function 56

4 RESULTS AND DISCUSSIONS 58

4.1 Introduction 58

4.2 Break Down Model 58

4.3 Material Removal Rate 59

4.4 Current of Armature, Torque and Velocity of Motor 64

ix

4.5 Results from Servo System with Two PID Controllers 66

4.6 Controller Error 68

4.7 Dynamic process and response within Simulink model

when single & multiple pulses applied 68

4.7.1 Pulse Generator Block 69

4.7.2 Band-Limited White Noise 71

4.8 Maximization of EDM Process 77

4.8.1 Constriction Coefficient 78

4.8.2 Results Obtained from PSO Algorithm 79

5 CONCLUSION AND FUTURE WORK 80

5.1 Conclusion 80

5.2 Future Work 81

REFERENCES 82

Appendices A-B 86-91

x

LIST OF TABLES

TABLE NO. TITLE PAGE

3.1 Advantages and disadvantages of various types of DC

motor

36

3.2 Motor Parameters 40

3.3 PID Controller Properties 45

4.1 Input Parameters of MRR Function Based On PSO

Approach

79

xi

LIST OF FIGURES

FIGURE NO. TITLE PAGE

1.1 Electrical Discharge Machine (EDM) 2

1.2 Small hole EDM drilling 2

1.3 Simple Structures of Die-Sinking EDM System(MING, C.

L. 2012)

4

1.4 Simple Structures of Wire EDM System (MING, C. L.

2012)

4

1.5 Die Sinking EDM With Main Parts (Yahya A. 2005) 5

1.6 Voltage Waveform, Current Flow and Pulse Train (Oscar,

C., et al. 2009)

6

3.1 Flowchart of Methodology 25

3.2 Model of EDM System Consist of Two Main Subsystem 31

3.3 DC motor 33

3.4 Loop Equation Around The Armature Current 36

3.5 Typical mechanical loading on a motor 38

3.6 Different types of friction 40

3.7 Basic Block Diagram Of PID Controller 44

3.8 Simulation Block Diagram of DC Motor System 47

3.9 Simulink of EDM Process System 48

3.10 Updated Particle Position in its Search Space 53

3.11 Implementation of PSO to Maximize MRR 55

3.12 Search Space 56

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4.1 Relation Between Gap Width and Ignition Delay Time 59

4.2 Material Removal Rate Versus Pulse On Time (at =

4A)

60

4.3 Material Removal Rate Versus Pulse On Time (at =

6A)

60

4.4 Material Removal Rate Versus Pulse On Time (at =

8A)

61

4.5 Material Removal Rate Versus Pulse On Time (at =

12.5A)

61

4.6 Material Removal Rate Versus Pulse On Time (at =

18A)

62

4.7 Material Removal Rate Versus Pulse On Time (at =

25A)

62

4.8 Material Removal Rate Versus Pulse On Time (at =

36A)

63

4.9 Material Removal Rate Versus Pulse On Time (at =

50A)

63

4.10 Armature Current of DC Motor 64

4.11 Motor Torque 65

4.12 Velocity of DC Motor 66

4.13 Electrode Position 67

4.14 Electrode Velocity 67

4.15 Error Between The Reference and Average Voltage Gap 68

4.16 Effect of each parameter on the waveform. 69

4.17 Configuration of Pulse Generator used in this study 70

4.18 multi-pulse generated by pulse generator block 72

4.19 Configuration of Band-Limited White Noise used in this

study

72

4.20 Schematic EDM system with Single pulse without Noise 72

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4.21 Schematic EDM system with Single pulse with Noise 73

4.22 Input and Electrode Position with Single pulse without

Noise

74

4.23 Input and Electrode Position with Single pulse with Noise 74

4.24 Schematic EDM system with Multi-Pulse without Noise 75

4.25 Schematic EDM system with Multi-Pulse with Noise 75

4.26 Input and Electrode Position with Multi Pulse without

Noise (for 0.01 ms)

76

4.27 Electrode Position with Multi Pulse without Noise (for 0.05

ms)

76

4.28 Input and Electrode Position with Multi Pulse without

Noise (for 0.04 ms)

77

4.29 Electrode Position with Multi Pulse with Noise (for 0.04

ms)

79

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LIST OF ABBREVIATIONS

EDM - Electrical Discharge Machining

MRR - Material Removal Rate

PSO - Particle Swarm Optimization

RC - Resonant Converter

ZN - Ziegler-Nichols

GA - Genetic Algorithm

ANN - Artificial Neural Network

DC - Direct Current

ACO - ANT Colony Optimization

DE - Differential Evolution

PID - Proportional Integral Derivative

PSD - Power Spectral Density

PI - Proportional Integral

IAE - Integral Absolute Error

DOE - Design of Experiment

DSP - Digital Signal Processor

AR - Auto Regressive

MD - Molecular Dynamics

xv

PMEDM - Powder Mixed Electric Discharge Machining

FEM - Finite Element Method

DOA - Dimensional Analysis

RERF - Reduced Erosion Rate Factor

WEDM - Wire Electrical Discharge Machined

WLT - White Layer Thickness

ANFIS - Adaptive Neuro-Fuzzy Inference

FL - Fuzzy Logic

S/N - Signal to Noise

TWR - Tool Wear Ratio

CLA - Centre Line Average

ANOVA - Analysis of Variance

EW - Electrode Wear

MOGA - Multi Objective Genetic Algorithm

MOPSO - Multi Objective Particle Swarm Optimization

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LIST OF APPENDICES

APPENDIX TITLE PAGE

A Source MRR Cost Function 85

B Source Code for PSO Optimization 86

1

CHAPTER 1

INTRODUCTION

1.1 Background of Study

Electrical discharge machining (EDM) is a nonconventional milling in which

there is no contact between electrode and work piece during the material removal

process. Hence no press is needed in this procedure; it is convenient for most work

pieces even harder than electrode. So this method is usually used for grinding of high

solidity steel and also any kinds of metal alloy. Moreover, with EDM we can

produce complicated and precise holes and hovels which cannot be produced with

traditional milling methods. During the milling process a series of stochastic sparks

are produced when the distance of electrode from work piece is very small just about

10 to 50 microns. Spark is a process in which current discharge occurs suddenly with

high frequency. Then, the current flows through the gap between electrode and work

piece filled with dielectric fluid. Finally, electrical current changes to thermal energy

and melts the work piece. Furthermore, it can be observed that use of EDM for

biomedical science is in progress these days due to its outstanding application in

lubrication of implant joint. Elders or patients who suffer from diseases such as

osteoarthritis, rheumatoid arthritis, bone tumors and traumas need to implant joints.

Moreover, in many cases damaged joint will be replaced with metal head and cup.

Besides that, to prolong life span of joint we can use EDM to create holes in microns

2

for lubrication. If we reach a good surface finish the lump of fluid in holes not only

eases movement of metal head on metal joint but also improves the life span.

Figure 1.1 : Electrical Discharge Machine (EDM)

(Canadian Industrial Machinery, March 1, 2010)

Figure 1.2 : Small hole EDM drilling (MILCO Wire EDM, 2010)

There are many types of EDM such as Milling, grinding, Abrasive Grinding,

Wire Grinding, Wire EDM and Die-sinking EDM. However, Wire EDM and Die-

sinking EDM have more usages compared to other. In this project Die-sinking EDM

will be emphasized. Die-sinking EDM consists of servo system with DC motor, gap

3

voltage and current pulse power generator. The objectives that will be discussed in

EDM model are position control of DC motor, material removal rate and surface

finish. DC motor should be controlled in a definite distance about 10 to 50 microns

to maintain the gap. The definition of material removal rate is dimension of holes

produced divided by + . Due to high hardness of materials sometimes

machining becomes very time consuming. In addition, decreasing the duration of

machining or inversely improving of MRR is aimed. Another problem is the creation

of craters on the work piece surface caused by discontinuous current and leads to the

surface erosion.

The two most common EDM systems are as illustrated in Figure 1.1 and

Figure 1.2 are:

1. Die-sinking EDM: it is used in high precision machining of metal

such as micro-hole machining of metals.

2. Wire EDM: in this type a continuous wire is used as the electrode to

cut an electrically conductive workpiece for through-hole machining.

4

Figure 1.3 : Simple Structures of Die-Sinking EDM System (Ming, C. L. 2012)

Figure 1.4 : Simple Structures of Wire EDM System (Ming, C. L. 2012)

5

Basically, EDM system consists of the following subsystems as shown in

Figure 1.5:

1. DC Power Supply

2. Servo Mechanism System

3. Flushing System

4. Dielectric Fluid

5. Electrode and workpiece

Figure 1.5 : Die Sinking EDM With Main Parts (Yahya A. 2005)

Figure 1.6 illustrates the voltage waveform, current flow and pulse train from

the top to the bottom respectively. As be presented in this figure the high voltage,

open circuit voltage is created when the timer turns on. So the ionization occurs

through the gap. At the end of delay time the resistance of dielectric falls due to a

high electric field appears in the gap. So a high current flows between the

electrode and work piece during . As well as the voltage decreases to

considered as working voltage. Therefore, machining takes place during . After

this time the characteristic of dielectric which broke down through will be

improved during and there is an interruption in the current. The volume of

6

material removed is directly proportional the quantity and duration that current

flows. Since the ionization starts with some delay, the current appear only some part

of . For instance, in second pulse only small amount of metal has been removed

and in the following pulse there is no current during . The delay time is related

to the gap between the electrode and work piece.

Figure 1.6 : Voltage Waveform, Current Flow and Pulse Train

(Oscar, C., et al. 2009)

1.2 Problem Statement

Throughout the machining process in Electrical Discharge Machining (EDM),

a series of stochastic sparks are produced when the gap between electrode and work

piece is very small just about 10 to 50 microns. Controlling this gap in micro

dimension not only depends on electrode position but also work piece surface

changing during material removal process. This project contains some enhancements

comparing the other works relating to the EDM simulation. In this study an indirect

measurement of surface position is carried out by implementing voltage average gap

7

model. And Simulink models have been done to simulate the dynamic behavior of

EDM system. Since Material Removal Rate (MRR) can also affect the gap, our aim

is to have higher MRR with the optimization technique.

1.3 Objectives of Study

There are total of three objectives to be achieved upon the completion of this

project. The objectives of this study are:

(i) Simulation of EDM process including three subsystems: breakdown model,

material removal rate and average voltage gap model.

(ii) Simulation of single loop servo system (position control and velocity

control).

(iii) Optimization of EDM Process using Particle Swarm Optimization (PSO).

1.4 Scope and Limitations of Study

EDM model is considered a combination of two main models, servo system

and EDM process. This project work focus on EDM process. It starts by conducting

literature review in order to understand the method of the design approach. Then

proceed to simulate the combination of two main models in Matlab. In simulation

the model of the servo system was developed by using the Transfer Function

approach. As well as the EDM process model was simulated in Matlab consist of

three sub-models, material removal rate model, the breakdown model and the

average gap voltage model. In this research the mathematical model which has been

already developed by using Dimensional Analysis technique by (Yahya A. 2005) is

8

selected to get the mathematical model of the material removal rate. And also only

two simple PID controllers are used for improving DC servo motor system

performance. Finally the PSO technique will be implemented to optimize the EDM

process to have higher MRR with proper choosing of EDM parameters.

80

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