INTEGRATED CONTROL MECHANISM OF ELECTRICAL...
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INTEGRATED CONTROL MECHANISM OF ELECTRICAL DISCHARGE MACHINING SYSTEM FOR HIGHER MATERIAL REMOVAL RATE TRIAS ANDROMEDA A thesis submitted in fulfilment of the requirements for the award of the degree of Doctor of Philosophy (Electrical Engineering) Faculty of Electrical Engineering Universiti Teknologi Malaysia MAY 2015
Transcript of INTEGRATED CONTROL MECHANISM OF ELECTRICAL...
INTEGRATED CONTROL MECHANISM OF ELECTRICAL DISCHARGE
MACHINING SYSTEM FOR HIGHER MATERIAL REMOVAL RATE
TRIAS ANDROMEDA
A thesis submitted in fulfilment of the
requirements for the award of the degree of
Doctor of Philosophy (Electrical Engineering)
Faculty of Electrical Engineering
Universiti Teknologi Malaysia
MAY 2015
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This thesis is gratefully dedicated to my beloved family for their prayers and
supports.
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ACKNOWLEDGEMENT
First and foremost, I would like to thank Almighty Allah, Who is the most
Gracious, Merciful, Omniscient and Omnipotent for the continuous blessing and
giving me the strength and opportunity completing this study.
I wish to give my special thanks to both my project supervisor Dr. Azli
Yahya, and co-supervisor Dr. Nor Hisham Haji Khamis who gave constant guidance,
support and valuable advice with their brilliant idea on many important occasions
during the past years.
I am incredibly grateful to my family. My parents Abdur Rachim and
Sukartiyah, my beloved wife Yeny Mainita and my lovely daughters and son Alfa
Nadhya Maimanah, Beta Nadhya Maimunah and Gamma Nasim, my parents in law
Nasjah Djamin and Umi Nafiah, my brothers and sisters Ika, Dewi, Delta, Zeta, Eta,
Mega, Rini, Ipah and Rizal that gave their unconditional love, emotional support, and
their patient during all these years. I am also thankful to my colleagues and friends,
Teguh Santoso, Suwarno, Irsyad, M. Facta, Munawar, Iwan Setiawan, Hari Agung,
Kartiko, Ade, Nazriah, Liyana, Nima, Razak and Nabil for many valuable
discussions, their important advice, their encouragement, and the fun times in the
completion of this study.
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ABSTRACT
A servo control system in Electrical Discharge Machining (EDM) system is a
control system with an appropriate control algorithm to position electrode on a
particular distance from workpiece during machining process. The gap between the
electrode and the workpiece is in the range of 10 – 50 µm. This ideal gap is achieved
by applying an appropriate control algorithm to the servo control system of the
EDM, and maintaining this gap will improve the Material Removal Rate (MRR)
during the machining process. A considerable number of unique methods were
proposed in the control algorithm in order to bring the electrode to the optimum
position. This research proposes a new method called Integrated Control Mechanism
(ICM) to improve the MRR of the EDM system. A rotary encoder is used as an
additional mechanical sensor for the feedback control system in order to limit the
electrode movement. Modelling of EDM is further investigated to predict the MRR
parameter and optimization of electrode control position. A Neural Network system
is used to predict MRR where Particle Swarm Optimization (PSO) and Differential
Evolution (DE) are studied and simulated to optimize the Proportional Integral
Derivative (PID) control parameters for the EDM system. Research conducted shows
that the proposed Feed Forward Artificial Neural Network improves the accuracy of
prediction in determining MRR by 2.92% and PID parameter optimization is
successfully applied either using PSO or DE. The ICM is successfully implemented
and the result shows that MRR is higher when compared to the normal machining
process.
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ABSTRAK
Sistem kawalan servo dalam sistem Pemesinan Nyahcas Elektrik (EDM)
adalah sistem kawalan dengan algoritma kawalan yang sesuai untuk meletakkan
elektrod pada jarak tertentu dari bahan kerja semasa proses pemesinan. Jarak antara
elektrod dan bahan kerja adalah dalam lingkungan 10 - 50 μm. Jarak ideal ini dicapai
dengan menggunakan algoritma kawalan yang sesuai untuk sistem kawalan servo
daripada EDM, dan dengan mengekalkan jarak ini akan meningkatkan Kadar
Hakisan Bahan (MRR) semasa proses pemesinan. Sebilangan besar kaedah unik
telah dicadangkan dalam algoritma kawalan untuk membawa elektrod ke kedudukan
yang optimum. Kajian ini mencadangkan satu kaedah baru yang dikenali sebagai
Mekanisme Kawalan Bersepadu (ICM) untuk meningkatkan MRR sistem EDM.
Pengekod putar digunakan sebagai pengesan mekanikal tambahan bagi sistem
kawalan maklum balas bagi menghadkan pergerakan elektrod. Pemodelan EDM
seterusnya dikaji guna untuk meramalkan parameter MRR dan mengoptimumkan
kedudukan kawalan elektrod. Sistem Rangkaian Neural digunakan untuk
meramalkan MRR di mana Pengoptimuman Kerumunan Zarah (PSO) dan Evolusi
Pembezaan (DE) dikaji dan disimulasi untuk mengoptimumkan parameter kawalan
Kadar Kamir Pembeza (PID) untuk sistem EDM tersebut. Penyelidikan yang
dijalankan menunjukkan bahawa Jaringan Saraf Buatan Suap Depan yang
dicadangkan dapat meningkatkan ketepatan ramalan dalam menentukan MRR
sebanyak 2.92% dan pengoptimuman parameter PID berjaya digunakan sama ada
menggunakan PSO atau DE. ICM berjaya dilaksanakan dan keputusan menunjukkan
bahawa MRR adalah lebih tinggi jika dibandingkan dengan proses pemesinan biasa.
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TABLE OF CONTENTS
CHAPTER TITLE
PAGE
DECLARATION
DEDICATION
ACKNOWLEGMENT
ABSTRACT
ABSTRAK
TABLE OF CONTENTS
LIST OF TABLES
LIST OF FIGURES
LIST OF SYMBOLS
LIST OF ABBREVIATIONS
LIST OF APPENDICES
ii
iii
iv
v
vi
vii
xii
xiii
xviii
xx
xxii
1 INTRODUCTION
1
1.1 Background of Study
1
1.2 Problem Statement 2
1.3 Objectives
3
1.4 Scope of Research
4
1.5 Significance of Study
5
1.6 Summary of Methodology 5
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2 LITERATURE REVIEW
8
2.1 Introduction
8
2.2 Electrical Discharge Machining (EDM) System
9
2.2.1 Types of EDM
15
2.2.1.1
Die-sinking EDM 16
2.2.1.2 Wire-cut EDM 17
2.2.1.3 Grinding EDM 17
2.2.2 Power Generator Configurations for EDM 18
2.2.2.1 Relaxation Generator 19
2.2.2.2 Pulse Power Generator 20
2.2.3 Machining Performance Evaluation 22
2.3 Control Methods for Electrical Discharge Machining 23
2.3.1 Electrode Control Based on Relative Velocity 24
2.3.2 Electrode Control Based on Gap Voltage 26
2.3.3 Electrode Control Based on Ignition Voltage 28
2.3.4 Electrode Control Based on Acoustic Signals 30
2.3.5 Electrode Control Based on Pulse
Discrimination
36
2.3.6 Electrode Control Based on Fuzzy Logic 43
2.4 Improving Electrical Discharge Machine’s Performance
45
2.4.1 Electrode Design
45
2.4.2 Process Parameter Control
47
2.4.3 EDM Variations 48
2.4.4 Powder Mixed Dielectric Fluid 51
2.4.5 Dry EDM and EDM with Fluid 53
2.4.6 Multi-spark EDM 54
2.5 Summary 55
3 MODELLING AND SIMULATION IN EDM SYSTEM
58
3.1 Methodology for Modelling and Simulation
59
3.2 Description of Control Loop in EDM System
59
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3.3 Gap Control in EDM System
60
3.4 Model of EDM Process 66
3.5 Electrode Control Position 68
3.5.1 Results of Linear Model
70
3.5.2 Results of Stochastic Model without Limiter
71
3.5.3 Results of Stochastic Model including Limiter
72
3.6 Artificial Neural Network for Material Removal Rate
(MRR) Modelling
74
3.6.1 Experimental Data of EDM Process
78
3.6.2 New Training Algorithm in Feed Forward
Artificial Neural Network
81
3.6.3 Analysis, Results and Discussion 83
3.7
Summary 86
4 OPTIMIZATION IN THE EDM SYSTEM
88
4.1 Overview
89
4.2 Methodology for Optimization in EDM
90
4.3 Particle Swarm Optimization (PSO)
91
4.4 Differential Evolution (DE) 93
4.4.1 Initialization
93
4.4.2
Mutation
94
4.4.3 Recombination 94
4.4.4 Selection 95
4.5 Implementation of PID Controller Tuning on EDM 96
4.6 Simulation Results
97
4.6.1 Particle Swarm Optimization Tuning Simulation
Results
97
4.6.2 Differential Evolution Tuning Simulation
Results
99
4.7 Analysis for Optimization in EDM System
101
4.8 Summary
102
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5 INTEGRATED CONTROL SYSTEM IN THE EDM
MACHINING
103
5.1 Overview
103
5.2 Methodology for Integrated Control in EDM
104
5.3 Laboratory Scaled EDM System
104
5.3.1 EDM Pulse Power Supply
108
5.3.1.1 DC Supply Circuit 109
5.3.1.2 Power Resistor 110
5.3.1.3 Power Semiconductor Switch 111
5.3.1.4 Pulse Timer 112
5.3.2 Motor Control 113
5.3.3 DC Motor Driver 115
5.4 Flushing
117
5.5 Integrated Control Mechanism (ICM)
117
5.5.1 Encoder Signal Feedback as Limitation
Mechanism
118
5.5.2 dsPIC 30F4011 Based System 120
5.5.3 Incremental Rotary Encoder 121
5.5.4 Algorithm to Limit the Electrode Movement 123
5.6 Summary
126
6 EXPERIMENTAL WORK
127
6.1 Experimental Work Set Up
128
6.2 Material Removal Rate Measurements
132
6.3 Experimental Work of Brass Workpiece and Copper
Electrode
134
6.4 Experimental Work of Steel Workpiece and Copper
Electrode
136
6.5 Delay Time Measurement
137
6.6 Analysis for Experimental Work Results
142
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7 CONCLUSION AND FUTURE WORKS
146
7.1 Summary of Research and Conclusions
146
7.2 Suggestions for Future Works
149
7.2.1 Prediction Machining Time 150
7.2.2 Digital Filter Application to Improve Voltage
Drop Signal
150
7.2.3 Digital Controller Application to Drive the
Electrode Movement
150
REFERENCES
152
Appendices A-B
162-165
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LIST OF TABLES
TABLE NO. TITLE PAGE
2.1 Summary of electrode control method
56
3.1 Finishing Machining Parameters
80
3.2 Roughing Machining Parameters
80
3.3 Testing data and results for FFANN
84
3.4 Testing data and results for ANFFANN 86
4.1 Experimental result for P, PI, PID and PD by PSO 97
4.2 Experimental result for P, PI, PID and PD by DE 100
5.1 Typical number for Pulse Power EDM 111
5.2 Signal output of motor control 115
5.3 Label function of MD30B 116
6.1 Experimental data for Brass workpiece 133
6.2 MRR results for Brass workpiece and Copper electrode 135
6.3 MRR results for Steel workpiece and Copper electrode 136
6.4 Delay time machining without ICM for Igap = 2.6A 139
6.5 Delay time machining with ICM for Igap = 2.6A 139
6.6 Delay time machining without ICM for Igap = 5.8A 140
6.7 Delay time machining with ICM for Igap = 5.8A 140
6.8 Delay time machining without ICM for Igap = 9.6A 140
6.9 Delay time machining with ICM for Igap = 9.6A 141
6.10 Average delay time for machining 141
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LIST OF FIGURES
FIGURE NO. TITLE PAGE
1.1 Flow of research work 6
2.1 Classification of EDM processes 10
2.2 Basic elements of an EDM system
11
2.3 Phase for ignition
12
2.4 Phase for discharge
13
2.5 Interval phase between discharges
13
2.6 Die Sinking EDM process
16
2.7 Wire cut EDM process 17
2.8 Grinding EDM 18
2.9 RC Generator 19
2.10 Voltage and current waveforms for a basic relaxation
circuit
20
2.11 Pulse Power Generator 21
2.12 Voltage, current and pulses waveforms for a basic
Pulse Power Generator
21
2.13 Diagram of electrode control proposed by Martin and
Vuache
24
2.14 Dither circuit arrangement proposed by Bell et al. 27
2.15 Schematic of Arc protection circuit design proposed
by Syria et al.
29
2.16 Sound spectrum of sound energy emitted vs
frequency (KHz)
31
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2.17 Waveforms occurring in an EDM machine and in
amplifier coupled to a piezoelectric transducer in the
vicinity of the machine
32
2.18 Schematics and block diagram of the ultrasound
transducer
33
2.19 Ultrasound sensor in EDM setup arrangement 1 34
2.20 Ultrasound sensor in EDM setup arrangement 2 35
2.21 Gap voltage level classification for different
thresholds
37
2.22 Pulse diagram under different time durations 39
2.23 Flow chart of the pulse discrimination program 40
2.24 Gap voltage and gap current thresholds of the
proposed pulse discrimination
41
2.25 Flow chart of the pulse discrimination method 42
2.26 Pulse discrimination simulation block diagram 42
2.27 Various design illustration for 3D form, frame and
plate type tool
46
2.28 Vibratory, rotary and vibro-rotary electrode 49
2.29 Basic Principle of PMEDM 51
2.30 Basic Principle of Dry EDM 53
2.31 Basic Principle of Multi-spark EDM 55
3.1 EDM system model 60
3.2 Die Sinking EDM system 61
3.3 EDM control block diagram 62
3.4 Practical gap control of EDM process 63
3.5 Gain distribution of the feedback device 65
3.6 Block diagram of the EDM gap control system 65
3.7 Breakdown curve 67
3.8 Linear model of EDM process 68
3.9 Block diagram of EDM system 68
3.10 Block diagram EDM system in Simulink 69
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3.11 Electrode position response of EDM system using
linear model
70
3.12 Gap voltage response of EDM system using linear
model
70
3.13 Gap response of EDM system using stochastic model 71
3.14 Gap voltage response of EDM system using
stochastic model
72
3.15 Gap response of EDM system using stochastic model
with limiter mechanism
73
3.16 Gap voltage response of EDM system using
stochastic model with limiter mechanism
73
3.17 Architecture of the neural network model 75
3.18 Single neuron 76
3.19 Multiple neurons 77
3.20 Method used to measure the material removal rate 79
3.21 A new architecture of neural network model
proposed
82
3.22 Comparisons between target and output neural
network using FFANN
83
3.23 Comparison between actual and predicted value using
FFANN
84
3.24 Comparison between target and output neural
network using ANFFANN
85
3.25 Comparison between actual and predicted value using
ANFFANN
86
4.1 Block diagram of EDM servo control system 89
4.2 Optimization of PID controller block diagram 90
4.3 PSO-PID flow chart for EDM 92
4.4 Flow graph of differential evolution algorithm 93
4.5 Electrode movement response of PSO PID 98
4.6 Zoomed of initial response of PSO PID 98
4.7 Zoomed of steady state response of PSO PID 99
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4.8 Electrode movement response of DE PID 100
5.1 Laboratory scaled EDM die sinking 105
5.2 Laboratory scaled EDM diagram 105
5.3 Laboratory scaled EDM dual loop block diagram 106
5.4 Pulse power EDM diagram 108
5.5 DC supply circuit 109
5.6 Transformer, Bridge Diode and Capacitor 109
5.7 Power resistor bank 110
5.8 Power semiconductor switch and mosfet driver 112
5.9 Block diagram of timer 113
5.10 Pulse timer signal 113
5.11 DC motor control circuit 114
5.12 DC motor driver 116
5.13 General idea of limitation 119
5.14 Block diagram of ICM 120
5.15 DC motor limitation (force stop) mechanism 121
5.16 Encoder mounted to a DC motor 122
5.17 Illustration of output signals from encoder 122
5.18 Dip Switch and resolutions of AMT 102 123
5.19 Flowchart for limiter algorithm 125
6.1 Experimental work set up 128
6.2 Placement of probes in the EDM machine 129
6.3 EDM Gap voltage and current waveforms,
time/div=100µs
130
6.4 EDM Gap voltage and current waveforms with
shorter time delay, time/div=100µs
131
6.5 EDM Gap voltage and current waveforms with
longer time delay, time/div=100µs
131
6.6 Experimental results for Brass workpiece 134
6.7 MRR with and without ICM System for Brass
workpiece
135
6.8 Experimental results for Steel workpiece 136
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6.9 MRR with and without ICM for Steel workpiece 137
6.10 Ignition delay time measurements 138
6.11 Average delay time measurements 142
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LIST OF SYMBOLS
α - Material properties factor
c1, c2 - Inertia coefficients
d - Dimension variables optimization problems
d(s) - Actual position of electrode
dd(s) - Disturbed distance
de(s) - Actual gap length
dm(s) - Distance caused by metal removal
e(s) - Deviation between r(s) and feedback signal y(s)
fr(s) - Feed rate
F - Mutation scaling factor
G - Generation number
Gbest - Best particle in the group
H(s) - Gap controller
I, Igap
- Current, Maximum discharge current
ks
- Magnification constant
Kd - Derivative gain
Ki - Integral gain
Kp - Proportional gain
n - Number of particle in the group
N - Nonlinear and time varying gain
�n - Natural frequency
� - Damping ratio
Pbest - Particle with best value
r1, r2 - Random value
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r(s) - Reference
t, tm - Time, machining time
tD - Ignition delay time
Toff - Off time period
Ton - On time period
Ui,G - Trial vector in DE
vi, vt+1 - Velocity of particle, new value of particle’s velocity
V - Voltage
V(s) - Actual speed
Vg - Gap voltage
Vi,G - A donor vector in DE
Vs - Voltage reference
Vsd - Deviation between Vs and Vg
Vspark - Voltage level when the spark is occurred
w - Inertia weight
Wa - Weight of workpiece material after machining
Wb - Weight of workpiece material before machining
xi, xt+1 - Particle, updated particle position
Xi,G - Vector as a candidate solution in DE
y(s) - Feedback signal
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LIST OF ABBREVIATIONS
AC - Alternating Current
ANFFANN - A New Feed Forward Artificial Neural Network
ANN - Artificial Neural Network
CB - Circuit Breaker
CCW - Counter Clock Wise
CNC - Computer Numerical Control
CPU - Central Processing Unit
CW - Clock Wise
DC - Direct Current
DE - Differential Evolution
DSP - Digital Signal Processor
EDG - Electrical Discharge Grinding
EDM - Electrical Discharge Machining
EWR - Electrode Wear Rate
EWW - Electrode Wear Weight
FFANN - Feed Forward Artificial Neural Network
GA - Genetic Algorithm
HMP - Hybrid Machining Process
IAE - Integral Absolute Error
ICS - Integrated Control System
ISM - Integrated Servo Mechanic
LCD - Liquid Crystal Display
MRR - Material Removal Rate
P - Proportional
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PD - Proportional Derivative
PI - Proportional Integral
PID - Proportional Integral Derivative
PMEDM - Powder Mixed Electric Discharge Machining
PPG - Pulse Power Generator
PSO - Particle Swarm Optimization
PWM - Pulse Width Modulation
QEI - Quadrature Encoder Interface
RC - Resistor Capacitor
RISC - Reduced Instruction Set Computer
SR - Surface Roughness
TWR - Tool Wear Rate
WRW - Workpiece Removal Weight
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LIST OF APPENDICES
APPENDIX TITLE PAGE
A List of Author’s Publications
162
B List of Author’s Awards
165
CHAPTER 1
INTRODUCTION
1.1 Background of Study
Along with the rapid growth in the industrial machining processes, Electrical
Discharge Machining (EDM) has gained widespread recognition as a spark erosion
machine which can provide important practical and economic advantages in many
fields, notably in production engineering and tool making. An EDM system is a
significant machining technique used for finishing parts, for example, in an
automotive industry, aerospace and other commercial components [1]. This
technique allows for processing high strength alloys and ceramics polycrystalline
diamond ultra-hard conductive material because the machining process is not directly
affected by the substance of the workpiece. Ceramic (ultra-hard conductive material)
also can be machined using this method. In addition, with the use of hard material in
Biomedical Engineering, the EDM process contributes greatly to this field.
The machining performance indicator of EDM machines, which includes
Material Removal Rate (MRR), Tool Wear Rate (TWR) and Surface Roughness
(SR) are important values in order to enhance the capability of the EDM machine.
Many studies have been carried out in order to discuss and discover a suitable
2
approach to improve the performance of EDM machines. Efforts have also been
made to enhance the performance of the power generator, controller to adjust the
position of the electrode gap, materials which are suitable for use as an electrode and
workpiece, the flushing system, engineered dielectric materials, and vibrating
electrodes. Researchers have also studied methods of implementing the multi-spark
in EDM to increase the MRR [2-5]. Furthermore, several studies have reported
various simulations and models for MRR prediction [6-9]. Among the three
parameters above, the most dominant is MRR, since MRR directly affects the
production cost even though the value of MRR is not the only indicator that
measures the performance of EDM machines. Higher MRR means the faster the
machining process, that will save time and production cost. The value of MRR is
strongly influenced by the performance of the control system to position the
electrode towards the workpiece during the machining process. Therefore, using a
control mechanism in order to increase the MRR is very challenging. In the EDM
process, electrical parameters such as current, frequency, and duration of injected
pulse (Ton and Toff) influence the MRR, while mechanical movement of the electrode
position also has a significant effect on machining output, MRR. Estimating
electrical parameters to predict MRR is possible by modelling, however, the
available models from previous studies need to be improved. On the other hand, the
challenge in research is to maintain the distance of the electrode towards the
workpiece, therefore a shorter machining time could be achieved with better MRR.
1.2 Problem Statement
Material removal rate (MRR) is an important parameter in determining the
performance of an Electrical Discharge Machining (EDM) System. MRR is
determined by the ability of the controller to position the electrode to the workpiece
or, in other word, to regulate the gap between the electrode and the workpiece.
Currently controllers rely on the feedback voltage as an input to the controller that
has a stochastic process in nature, so sometimes the position of the electrode will be
3
too far away from the workpiece. This situation causes the ignition delay time
becoming too large and eventually resulted in missing spark, thus reducing the MRR.
To overcome this problem, an additional feedback sensor is proposed that monitors
the electrode movement mechanically which is not affected by the stochastic nature
of the gap voltage. When the electrode is moving upwards and too far away from the
workpiece, the retraction movement will be forced to stop as to maintain the gap. By
maintaining a suitable gap, an appropriate delay time is achieved while machining
take place.
1.3 Objectives
Based on the current issue of EDM, there is a demand to research designing
EDM’s servo control system. The expected outcome of this work would be to
maintain the electrode position towards the workpiece that increases the material
removal rate during the machining process. During the research work, several
objectives are listed as follows:
(i) To develop an EDM model to predict MRR using Artificial Neural
Network (ANN).
(ii) To optimize the PID (Proportional Integral and Derivative) controller
by application of Particle Swarm Optimization (PSO) and Differential
Evolution (DE)
(iii) To improve Material Removal Rate (MRR) by applying additional
mechanical sensor to control the electrode movement within the
acceptable limit.
4
1.4 Scope of Research
Scope of research for this study is outlined as follows:
(i) A critical literature review of the effort to improve MRR by
considering several EDM systems. In this regard, fundamental theory,
control mechanism and EDM performance are reviewed.
(ii) An attempt to predict the MRR by considering related parameters is
carried out by developing a model based on the Artificial Neural
Network. The related parameters become an input to the model and
MRR is the output. In this modelling process, experimental data are
obtained from past research to train and investigate the capability of
the MRR prediction.
(iii) An optimization process to achieve optimum PID parameters is
carried out by PSO and DE algorithms. Several PID controller
formations, i.e., P, PD, PI and PID are optimized using Integral
Absolute Error as an objective function.
(iv) The implementation of ICM is applied to the laboratory scaled EDM
machine. The limitation is activated when the electrode retraction is
too wide. This forces the gap to be around 10 – 50 microns. During
the experimental work Copper is used as the electrode, whereas Brass
and Steel are used as the workpieces. MRR and ignition delay time
are measured for comparison and analysis.
(v) Experimentation of machining process for Copper-electrode and Brass
or Steel-workpiece in order to measure MRR and ignition delay time.
5
1.5 Significance of Study
Higher MRR is the key performance of the EDM process in order to
accomplish faster machining. The study of MRR is important as it directly affects the
duration of the time processes and consecutively influences the cost of production.
Moreover, the study to predict MRR is significant in estimating the time of the EDM
process based and based on the experimentation parameters, the ANN is expected to
accurately predict the MRR
The value of MRR is essential to the selection of suitable control systems for
positioning the electrode accordingly. Therefore the study of the acceptable distance
between the electrode and the workpiece is necessary. An incremental rotary encoder
as a sensor to limit the electrode movement is proposed as part of the Integrated
Control Mechanism (ICM) to keep retraction in an effective range. The application
of Particle Swarm Optimization (PSO) and Differential Evolution (DE) in order to
gain maximum optimization of PID is crucial to ensure the best performance of PID
as a controller to govern the DC motor.
1.6 Summary of Methodology
During this research work, some activities are arranged in order to reach the
main objective of this research and generally the flow is described as shown in
Figure 1.1.
6
Figure 1.1 Flow of research work.
Generally, this research work is conducted through modelling, simulation and
implementation. The following methodology will be implemented in order to
accomplish the objectives of this research:
(i) Literature review of EDM systems
A study of previous EDM work, especially servo mechanics used for
controlling electrode positions is reviewed. Their strong points and
limitations based on several evaluating factors are emphasized. In
addition, finding a gap in this field becomes the main concern of this
review, particularly on the issue of controlling electrodes during
machining in EDM systems.
(ii) Modelling and predicting MRR of EDM system using the Artificial
Neural Network.
In order to achieve a deep understanding of EDM systems, designing a
proper and accurate model of the EDM control system is essential. A
simulation is conducted to determine a suitable model which affects the
machining performance. The Artificial Neural Network (ANN) is used to
model the EDM system, especially in terms of the determination of MRR.
7
(iii) Optimization of PID controller gain using Particle Swarm Optimization
PSO) and Differential Evolution (DE) Algorithms.
Once a functional EDM model is developed, it can be used to simulate the
EDM control system which describes the dynamic of the EDM process.
The PID controller, which is used in this control system, will be tuned in
order to get an optimum result. Particle Swarm Optimization and
Differential Evolution algorithms are applied and compared. This
optimization process will provide a reference when implementing the
integrated servo mechanics methods throughout this research.
(iv) Integrated Control Mechanism of the EDM system
An Integrated Control Mechanism (ICM) of the EDM system will be
implemented to improve the machine’s capability. Its performance is
evaluated through comparing machining results between the existing
control system and the proposed method. The proposed technique uses an
incremental rotary encoder as an additional sensor to control the electrode
movement within the acceptable limit. The effectiveness of this proposed
controller is validated through some experiments conducted using Copper
electrode and Brass and/or Steel workpieces.
REFERENCES
1. Ho, K.H. and S.T. Newman, State of the art electrical discharge machining
(EDM). International Journal of Machine Tools and Manufacture, 2003.
43(13): p. 1287-1300.
2. Kunieda, M. and H. Muto, Development of Multi-Spark EDM. CIRP Annals -
Manufacturing Technology, 2000. 49(1): p. 119-122.
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