EXPERIMENTAL INVESTIGATIONS OF ELECTRO CHEMICAL MICRO...
Transcript of EXPERIMENTAL INVESTIGATIONS OF ELECTRO CHEMICAL MICRO...
EXPERIMENTAL INVESTIGATIONS OF
ELECTRO CHEMICAL MICRO MACHINING
ON NICKEL AND ITS ALLOYS
A THESIS
Submitted by
SARAVANAN D
in partial fulfillment for the requirement of award of the degree
of
DOCTOR OF PHILOSOPHY
FACULTY OF MECHANICAL ENGINEERING
ANNA UNIVERSITY
CHENNAI 600 025 JULY 2012
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ABSTRACT
The advancement in the field of mechanical engineering is very
essential to meet the growing demands of the industry. In particular the
demand for alloy materials having high hardness, toughness and impact
resistance has grown multi fold due to high level of design constraints. Electro
Chemical Micro Machining (ECMM) machines are used to cut metals of any
hardness or that are difficult or impossible to cut with traditional methods.
These machines also specialize in cutting complex contours or geometries that
would be difficult to produce using conventional cutting methods. Machine
tool industry has made exponential growth in its manufacturing capabilities in
last decade but these machine tools are yet to be utilized at their full potential
due to inadequate data on optimum operating parameters. The problem of
arriving at the optimum levels of the operating parameters on Material
Removal Rate (MRR) has attracted the attention of researcher to take-up
research in this area.
The literature survey has revealed that a little research has been
conducted to obtain the combination of optimal levels of machining
parameters that yield the maximum MRR and best machining quality in
machining of difficult to machine materials like Nickel, Super Duplex
Stainless Steel (SDSS) and Inconel 600. It is difficult to achieve required
quality in parts machined by ECMM process consistently with improper
selection of levels of various process parameters. The selection of
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optimum parameters for machining of Nickel and its alloys is a tough
challenge for Aero space, Electronics and Bio-medical industries. Hence,
the Nickel and its alloys are specifically selected for this research.
The objective of the present research work is to investigate the
effects of the various ECMM process parameters on the MRR and
dimensional deviation to obtain the optimal sets of process parameters to
produce efficient high quality machining.
The Taguchi technique has been used to investigate the effects of
the ECMM process parameters and subsequently to predict sets of optimal
parameters for maximum MRR. The working ranges and levels of the
ECMM process parameters are found using one factor at a time approach.
The ANOVA has been used to find optimal combination of machining
parameters. The confirmation experiments are conducted based on the
predicted levels of process parameters. The coherence between confirmation
experiment results, ANOVA and Genetic Algorithms is analyzed.
The experimental setup for this research on ECMM consists of Electrolyte
tank, non-corrosive work holding platform, Feeding device actuated with stepper
motor, Microprocessor based machine control unit and Power supply system.
This setup is capable of maintaining an accuracy of 4 microns in the machining
process. The experiments were conducted on 0.15 mm thick specimens made up
of Nickel, SDSS and Inconel 600 to find the optimum combination of machining
parameters viz., Electrolyte concentration, Machining Voltage, Machining
Current, Duty cycle and Frequency. The following levels of the process
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parameters are selected for the present study:
Materials Levels Process Parameters EC V C DC F
Nickel I 0.1 3.5 0.1 33.33 30II 0.2 5.0 0.3 50.00 40III 0.3 6.5 0.5 66.66 50
SDSS I 0.40 8.0 0.6 33.33 30II 0.45 9.0 0.8 50.00 40III 0.50 10.0 1.0 66.66 50
Inconel 600 I 0.40 8.0 0.6 33.33 30II 0.45 9.0 0.8 50.00 40III 0.50 10.0 1.0 66.66 50
EC: Electrolyte Concentration (mol/lit), V: Voltage (Volt), C: Current (Ampere), DC: Duty Cycle (%), F: Frequency (Hz).
The entire set of experiments is carried out in a phased manner. The
experiments in each phase were repeated two times in order to achieve mean
values. The analysis and verification of experimental results using Taguchi
methodology, ANOVA and GA it is concluded that the major factor affecting the
MRR on Nickel is Machining Current, on SDSS is Duty cycle and on Inconel
600 is Electrolyte Concentration. It is inferred from the experiment that the
reduction in % of Nickel present in the alloy, the other processing factors like
Duty Cycle and Electrolyte concentration becomes the major parameter affecting
the MRR.
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ACKNOWLEDGEMENT
I express my heartfelt adulation and gratitude to my supervisor,
Dr. M. Arularasu, Principal, Thanthai Periyar Government Institute of
Technology, Vellore, for his unreserved guidance, constructive suggestions
and outstanding inspiration throughout this research work.
I am grateful to Dr. K. Ganesan, Professor, Mechanical Engineering
Department, PSG College of Technology, Coimbatore, for his
incomparable support in every stage of this research work.
I wish to thank Dr. S.R. Devadasan, Professor, Department of
Production Engineering, PSG College of Technology, Coimbatore, for
providing valuable suggestions. I express my sincere thanks to
Dr. G. Mohankumar, Principal, Park college of Engineering, Coimbatore, for
providing expert guidance throughout this research work.
I am also grateful to Dr. R.M. Arunachalam, for extending facilities
to carry out investigations. Thanks are also due to Dr. P. Asokan,
Professor, Department of Production Engineering, NIT, Trichy, who
provided excellent advices to carryout experimental work.
I am thankful to Dr. J. Jerald, Associate Professor, Department of
Production Engineering, National Institute of Technology, Trichy, for his
timely guidance, support and encouragement during the course of my work.
This prefatory remark will become complete by expressing my
deep sense of gratitude to my dear parents for their blessings, to my wife
R. Ananthi, daughter Ms. S.Vijayashanthy and son S.Veera for their care and
support.
I express my sincere thanks to all those who directly or indirectly
helped at various stages of this research work for its successful completion.
Above all, I humbly offer my sincere indebtedness to the
“ALMIGHTY” for every moment of my life.
(D. Saravanan)
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TABLE OF CONTENTS
CHAPTER NO. TITLE PAGE NO.
CERTIFICATE ii
PROCEEDINGS iii
ABSTRACT v
ACKNOWLEDGEMENT viii
LIST OF TABLES x
LIST OF FIGURES xii
NOMENCLATURE xiv
1. INTRODUCTION
1.1. INTRODUCTION 1
1.1.1. Importance of ECMM in Present Day Scenario 3
1.1.2. Electrochemical Machining 5
1.1.3. Basic Principles of ECMM Process 7
1.1.4. Mechanism of Material Removal in ECMM 7
1.1.5. Advantages of ECMM 10
1.1.6. Disadvantages of ECMM 11
1.1.7. Applications of ECMM 12
1.2. OBJECTIVES OF PRESENT INVESTIGATION 13
1.3. OUTLINE OF THE RESEARCH WORK 15
1.4. STATEMENT OF THE PROBLEM 16
2. LITERATURE SURVEY
2.1. REVIEW OF LITERATURE 17
2.2. OVERVIEW ON ECMM 17
2.3. OUTCOME OF LITERATURE REVIEW 36
3. EXPERIMENTAL DESIGN
3.1 INTRODUCTION 38
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3.2 TAGUCHI EXPERIMENTAL DESIGN AND ANALYSIS 41
3.2.1. Taguchi’s Philosophy 41
3.2.2. Experimental Design Strategy 42
3.2.3. Signal to Noise Ratio 45
3.2.4. Selection of orthogonal array (OA) 49
3.2.5. Assignment of parameters and interaction to the OA 52
3.2.6. Experimentation and data collection 52
3.2.7. Data analysis 53
3.2.8. Parameter classification and selection of optimal levels 53
3.2.9. Prediction of the mean 54
3.2.10. Determination of confidence interval 55
3.3 MACHINING PERFORMANCE EVALUATION 56
3.3.1. Material Removal Rate (MRR) 56
3.3.2 Signal-to-Noise Ratio (S/N Ratio) 57
3.3.3. Analysis of variance (ANOVA) 57
3.3.4. Confirmation Test 62
3.4. GENETIC ALGORITHMS (GA) 63
3.4.1 Introduction 63
3.4.2 Implementation of GA 66
3.4.3 Experimental Validation (GA) 70
4. EXPERIMENTAL SET-UP
4.1 INTRODUCTION 71
4.2 MACHINING SETUP STRUCTURE 72
4.2.1 Work Holding Platform 73
4.2.2 Tool Feeding Device 74
4.2.3 Inter Electrode Gap Control System 75
4.2.4 Electrolyte Flow System 76
4.2.5 Microcontroller Unit 78
4.2.6 Power Supply System 79
4.3 MATERIALS FOR RESEARCH 81
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4.3.1 Nickel 82
4.3.2 SDSS 85
4.3.3 INCONEL 600 905. EXPERIMENTAL RESULTS AND ANALYSIS
5.1 INTRODUCTION 95
5.2 SELECTION OF ORTHOGONAL ARRAY 95
5.3 EXPERIMENTAL RESULTS 101
5.3.1 Experimental Results - Nickel 103
5.3.2 Experimental Results - SDSS 106
5.3.3 Experimental Results - Inconel 600 109
5.4 ANALYSIS AND DISCUSSION OF RESULTS 112
5.5 CONFIRMATION TEST 112
5.5.1 Results and Discussion for Nickel 113
5.5.2 Results and Discussion for SDSS 121
5.5.3 Results and Discussion for Inconel 600 129
5.6 DIMENSIONAL DEVIATION 138
5.6.1 Dimensional Deviation - Nickel 138
5.6.2 Dimensional Deviation - SDSS 139
5.6.3 Dimensional Deviation - Inconel 600 141
6. SUMMARY AND CONCLUSIONS
6.1 SUMMARY 143
6.2 CONCLUSIONS 144
6.2.1 Conclusion on ECMM of Nickel 145
6.2.2 Conclusion on ECMM of SDSS 146
6.2.3 Conclusion on ECMM of Inconel 600 147
6.3 SUGGESTIONS FOR FUTURE WORK 148
APPENDICES 149-150
REFERENCES 151-155
LIST OF PUBLICATIONS 156
CURRICULUM VITAE 157
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LIST OF TABLES
Number Title Page
1.1 Dissolution valence for different metals 10
1.2 Comparison between ECM and ECMM 10
3.1 Degree of Freedom 50
3.2 Calculated ON time and OFF time - Nickel 59
3.3 Calculated ON time and OFF time - SDSS and Inconel 600 60
3.4 Average Current - Nickel 60
3.5 Average Current - SDSS and Inconel 600 61
3.6 Average Voltage - Nickel 61
3.7 Average Voltage - SDSS and Inconel 600 61
4.1 General Properties of Nickel 83
4.2 Physical Properties of Nickel 83
4.3 Atomic Properties of Nickel 83
4.4 Miscellaneous Properties of Nickel 84
4.5 Specifications of SDSS 89
4.6 Compositions of Inconel Alloy 90
4.7 Physical Properties of Inconel 600 91
5.1 Process Parameters and their Levels - Nickel 95
5.2 Process Parameters and their Levels - SDSS 96
5.3 Process Parameters and their Levels - Inconel 600 96
5.4 Experiment Layout using L18 Orthogonal Array 97
5.5 Orthogonal Array of Process Parameters - Nickel 98
5.6 Orthogonal Array of Process Parameters for SDSS 99
5.7 Orthogonal Array of Process Parameters for Inconel 600 100
5.8 Experimental Results for MRR - Nickel 102
5.9 Experimental Results - Nickel 103
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5.10 Experimental Results - SDSS 106
5.11 Experimental Results - Inconel 600 109
5.12 Response Table for Means - Nickel 113
5.13 Response Table for S/N Ratio - Nickel 115
5.14 Results of ANOVA - Nickel 115
5.15 Results of Confirmation Test - Nickel 119
5.16 Response Table for Means - SDSS 122
5.17 Response Table for S/N Ratio - SDSS 123
5.18 Results of ANOVA - SDSS 124
5.19 Results of Confirmation Test - SDSS 127
5.20 Experimental Results - Inconel 600 130
5.21 Response Table for S/N Ratio - Inconel 600 131
5.22 Results of ANOVA - Inconel 600 132
5.23 Results of Confirmation Test - Inconel 600 135
A 1.1 Experimental Results for MRR - SDSS 149
A 1.2 Experimental Results for MRR - Inconel 600 150
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LIST OF FIGURES
Number Title Page
1.1 Physical Model of ECMM 8
1.2 Mechanism of ECMM 9
1.3 Outline of Research Work 15
3.1 Taguchi Loss Function 44
3.2 The Taguchi Loss-Function for HB and LB Characteristics 47
3.3 Taguchi Experimental Design and Analysis Flow Diagram 51
3.4 Structure of Genetic Algorithm 65
4.1 Schematic Diagram of Experimental Setup 71
4.2 Work Holding Platform, Tool holding arrangement 74
4.3 Control System 76
4.4 Electrolyte Filter 77
4.5 Electro Chemical Reactions 78
4.6 Pulse Rectifier 79
4.7 Complete Experimental Setup 81
5.1 Image of micro hole machined in 8th experiment 104
5.2 Image of micro hole machined in 12th experiment 105
5.3 Image of micro hole machined in 11th experiment 105
5.4 Image of micro hole machined in 17th experiment 107
5.5 Image of micro hole machined in 16th experiment 107
5.6 Image of micro hole machined in 7th experiment 108
5.7 Image of micro hole machined in 17th experiment 110
5.8 Image of micro hole machined in 6th experiment 110
5.9 Image of micro hole machined in 14th experiment 111
5.10 Main effect plot for means Nickel 114
5.11 Contribution of Process Parameters on MRR Nickel 116
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5.12 Normal Probability Plot (S/N Ratio) Nickel 117
5.13 Process Parameter Interaction Plot (MRR) Nickel 117
5.14 Image of micro hole machined for confirmation experiment 119
5.15 Comparison between GA and EV for Nickel 120
5.16 Screen Shot of GA output for Nickel 121
5.17 Main Effect Plot for Means SDSS 122
5.18 Contribution of Process Parameters on MRR SDSS 124
5.19 Normal Probability Plot (S/N Ratio) SDSS 125
5.20 Process Parameter Interaction Plot (MRR) SDSS 126
5.21 Comparison between GA and EV for SDSS 128
5.22 Screen Shot of GA output for SDSS 129
5.23 Main Effect Plot for Means Inconel 600 130
5.24 Contribution of Process Parameters on MRR Inconel 600 132
5.25 Normal Probability Plot (S/N Ratio) Inconel 600 133
5.26 Process Parameter Interaction Plot (MRR) Inconel 600 134
5.27 Image of micro hole machined for confirmation experiment 135
5.28 Comparison between GA and EV for Inconel 600 136
5.29 Screen Shot of GA output for Nickel 137
5.30 MRR Vs Dimensional Deviation - Nickel 138
5.31 MRR Vs Dimensional Deviation - SDSS 140
5.32 MRR Vs Dimensional Deviation - Inconel 600 141
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NOMENCLATURE
SYMBOL DESCRIPTION
y Actual value of characteristic
ANOVA Analysis of Variance
CI Confidence interval
CICEConfidence interval for the confirmation experiments
CIPOP Confidence interval for the population
k Constant depending on the magnitude of characteristic
DOF Degree of freedom
DD Dimensional deviation
DC Duty Cycle (%)
ECM Electro Chemical Machining
ECMM Electro Chemical Micro Machining
EC Electrolyte Concentration (mol/lit)
EL Expected loss
FAO F- test parameter
F Frequency (Hz)
GA Genetic Algorithm
HB Higher the better
IEG Inter Electrode Gap
L(y) Loss in monetary unit
LB Lower the better
C Machining Current (amps)
V Machining Voltage (volts)
MRR Material Removal Rate
µ Mean
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i Mean of S/N ratio at optimum level
MSD Mean Squared Deviation
q Nnumber of significant parameters
NB Nominal is Best
fA Number of degrees of freedom of parameter
R Number of Repetition
N Number of Trials
LN OA designation
Toff OFF Time
Ton ON Time
OA Orthogonal Array
S/N Signal to Noise Ratio
SE Sum of squares based on the error
Sm Sum of squares based on the mean
SA Sum of squares based on the parameter
ST Sum of squares based on the total variation
Ai Sum of the ith level parameter
m Target value for quality characteristic
fLN Total degrees of freedom of an OA
m Total mean of S/N ratio
Ttotal Total Time
VA Variance of parameter
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CHAPTER 1
INTRODUCTION
1.1 INTRODUCTION
The process of creativity proceeds by way of research, design and
development. The research work concerned with creation of new system, process,
and equipment for the benefit of mankind is Engineering. Research as the art of
executing a partial application of scientific knowledge by utilizing the established
facts, laws and principles of nature for the benefit of human rays. The new system
emerging from innovation may be constituted by mechanical, electro mechanical,
hydraulic, thermal, or other such elements. In these lines, this research tries to
innovate the process of Electro Chemical Micro Machining (ECMM) for Nickel
and its alloys.
Electrochemical machining (ECM) was developed during late 1950s
and early 1960s and used to machine difficult-to-cut materials in aerospace and
other heavy industries for shaping and finishing operations (Datta M 1998). It is
an anodic dissolution process based on the phenomenon of electrolysis, whose
laws were established by Michael Faraday. In ECM, electrolytes serve as
conductor of electricity. ECM offers a number of advantages over other
machining methods. The ECM technique now plays an important role in the
manufacturing of a variety of parts ranging from machining of large metallic
pieces of complicated shapes to opening of windows in silicon that are a few
microns in size. When ECM is performed at micro meter level (material removal
that ranges from 1-999 µm), it is known as ECMM (Bhattacharyya B 2007).
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In ECMM process, the work piece is connected to anode and the
micro tool is connected to cathode and they are placed inside the electrolyte with
a small gap between them. On the application of adequate electrical energy,
positive metal ions leave from the work piece and machining takes place.
Electrolyte circulation removes the machined particles from the electrode gap. To
continue the machining process, the electrode gap has to be maintained by
moving the tool at required rate.
ECMM is used for making smaller size components with high
precision. Advanced micro machining process consists of various ultra precision
activities to be performed on very small and thin work pieces (Bhattacharyya B
2004). The high precision components with micro sized holes, slots, and complex
surfaces are largely needed in mission critical applications like Nuclear power
plant, Aero space industry, Electronics industry, and Bio-medical field. ECMM
is a very promising technology since it offers several advantages like a) higher
machining rate, b) better precision and control, c) machining wide range of
materials, d) cost effective, and e) environmental friendly.
The ECMM process is capable of machining electrically conductive,
hard to cut materials without introducing any deformation on machined surface.
In this process, no tool wear is produced. Further, no residual stress is caused
because machining is not done with direct force on the work piece. Instead, ionic
dissolution is used to remove the material. Hence, there is no heat generation
involved while machining. The ECMM process can be effectively used for high
precision machining operations such as removal of micro burrs, making patterns
in foils and 3D micromachining. These qualities and capabilities of ECMM
process makes it useful in many industries where difficult-to-cut materials are
processed.
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1.1.1 Importance of ECMM in Present Day Scenario
The advancement in the field of metallurgy and the demand for high
strength materials in various industries resulted in development of high strength
alloys such as Nickel alloys. These alloys are extremely difficult to machine
using the traditional processes. Machining of these alloys with conventional tools
results in damage of work piece and the tool. The major difference between
conventional and non-conventional machining processes is that conventional
processes remove the material by physical means using a sharp tool. But the non-
conventional techniques remove material by utilizing chemical, thermal, or
electrical energy, or a combination of these energies.
Various machining non-conventional techniques are available to drill
micro holes in hard brittle materials. Few such non-conventional machining
techniques are Electro Chemical Drilling (ECD), Electron Beam Drilling (EBD),
Laser Beam Drilling (LBD) and, Electric Discharge Machining (EDM) (Kock M
2003). But these processes involve either heat or deformation of work piece.
The electro thermal processes such as ECD, EBD, LBD, and EDM
generally do not completely satisfy the quality requirements with respect to the
geometrical and metallurgical characteristics.
Machining materials on micrometric and submicrometric scales is
considered to be one of the key technologies of the future since, the present day
requirements of manufacturing micro and submicro level components for
miniaturized devices in biology, and medical field, chemical micro-reactors etc.
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An ECMM system setup was developed for carrying out in-depth
research for achieving satisfactory control of ECMM process parameters
(Bhattacharyya B 2003). The effect of machining voltage, pulse duration, and
pulse frequency on machining performance was studied to further improve
ECMM Process (Lee E.S 2007). The influence of ECMM process parameters on
radial overcut was investigated with RSM based approach (Munda J 2010).
The effective range of the process parameters for moderate Material
Removal Rate (MRR) with lesser overcut for 304 Stainless Steel was investigated
(Thanigaivelan R 2010). An ECMM experimental setup with constant electrode
gap control system was used to study the influence of tool tip shape and
machining gap on MRR (Thanigaivelan R 2010). The literature study reveals that
only a few authors have investigated the performance of ECMM process. Further
investigation is required for improvement of machining performance of ECMM
process for many newly developed difficult-to-cut materials.
The miniaturization of various ultra precision parts required for
producing high precision machines and equipments (Bhattacharyya B 2002)
necessitates the development of manufacturing processes capable of
performing micro manufacturing activities. Recent changes in society
demands us to introduce more and more micro-parts into various types of
industrial products. For example, the fuel injection nozzle for automobiles,
several regulations arising from environmental problems have forced
manufacturers to improve the design of compact, accurate, and efficient
nozzles. Inspection of internal organs of human body and surgery without
pain are universally desired. Miniaturization of medical tools is one of the
effective approaches to arrive at this target.
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Micromachining technology increasingly plays a decisive role in
the miniaturization of components ranging from biomedical applications to
chemical micro-reactors and sensors. The conventional machining methods
can also be used for micro machining of difficult to machine materials, but
the problems generally faced are high tool wear, rigidity problem of the tool,
heat generation at the tool-work piece interface, etc.
Non-traditional machining processes, especially ECMM is getting
importance due to its versatility and control over the process parameters. In
non-conventional machining, most of the processes are thermal oriented,
e.g. Electro discharge machining (EDM), laser beam machining (LBM),
Electron beam machining (EBM), etc. These processes may cause thermal
distortion of the machined surface. Chemical machining and Electrochemical
machining are thermal free processes, but chemical machining cannot be
controlled precisely for the micromachining domain (Bhattacharyya B 2003).
ECMM appears to be a very promising micromachining technology due to its
advantages that include high MRR, better precision and control, rapid
machining time, and environmentally acceptable (Datta M 1997). ECMM
also permits machining of chemically resistant materials like Titanium,
Copper alloys, Super alloys, and Stainless steel, which are widely used in
biomedical, electronic, and MEMS applications.
1.1.2 Electrochemical Machining
Electro chemical machining is a material removal process similar to
electro plating. In this process, the work piece to be machined is connected to
anode i.e. positive terminal and tool is connected to cathode i.e. negative terminal
of an electrolytic cell with an electrolyte made by a salt solution. The tool and
work piece is kept in such a way that there a gap measuring in microns is
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maintained between them. On the application of potential difference between the
electrodes and when adequate electrical energy is available between tool and
work piece, positive metal ions leave the work piece. Since electrons are removed
from the work piece, oxidation reaction occurs at the anode. The electrolyte
accepts these electrons resulting in reduction reaction. Ion displacement is the
phenomenon of material removal from work piece in electrochemical machining.
Hence the positive ions from the metal react with the negative ions in the
electrolyte forming hydroxides and thus the metal dissolute forming a precipitate.
The electrolyte is constantly flushed in the gap between tool and the
work piece to remove contaminated electrolyte. The non removal of electrolyte
with suspended precipitate from the machining zone leads to accumulation of
debris. This accumulation cause short circuit between the electrodes. The
electrolyte also carries away hydrogen bubbles created at the machining zone.
The tool electrode is advanced into work piece for the machining to be carried
out. A pumping system fitted with electrolyte filter is used to circulate the
electrolyte as the electrolyte carries away machining waste along with the heat
(Dayanand S. B. 2007).
Hard metals can be shaped using ECMM process and the rate of
machining does not depends on their hardness. The tool electrode used in the
process does not wear, and therefore soft metals can be used as tools to form
shapes on harder work pieces, unlike conventional machining method. The tool is
guided towards the work piece to maintain a constant inter electrode gap (IEG)
between them. If the tool feed is not in sync with the machining, either too high
movement of electrode or insufficient movement of electrode will occur. This
will result in either contact between anode and cathode or too large IEG. In both
cases, the premature termination of machining process will occur. Hence a
constant IEG is to be maintained to achieve desired machining.
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1.1.3 Basic Principles of ECMM Process
There are many industrial processes which works based on the
principal of Faraday’s law of electrolysis. ECM is one of them (Mukherjee S.K
2005). It is considered as the reverse of electroplating process. The major
difference between the ECM process and other electrolytic processes is that, in
ECM there it not merely the removal of material from work piece but also the
change of shape and size of work piece in a controlled manner.
Ions and electrons crossing phase boundaries (the interface
between two or more separate phases, such as liquid-solid) would result in
electron transfer and the reactions carried out at both anode and cathode.
The potential difference is fundamental in understanding the energy
distribution during the ECM process.
In ECMM, to enhance the MRR pulsed current and pulsed voltage
are applied. The use of pulsed voltage and pulsed current enhances the
activity of the cathode by reducing the cathode ionization while improving the
energy usage of the ECMM process effectively (De Silva A.K.M 1998).
The physical model of ECMM is shown in the figure 1.1.
1.1.4 Mechanism of Material Removal in ECMM
Atom-by-atom removal of metal by anodic dissolution is the basic
principle underlying electrochemical metal removal process. The movement
of the ions is accompanied by electrons flow in the opposite direction to the
positive current in the electrolyte (Hocheng H 2003). The reactions are the
consequence of the applied potential difference, that is, voltage from the
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electric source. The phenomena can be embodied in Faraday’s laws of
electrolysis:
Figure 1.1: Physical Model of ECMM
1. The amount of any substance dissolved or deposited is directly
proportional to the amount of electricity which has conducted.
2. The amount of different substances deposited or dissolved by the
same quantity of electricity is proportional to their chemical
equivalent weights. Since the electrolyte serves as the conductor
of electric current, Ohm’s law could be applied to this type of
conductor.
The Faraday’s law indicates a relation between the numbers of
electrons removed from an atom and the mass of the atom that would
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dissolve into electrolyte. The simple expression of Faraday’s law can be
described as:
m =kIt (1.1)
where k is the electrochemical equivalent of the anode metal
(=A/(Z·F) in (g/C))
m is the mass of material dissolved
I is the electric current (A)
T is the machining time
A is the atomic weight of dissolving ions
Z is the valence of dissolved ion immediately after dissolution
F is the Faraday’s constant of 96,487 Coulombs (C)
Figure 1.2: Mechanism of ECMM
Ion dissolution valence is required in describing the dissolution
electrochemical process and calculating material removal according to
Faraday’s law. Table 1.1 shows the dissolution valences of some metals in
different metal electrolyte. Ions valence can be varied in different solutions and
process conditions (Masuzava T 2000). The figure 1.2 shows the mechanism of
ECMM. Table 1.2 presents the comparison between ECM and ECMM.
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Table 1.1: Dissolution valence for different metals
Metal Electrolyte Dissolution valenceNi NaCl 2Ni NaNO3 2*Fe NaCl 2 and 3 Fe NaNO3 2*Cr NaCl 6Cr NaNO3 6
*Accompanied by oxygen evolution
Table 1.2: Comparison between ECM and ECMM
Parameters ECM ECMM
Voltage 10-30 V < 10 V
Current density 20-200 A/cm2 75-100 A/cm2
Power supply Continuous / pulsed Pulsed
Frequency Hz-KHz range KHz-MHz range
Electrolyte flow 10-60 m/s < 3 m/s
Tool size Large to medium Micro
Inter electrode gap 100-600 um 5-50 um
Surface finish Good Excellent
1.1.5 Advantages of ECMM
ECMM offers several advantages over other competing technologies.
These advantages have made ECM the best choice for a variety of applications.
The product after processing is free of burrs
No-contact process principle
The process does not cause thermal or physical strain in the product
Unlike other machining techniques, no upper-layer deformation
3-Dimensional processing in single step (Kurita T 2006)
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High surface quality level attainable depending on material
High dimensional accuracy attainable (Lee S.J 2008)
No local rust formation on the surface of the workpiece
Gives more freedom of design a product
ECMM is a technique with high machining speed at low costs
Low running and tooling costs (Jinjin Zhou 2005)
The hardness, toughness and thermal resistance has no effect
MRR is high.
MRR is almost independent on the type of material.
Hard and tough alloys are machined at the same speed.
Electrolyte regeneration (micro filtration) has enabled the cleaning
of the electrolyte to a ppm level and can therefore be reused
indefinitely. The produced sludge can often be recycled, depending
on composition and hence environmentally acceptable
Hence, ECMM has emerged as a most widely used non-conventional
technology for machining micro/meso scale components.
1.1.6 Disadvantages of ECMM
In spite of various advantages, ECMM has following few disadvantages.
Each product and material require new research for optimization
Higher production numbers are essential, as a special electrode must
be developed for each product (Jerzy Kozak 2004). The optimum
number of products depends on complexity and material
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Design of electrode is complex and initially expensive, it can however
be used for difficult-to-machine materials cost effectively.
1.1.7 Applications of ECMM
Due to numbers of advantages of this process, this method is
successfully used for the machining of high strength alloys and chemically
resistant material like nickel, stainless steels etc. It also finds majority of its
applications in deburring and hole drilling (Mithu M.A.H. 2011). The fuel
injection nozzle for automobiles, several regulations arising from environmental
problems have forced manufacturers to improve the design to produce compact
nozzle with high accuracy.
Micromachining technology is extensively used to machine complex
shapes required in medical and electronics industries. Further ECMM is
a promising and cost effective solution for various industrial applications such as
micro slots, complex surface finishes, drilling large number of micro-holes in
a single work piece, etc.
The ECMM process is capable of machining tough and hard materials
without inducing any residual stress and tool wear (Joao Cirilo da Silva Neto
2006). In this process no physical force is directly applied to the material, the
finished work piece is free from any deformation. The ECMM method can be
effectively used for high precision machining operations as this method offers
extensive control over various process parameters which directly affect the
machining process, especially MRR and dimensional deviation.
13
Deburring the machined components manually is a time consuming
process. Electrochemical machining with its inherent advantages is a suitable
choice for deburring. A flat faced tool is used to remove the surface asperities on
the workpiece. As the tool is moved slowly towards the workpiece surface it
encounters the burrs first. Since the tool is relatively large in comparison to the
burrs and the current densities are high at the peaks of the burrs, they are
machined first. This is a fast and simple to control process, being used vastly in
precision manufacturing (Hai Ping Tsuia 2008).
Flushing the precipitate is crucial in ECMM drilling (Mohan Sen
2005). Otherwise the machined particles would pile up and form a short circuit
between tool and workpiece. In order to ensure the material removal only at the
tip of the machining tool, a protective coat with an insulating material is applied
on the sides of the tool and thus quality of drilling is maintained.
In ECMM, a constant gap, termed as Inter Electrode Gap (IEG) is
maintained between the tool and the workpiece as the tool progresses into the
workpiece. In contrast to other processes the electrolyte flow is all over the
workpiece. This process is mainly used to manufacture complex shaped micro
structure components in electronics and medical industries (Rajurkar K.P 2006).
It is also widely used in shaping the high precision components for aerospace
industry.
1.2 OBJECTIVES OF PRESENT INVESTIGATION
Nickel-based alloys are the most sought material for manufacturing
machines and components which needs to withstand high temperature, high
pressure and aggressive chemical environment. Nickel alloys find wide
14
application in a) gas turbines, b) high temperature fasteners, c) chemical
processing and pressure vessels, and d) reactors of nuclear plants, etc.
Micromachining technology enables machining of miniature and
complex to machine shapes and surfaces, drilling of micro-holes, and other
special requirements in electronic industries (Zhang Z 2007). These things are
also performed by using conventional machining techniques, but the problems
generally faced are a) tool wear, b) rigidity problem of the tool, and c) heat
generation at the tool–work piece interface (Jerzy Kozak 2004). In general, it is
very difficult to produce complex shapes without compromising the quality by
using conventional techniques due to its own limitations.
The micro machining of Nickel alloys can be difficult using traditional
machining techniques as they easily harden during machining. High pressure is
developed between the tool and the work piece during machining. Such high
pressure produces a stressed layer of deformed metal on the surface of the work
piece. This deformation causes a hardening effect on the surface of the work
piece that slows down further machining. Due to this reason, age-hardened
Nickel-base alloys, such as alloy Inconel 600, are machined using an aggressive
but slow cut with a hard tool that minimizes the number of passes required. The
application of ECMM for Nickel alloy is more suitable but it cannot be applied
effectively unless the process parameters are optimized.
The levels of process parameters for experimentation is generally
selected either based on the experience or from the propriety machining
handbook. In most cases, selected parameters are conservative and far from
optimum. Extensive and laborious experimentation involving huge time and cost
is required to select the optimum parameters without optimization technique.
Hence, it is essential to use suitable optimization technique to study the complete
15
range of level of process parameter with least number of experiments. The
analysis of variance (ANOVA) is used to verify statistical significance of the
process parameters on MRR. Finally, a non-traditional optimization technique
called Genetic Algorithm (GA) (Jain N.K 2007) is used to derive the optimized
values of process parameters for the maximum MRR.
The objective of this research is to study the influence of ECMM
process parameters such as electrolyte concentration, machining voltage,
machining current, duty cycle and frequency on MRR of Nickel, SDSS and
Inconel 600. Further, design of experiments employing Taguchi’s Technique,
ANOVA and Genetic Algorithms are used to optimize the process parameters.
1.3 OUTLINE OF THE RESEARCH WORK
Figure 1.3 : Outline of Research Work
Experimental investigations of ECMM on nickel and its alloys
Machining of Nickel
Establishing relationship of MRR,
DD and Process Parameters
Optimization of Process Parameters
Comparative Analysis
Conclusion
Machining of SDSS
Establishing relationship of MRR,
DD and Process Parameters
Optimization of Process Parameters
Machining of Inconel 600
Establishing relationship of MRR,
DD and Process Parameters
Optimization of Process Parameters
16
1.4 STATEMENT OF THE PROBLEM
An elaborate literature survey has been done and inferred that only
a very few authors have investigated the performance of the ECMM process and
its parameters. Further investigation is required for machining performance
improvement for many newly developed difficult-to-cut materials.
Experiments are to be conducted to understand the influence of the
various ECMM parameters on MRR. Statistical and optimization techniques play
an important role in modeling the machining parameters and performing the
optimization of machining parameters for achieving the selected objectives.
Further research is needed to optimize the ECMM process parameters for the
most widely used materials like Nickel, Super Duplex Stainless Steel and Inconel
600 is need of the hour since use of these materials has grown in many industries.
This research mainly concentrates in finding optimum ECMM process
parameters to achieve maximum MRR in Nickel, Super Duplex Stainless Steel
and Inconel 600 alloys. The parameters subjected to the study are 1) Electrolyte
Concentration 2) Machining Voltage, 3) Machining Current, 4) Duty Cycle, and
5) Frequency.
In this study, Taguchi methodology is used to conduct complete
analysis of influence of process parameters with least number of experiments.
The analysis of variance (ANOVA) is used to verify statistical significance of the
process parameters and its optimal combination for maximum MRR. A non-
traditional optimization technique called Genetic Algorithm (GA) is used to
optimize the process parameters for maximum MRR. Necessary confirmation
experiments are conducted and the results are verified with the GA results.
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CHAPTER 2
LITERATURE SURVEY
2.1 REVIEW OF LITERATURE
ECMM is an important machining process in many manufacturing
industries, viz. aero space, bio-medical, electrical, and electronics, auto mobile,
thermal power plants, nuclear power plants, etc., where hard to cut materials are
used. Several researchers have attempted to improve the performance
characteristics of ECMM process by studying the effect of process parameters on
the machining process. But the full potential utilization of ECMM process is yet
to be achieved. This is due to complex and stochastic nature and number of
variables involved.
2.2 OVERVIEW ON ECMM
The literature survey made for this research work revealed that the
researches conducted on ECMM are related to recent trends in ECMM and
effects of process parameters on MRR. It is also inferred that more research
involving number of process parameters are to be done in this area.
The research titled "Electrochemical machining: new possibilities
for micromachining" highlights various design and development activities of
an ECMM system set up (Bhattacharyya B. 2002). A successful attempt has
been made to develop an ECMM setup for carrying out in-depth independent
18
research for achieving satisfactory control of ECM process parameters to meet
the micromachining requirements. The developed ECMM setup mainly
consists of various sub-components and systems, e.g., mechanical machining
unit, micro tooling system, electrical power, and controlling system and
controlled electrolyte flow system, etc. All these system components are
integrated in such a way that the developed ECMM system setup will be
capable of performing fundamental research in the area of ECMM fulfilling
the requirements of micromachining objectives.
The recent developments and future trends of EMM were
highlighted in the research titled “Advancement in electrochemical micro
machining” (Bhattacharyya B 2004). It suggests that micro-ECM (ECMM)
method can be effectively used for high precision machining operations such
as removal of burrs, making patterns in foils, and 3D micro-machining. The
research suggests that for utilizing ECMM in micro fabrication, improvement
in micro tool design and development, monitoring and control of the inter
electrode gap (IEG), control of material removal and accuracy, power supply,
and elimination of micro-sparks generation in IEG, and selection of electrolyte
is required.
The study titled “Process monitoring of electrochemical
micromachining” shows the importance of inter-electrode gap in ECMM set
up (De Silva A.K.M 1998). Electrochemical micro-machining utilizes very
small inter electrode gaps in order to obtain the accuracies. The narrow gaps
make the control of process much more complex than normal ECM. In order
to formulate spark prevention and gap control strategies, this paper investigate
the discharge mechanism in narrow electrolytic gap.
19
The paper titled “Electrochemical micro-machining : new
possibilities for micro-manufacturing”, highlights the design and development
of ECMM set up which includes various component like mechanical
machining components, electrical system and an electrolyte flow system etc.
(Bhattacharyya B 2001). A microprocessor controlled IEG controlling system
has been developed for this setup. The set up has versatile system components
such as controlled tool feed, controlled electrolyte flow, and pulse power
supply. The developed ECMM set up opens many challenging possibilities for
effective utilizations of the electrochemical material removal mechanism.
The paper titled “A review of electrochemical macro to micro-hole
drilling processes”, discusses about the Electrochemical machining processes
for drilling macro and micro holes with exceptionally smooth surface and
reasonably acceptable taper in numerous industrial applications particularly in
aerospace, electronic, computer and micro-mechanics industries (Mohan Sen
2005). Also this paper highlights about the hole-drilling processes like jet
electrochemical drilling have found acceptance in producing large number of
quality holes in difficult-to-machine materials. This paper highlights the recent
developments, new trends, and the effect of key factors influencing the quality of
the holes produced by these processes
The research titled “Selected problems of microelectrochemical
machining”, included the study of electrochemical copying of slots, mini
holes, grooves, and insulating groove features (Jerzy Kozac 2004). The
limiting conditions of ECMM are considered from the point of view of
copying and micro shaping using non profiled tool electrodes. For improving
micro machining capabilities of ECM processes, the application of ultra short
pulse current and ultra small gap size is recommended which is main point of
discussion in this paper.
20
A rare application of electrochemical micromachining was
discussed in the paper titled “Electro-chemical micro drilling using ultra short
pulses” (Se Hyun Ahn 2004). In this work, ultra short pulses with tens of
nanoseconds duration are used to localize dissolution area. The effect of
voltage, pulse duration, and pulse frequency on localization distance were
studied. High quality micro holes with 8 micron diameter were drilled on 304
stainless steel foil having 20 micron thickness.
An ECµM system with a machining gap control system was
discussed in the research titled “A study of three-dimensional shape machining
with an ECµM System” (Kurita T 2006). The applications of ultra short pulse
current and ultra small gap size improves micromachining capabilities of ECM
process. The utilization of edge cut electrode is advantageous to machine
micro holes with high aspect ratio.
In the work titled “Localized electrochemical Micromachining with
gap control”, an approach to electrochemical micromachining was presented
in which side-insulated electrode, micro gap control between the cathode and
anode, and the pulsed current are synthetically utilized (Li Yong 2003). An
experimental set-up for electrochemical micromachining is constructed, which
has machining process detection and gap control functions; also a pulsed
power supply and a control computer are involved in. Microelectrodes are
manufactured by micro electro-discharge machining (EDM) and side-insulated
by chemical vapor deposition (CVD). A micro gap control strategy is
proposed based on the fundamental experimental behavior of electrochemical
machining current with the gap variance. Machining experiments on micro
hole drilling, scanning machining layer-by-layer, and micro electrochemical
deposition are carried out. Preliminary experimental results show the
21
feasibility of electrochemical micromachining and its potential capability for
better machining accuracy and smaller machining size.
An experimental set up for micro electro chemical machining was
developed with a machining gap control system for the research titled
“Theoretical and Experimental investigation on electrochemical micro
machining” (Zhang Z 2007). Experiments were conducted to identify the
optimum parameters for machining voltage, pulse on time, piezo oscillation
amplitude and electrolyte concentration. Based on the optimum parameters,
three dimensional shapes with sub millimeter range was successfully
machined.
The research titled “Electrochemical Micromachining of Stainless
Steel by Ultra short Voltage Pulses” discusses the application of ultra short
voltage pulses to a tiny tool electrode under suitable electrochemical
conditions enables precise three-dimensional machining of stainless steel
(Laurent Cagnon 2003). In order to reach sub micrometer precision and high
processing speed, the formation of a passive layer on the work piece surface
during the machining process has to be prevented by proper choice of the
electrolyte. Mixtures of concentrated hydrofluoric and hydrochloric acid are
well suited in this respect and allow the automated machining of complicated
three-dimensional microelements. The dependence of the machining precision
on pulse duration and pulse amplitude was investigated in detail.
A comprehensive mathematical model for analyzing the effects of
various process parameters on the micro-spark and stray current affected zone
was studied in the research titled “Control of micro spark and stray current
effect during EMM process” (Munda J. 2007). Micro-spark and stray current
22
affected zone has been reduced as low as 0.0001 mm under proper controlled
machining parametric combination.
The paper titled “Influence of tool vibration on machining
performance in electrochemical micro-machining of copper” highlights the
influence of various electrochemical micromachining parameters like
machining voltage, electrolyte concentration, pulse period and frequency on
material removal rate, accuracy and surface finish in microscopic domain
(Bhattacharyya B 2007). According to their experimental study, the most
effective values for micromachining parameters have been considered as 3 V
machining voltage, 55 Hz frequency, and 20 g/l electrolyte concentration that
can enhance the accuracy with highest possible amount of material removal.
The research titled “Experimental investigation on the influence of
electrochemical machining parameters on machining rate and accuracy in
micromachining domain”, has made an attempt to develop an EMM
experimental set-up for carrying out in-depth research for achieving a
satisfactory control of the ECMM process parameters to meet the
micromachining requirements (Bhattacharyya B 2003). Keeping in view these
requirements, sets of experiments have been conducted to investigate the
influence of some of the predominant electrochemical process parameters such
as machining voltage, electrolyte concentration, pulse on time, and frequency
of pulsed power supply on the material removal rate (MRR) and accuracy to
achieve the effective utilization of electrochemical machining system for
micromachining. A machining voltage range of 6 to 10 V gives an appreciable
amount of MRR at moderate accuracy
23
The paper titled “Experimental research on the localized
electrochemical micro-machining” proposes a method of electrochemical
micromachining of micro hole or dimple array, in which a patterned insulation
plate coated with metal film as cathode is closely attached to work piece plate
(Zhang Z 2008). When voltage is applied across the work piece and cathode
film over which the electrolyte flows at high speed, hole or dimple array will
be produced. The proposed technology offers unique advantages such as short
lead time and low cost. The effect of process parameters on the microstructure
shape was demonstrated numerically and experimentally. Arrays of holes or
dimples of several hundred micrometers diameter have been produced.
The work titled “Experimental investigation into electrochemical
micromachining (EMM) process”, with a suitable ECMM setup mainly
consists of mechanical machining unit, micro-tooling system, electrical power,
and controlling system and controlled electrolyte flow system to control
electrochemical machining (ECM) (Bhattacharyya B 2003). Investigation
indicates most effective zone of predominant process parameters such as
machining voltage and electrolyte concentration, which give the appreciable
amount of material removal rate (MRR) with less overcut. The experimental
results and analysis on ECMM will open up more application possibilities for
ECMM.
The research work titled “Experimental study on the influence of
tool electrode tip shape on Electrochemical Micromachining of 304 stainless
steel”, used an experimental set-up with constant gap control system
(Thanigaivelan R 2010). The experimental study on the influence of tool tip
shape on machining rate and machining gap for 304 stainless steel has been
presented. The tool electrode tips of different shapes like flat ended, conical
ended, round ended and wedge shape are used for this study. The experimental
24
results show that the round ended tip improves the machining rate and conical
shape tip reduces the machining gap when compared with the other shapes.
In the paper titled “Experimental study of overcut in
electrochemical micromachining of 304 stainless steel” an attempt was made
to determine optimum machining condition of ECMM for 304 SS
(Thanigaivelan R 2010). From the experimental results, it is evident that the
most effective range of pulse on-time and electrolyte concentration can be
considered as 25-30 ms and 0.23-0.29 mole/l, which gives lower overcut.
Overcut increases with increase in pulse on-time and machining voltage. After
the preliminary ECMM experiments the Taguchi experimental design has
been applied to determine the optimal combinations of the machining
parameters levels. According to the Taguchi’s quality design concepts, a L16
orthogonal array was used. The optimal combinations of machining
parameters levels for lesser overcut are machining voltage at 12V, pulse on-
time at 25ms,machining current at 0.8 A and then electrolyte concentration of
.29 mole/l.
An experimental study titled “Study of dominant variables in
Electrochemical Micromachining” was carried out to determine the effects of
dominant variables like pulse on time, electrolyte concentration and voltage on
machining speed and overcut of stainless steel (Thanigaivelan R 2010). With
the experimental results, it is inferred that machining speed reaches maximum
at a pulse on time of 30 ms. The most effective range of pulse on time and
electrolyte concentration can be considered as 25-30 ms and 0.23-0.29 mol/lit
which gives moderate machining speed and lower overcut.
25
The paper titled “Investigation into the influence of Electrochemical
Micromachining (EMM) parameters on Radial Overcut through RSM-based
approach” highlights the features of the development of mathematical model for
correlating the interactive and higher-order influences of various machining
parameters (Munda J 2010). This paper also highlights mathematical models for
analyzing the effects of various process parameters on the machining rate and
overcut phenomena. These parameters can be used in order to achieve
maximization of the metal removal rate and the minimum overcut effects for
optimal accuracy of shape features.
The work titled “Hole quality and inter electrode gap dynamics during
pulse current electrochemical deep hole drilling” presents an experimental
investigation of pulse-current shaped-tube electrochemical deep hole drilling
(PC-STED) of nickel-based superalloy (Dayanand S. B. 2007). Influence of
five process variables (voltage, tool feed rate, pulse on-time, duty cycle, and
bare tip length of tool) on the responses, namely, depth-averaged radial
overcut (DAROC), mass metal removal rate (MRRg), and linear metal
removal rate (MRRl) have been discussed. Mathematical models have been
developed to express the effects of these process variables. The proposed
model permits quantitative evaluation of the hole quality and process
performance simultaneously. The results have been confirmed for the profile
of the drilled hole and MRRl obtained experimentally. In all the experiments,
through holes of 26 mm depth with diameters ranging from 2.205 mm to 3.279
mm were drilled. The results have been explained by the inter electrode gap
dynamics prevailing during pulse electrochemical deep hole drilling. Optimum
parameters determined from these experiments can be used to efficiently drill
high-quality deep holes.
26
The paper titled “Effect of over voltage on Material Removal Rate
during Electrochemical machining” gives a report about the MRR in
electrochemical machining by using over voltage and conductivity of the
electrolyte solution (Mukherjee S.K 2005). It is observed that over voltage plays
an important role equilibrium gap and tool feed rate. MRR decreases due to
increase in over voltage and decrease in current efficiency, which is directly
related to the conductivity of the electrolyte solution.
The study titled “State of the art of micromachining” discusses about
the miniaturization in manufacturing various types of industrial products
(Masuzava T 2000). Micromachining is the foundation of the technology to
realize such miniaturized products. In this paper, the author summarizes the
basic concepts and applications of major methods of micromachining. The
basic characteristics of each group of methods are discussed based on different
machining phenomena. Promising methods are introduced in detail hinting at
suitable areas of application. Finally, the present state of these technologies is
shown with examples of experimental and practical applications.
The work titled “Experimental investigation of microholes in
electrochemical machining using pulse current” investigates the influences of
some of the predominant electrochemical process parameters such as pulse
frequency, feed rate of tool, machining voltage, and electrolyte concentration
on the machining accuracy of micro-holes (Zhiyong Li 2008). According to
the investigation, the most effective zone of pulse on time and electrolyte
concentration can be considered as 15-50 µs and 30-50 g/l, respectively, which
can gives a desirable machining accuracy for micro-holes. A machining
voltage range of 6-10 V can be commended to obtain high machining
accuracy. From the micrographs of the machined micro-holes, it may be
observed that a lower value of electrolyte concentration with moderate
27
machining voltage and moderate value of pulse on time will produce more
accurate shape of micro-holes.
In the paper titled “Electrochemical micromachining with ultrashort
voltage pulses – a versatile method with lithographical precision” discusses
about the application of ultrashort voltage pulses electrochemical reactions
(Kock M 2003). As an example, electrochemical machining parameters for the
micromachining of Ni are derived from conventional electrochemical cyclic
voltammetry. Depending on the average potentials of tool and workpiece,
overall corrosion of the workpiece and the location of the counter reaction of
workpiece dissolution can be controlled. The pulse duration provides a direct
control for setting the machining accuracy. Machining precisions below 100
nm were achieved by the application of 500 ps voltage pulses.
The research paper titled “Investigation into electrochemical
micromachining (EMM) through response surface methodology based
approach”, attempts to establish a comprehensive mathematical model for
correlating the interactive and higher-order influences of various machining
parameters through response surface methodology (RSM) (Munda J 2008).
Validity and correctness of the developed mathematical models have also been
tested through analysis of variance. Optimal combination of these predominant
micromachining process parameters is obtained from these mathematical
models for higher machining rate with accuracy. Considering MRR and ROC
simultaneously optimum values of predominant process parameters have been
obtained as; pulse on/off ratio, 1.0, machining voltage, 3 V, electrolyte
concentration, 15 g/l, voltage frequency of 42.118 Hz and tool vibration
frequency as 300 Hz.
28
The research paper titled “Improving Machining Accuracy of the EMM
Process through Multi-Physics Analysis” studies the parametric effects of the
EMM process by both numerical simulation and experimental tests (Shuo Jen
Lee 2007). The numerical simulation was performed using commercial
software, FEMLAB, to establish a multi-physics model which consists of
electrical field, convection, and diffusion phenomena to simulate the
parametric effects of pulse rate, pulse duty, electrode gap and inflow velocity.
From the simulated results, the relationship between parameters, and the
distribution of metal removal could be established. Proper process variables
were also chosen to conduct the EMM experiments. After the experiments, the
profile of the processed rectangular slot was measured by a Keyence digital
microscope. Comparing profile of the processed rectangular slot with the
profile of the cathode, the machining accuracy of EMM process could be
determined. It could also verify the goodness of the multi-physics model for
predicting machining accuracy. From this study, the effects of parameters such
as pulse rate, pulse duty, electrode gap, and inflow velocity are better
understood. The simulation model could be employed as a predictive tool to
provide optimal parameters for better machining accuracy and process
stability of the EMM process.
The investigation titled “Electrochemical micromachining,
polishing and surface structuring of metals: fundamental aspects and new
developments” discusses about the application of Electrochemical
micromachining (EMM) as a versatile process for machining and surface
structuring of metallic materials for biomedical and micro systems (Landolt D
2003). From a fundamental point of view EMM presents many similarities
with electrochemical machining (ECM) and electro polishing (EP) provided
one takes into account the scale dependence of phenomena. In the present
paper the role of mass transport, current distribution, and passive films for
shape control and surface smoothing is discussed and illustrated with
29
examples. The usefulness of numerical simulation using simplified models is
stressed. New developments in EMM of titanium are presented, including
oxide film laser lithography permitting EMM on non-planar surfaces without
photo resist and the fabrication of two-level and multi-level structures. Scale
resolved electro chemical surface structuring of titanium leads to well-defined
topographies on the micrometer and nanometer scales, which are of interest
for biomedical applications.
The technical paper titled “Improvement of Electrochemical
Microdrilling Accuracy Using Helical Tool” presents a microhelical tool as
a novel solution in electrochemical microdrilling process to improve the
machining accuracy and ability (Hai-Ping Tsuia 2008). Fluent CFD is adopted
to analyze the flow field status in process. The inlet and outlet diameters of the
microholes are 425 µm and 362 µm, respectively; the values are obtained
using the conventional microsolid cylindrical tool. When the rotation speed of
the helical tool is 20,000 rpm, and the pulse-off time is 90 µs, the inlet and
outlet diameter significantly decline to 335 µm and 299 µm. The experimental
results reveal that the accuracy of microhole shape can be significantly
improved using the microhelical tool in a simple and low-cost way.
The study titled “A study of the characteristics for electrochemical
micromachining with ultrashort voltage pulses” about the application of
voltage pulses between a tool electrode and a workpiece in an electrochemical
environment that allows the three-dimensional machining of conducting
materials with micrometer precision (Lee E.S 2007). In this paper, tool
electrodes (5 m in diameter, 1 mm in length) are developed by EMM and
microholes are manufactured using these tool electrodes. Microholes with
a size of below 50 m in diameter can be accurately achieved by using
ultrashort voltage pulses (1–5 s).
30
The article “A step towards the in-process monitoring for
electrochemical microdrilling”, presents a step towards the in-process
monitoring based on waveforms generated during electrochemical
micromachining (Mithu M.A.H 2011). An attempt has been made to correlate
between the waveforms generated during machining and experimental
outcomes such as material removal rate, machining time, and the dimensions
of the microholes fabricated on commercially available nickel plate with
prefabricated tungsten microtools. An electrical function generator is used as
a signal source and a digital storage oscilloscope is provided for observing the
nature of electrical pulses used and recording the waveforms generated during
machining. The waveforms are subgrouped depending on the parameters used
and analyzed to correlate the waveform shape and the machining outcomes.
The digital storage oscilloscope also facilitates for observing the short-circuit
condition which may occur during microdrilling. These results show that the
shape of the waveforms and their corresponding values are in good agreement
with the material removal rate, machining time, and on the dimension of
fabricated microholes. Therefore, the proposed monitoring technique can be
employed as a predictive tool in electrochemical micromachining.
The research titled “Research on pulse electrochemical finishing
using a moving cathode” discusses about the improvement of the surface
quality of parts with a finishing method Pulse Electrochemical Finishing
(PECF) using a Moving Cathode (Jinjin Zhou 2005). The results reveals that
machining with an inter electrode gap as small as possible could smoothen the
anode surface quickly; with an invariable gap size, the current density and the
machining time are two key parameters influencing the smoothening effect;
there is a critical current density above which a bright surface could be
obtained, and this process could finish a large surface area over the critical
current with a low power supply, which is helpful to get a lustrous surface.
31
The result shows that the surface roughness value (Ra) reduces from 0.5 to
0.065 µm and a mirror-like surface is obtained.
The research work titled “A material removal analysis of
electrochemical machining using flat-end cathode” discusses about the process
to erode a hole of hundreds of micrometers on the metal surface (Hocheng H
2003). The paper also discusses the influence of experimental variables
including time of electrolysis, voltage, molar concentration of electrolyte and
electrode gap upon the amount of material removal and diameter of machined
hole. The results of experiments show the material removal increases with
increasing electrical voltage, molar concentration of electrolyte.
The research article titled “Optimization of electro-chemical
machining process parameters using genetic algorithms” discusses about the
optimum choice of the process parameters for the economic, efficient, and
effective utilization of these processes (Jain N.K 2007). Process parameters of
AMPs are generally selected either based on the experience, and expertise of
the operator or from the propriety machining handbooks. In most of the cases,
selected parameters are conservative and far from the optimum. This hinders
optimum utilization of the process capabilities. Selecting optimum values of
process parameters without optimization requires elaborate experimentation
which is costly, time consuming, and tedious. Process parameters optimization
of AMPs is essential for exploiting their potentials and capabilities to the
fullest extent economically. This paper describes optimization of process
parameters of four mechanical type AMPs namely ultrasonic machining
(USM), abrasive jet machining (AJM), water jet machining (WJM), and
abrasive-water jet machining (AWJM) processes using genetic algorithms
giving the details of formulation of optimization models, solution
methodology used, and optimization results.
32
The study titled “Development of micro machining for air-lubricated
hydrodynamic bearings” uses a specially-built EMM / PECM (Pulse
Electrochemical Machining) cell, an electrode tool fitted with non-conducting
material, a electrolyte flow control system and a small & stable gap control unit
are developed to achieve accurate dimensions (Park J.W 2002). Two
electrolytes, aqueous sodium nitrate and aqueous sodium chloride are
investigated in this study. The former electrolyte with few pits on the surface of
workpiece has better machine-ability than the latter one with many pits on the
surface of workpiece. It is easier to control the machining depth precisely with
pulse electrical current than direct electrical current. This paper also presents an
identification method for the machining depth by in-process analysis of applied
electrical current and inter electrode gap size. The inter electrode gap
characteristics, including pulse electrical current, effective volumetric
electrochemical equivalent and electrolyte conductivity variations, are analyzed
using the model and experimental results.
The research work titled “Micro and nano machining by electro-
physical and chemical processes” discusses the issues related to the supporting
technologies such as standardization, metrology, and equipment design
(Rajurkar K.P 2006). Non-technological issues including environmental effects
and education are also discussed.
The investigation titled “Parametric optimization of electrochemical
machining of Al/15% SiCp composites using NSGA-II” discusses about optimal
parameters for improving cutting performance (Senthilkumar C 2011). MRR and
surface roughness are the most important output parameters, which decide the
cutting performance. There is no single optimal combination of cutting
parameters, as their influences on the metal removal rate and the surface
roughness are quite opposite. A multiple regression model was used to represent
33
relationship between input and output variables and a multi-objective
optimization method based on a non-dominated sorting genetic algorithm-II
(NSGA-II) was used to optimize ECM process. A non-dominated solution set
was obtained.
Application of an environmentally friendly electrolyte of citric acid
for micro electrochemical machining of stainless steel has been discussed in the
research paper entitled “Micro fabrication by electrochemical process in citric
acid electrolyte” (Shi Hyoung Ryu 2009). Micro holes of 60 µm in diameter with
depth of 50 µm and 90 µm in diameter with the depth of 100 µm are perforated
using citric acid electrolyte.
The experimental work titled “Intervening variables in
electrochemical machining” throws light on intervening variables in
electrochemical machining (ECM) of SAE-XEV-F Valve-Steel (Joao Cirilo da
Silva Neto 2006). In this research, the material removal rate (MRR), roughness
and over-cut were studied. Four parameters were changed during the
experiments: feed rate, electrolyte, flow rate of the electrolyte and voltage. Forty-
eight experiments were carried out in the equipment developed. Two electrolytic
solutions were used: sodium chloride (NaCl) and sodium nitrate (NaNO3). The
results show that feed rate was the main parameter affecting the material removal
rate. The electrochemical machining with nitride sodium presented the best
results of surface roughness and over-cut.
The Micro electrochemical machining (ECM) using ultra short pulses
with sulfuric acid as electrolyte to machine 3D micro structures on stainless steel
was discussed in the paper titled “Micro Electrochemical Milling” (Kim B.H
2005). This paper shows how to prevent taper, by using a disk-type electrode. To
34
improve productivity, multiple electrodes were applied and multiple structures
were machined simultaneously. Since the wear of electrode is negligible in
ECM.
The article titled "Taguchi concepts and their applications in marine
and offshore safety studies” discusses about how the Taguchi concepts such as
‘quality loss function’, ‘signal-to-noise ratio’, ‘orthogonal arrays’, ‘degree of
freedom’ and ‘analysis of variance’ may be synthesized in maritime safety
engineering studies (How Sing Sii 2001). Brainstorming, an integral part of the
Taguchi philosophy, is also briefly discussed. Orthogonal arrays are used to
study many parameters simultaneously with a minimum of time and resources to
produce an overall picture for more detailed safety-based design and operational
decision-making. The S/N ratio is employed to measure quality; in this case, risk
level. The loss function is considered as an innovative means for deter-mining
the economic advantage of improving system safety or operational safety. Noise
factors are considered as any uncontrollable or uncontrolled variables or any
other undesired influences.
The article, “Optimizing feed force for turned parts through the
Taguchi technique” discusses about an optimal setting of turning process
parameters (cutting speed, feed rate and depth of cut) resulting in an optimal
value of the feed force when machining EN24 steel with TiC-coated tungsten
carbide inserts (Hari Singh 2006). The effects of the selected turning process
parameters on feed force and the subsequent optimal settings of the parameters
have been accomplished using Taguchi’s parameter design approach. The results
indicate that the selected process parameters significantly affect the selected
machining characteristics. The results are confirmed by further experiments.
35
The research paper titled “Micro fabrication by electrochemical metal
removal” discusses about the recent advancements in the electrochemical metal
removal processes for micro fabrication (Datta M 1998). After a brief description
of the process, several important parameters are identified that determine the
material-removal rate, shape control, surface finishing, and uniformity. The
influence of surface film properties, mass transport, and current distribution on
microfabrication performance are discussed. Several examples of
microelectronic component fabrication are presented. These examples
demonstrate the challenges and opportunities offered by electrochemical metal
removal in microfabrication.
The research paper titled "Electrochemical micromachining: An
environmentally friendly, high speed processing technology" discusses about the
wet chemical etching processes that are employed in the manufacturing of a
variety of microelectronic components (Datta M 1997). These processes use
etchants that generally contain aggressive and toxic chemicals, generate
hazardous waste and have limited resolution. Electrochemical metal removal is
an evolving alternate processing technique that involves controlled metal
shaping by an external current, thereby requiring less aggressive and nontoxic
electrolytes. The application of controlled electrochemical metal removal in the
fabrication of microstructures and micro components is referred to as
electrochemical micromachining (EMM). In this paper a recently developed
EMM process and tool for metal mask fabrication is discussed. EMM
performance is compared to that obtained by the conventional chemical etching
process. Obtained results demonstrate the opportunities offered by EMM
particularly as a high-speed, environmentally friendly processing technology.
The investigation work titled "Ultrasonic measurement of the inter
electrode gap in electrochemical machining" discusses about the dependency of
36
the inter electrode gap with time and process parameters and its usage to
determine process characteristics (Clifton D 2002). Defining process variables to
map out the required gap time function requires the use of time-consuming
iterative trials. In-line monitoring of the gap would enable process control and
tool to workpiece transfer characteristics to be achieved (for ideal conditions)
without the requirement to generate such parameter maps. This work explores
the use of ultrasound applied as a passive, non-intrusive, in-line gap
measurement system for ECM. The accuracy of this technique was confirmed
through correspondence between the generated gap-time and current time data
and theoretical models applicable to ideal conditions. The monitoring of the gap
size has also been shown to be effective when determining shape convergence
under ideal conditions, for the example case of a 2D sinusoidal profile.
2.3 OUTCOME OF LITERATURE REVIEW
The literature survey helped to successfully design, construct and
conduct the experimentation of this research work. Some of the major ideas
learnt from the literature survey are listed below.
1. The experimental setup is designed based on the various
requirements stated by above cited literature.
2. The specific studies of each process parameters made by
various authors on for MRR and Dimensional deviation are
helpful to understand the behaviour of each parameter.
3. Necessary ideas were obtained for making a suitable tool for the
current study.
4. Clear outline about Taguchi methodology, and various other
optimization techniques were learnt.
5. It is learnt that experimental investigation considering 5 most
37
influencing process parameters viz. Electrolyte Concentration,
Machining Voltage, Machining Current, Duty Cycle, and
Frequency on MRR is yet to be conducted.
6. It is understood that further research is to be conducted on
Nickel and its alloys for maximum MRR.
7. Further study is needed in the area of Dimensional deviation.
Hence, it is inferred that more in depth research involving
maximum number of process parameters are to be conducted to achieve
maximum MRR with less dimensional deviation for Nickel and its alloys.
38
CHAPTER 3
EXPERIMENTAL DESIGN
3.1 INTRODUCTION
The experimental design methodology is very important in
maintaining the reliability of entire research work. It is useful for conducting
experiments, recording the experimental results, and analysis of the results.
The methodology for the present research work has been designed effectively
to conduct least number of experiments to study the entire spectrum of levels
of ECMM process parameters for Maximum MRR on Nickel and its alloys.
The reduction in number of experiments greatly reduces the time and the cost.
In order to understand the effect of each ECMM process parameter
on MRR and to identify the significant parameters, experiments need to be
conducted by varying the level of each parameter one at a time. This proves
very cumbersome as the number of experiments to be conducted increases
exponentially with the number of process parameters. Hence, it’s highly
difficult to draw any conclusion with minimum number of experiments in this
approach. Hence, well planned set of experiments, in which all parameters of
interest are varied over a specified range, is a much better approach to obtain
systematic data.
Performing the experiments on the sub set of complete set of
experiments makes the experimentation process quick and cost effective. The
39
Taguchi method using the orthogonal array is highly effective in identifying
the sub set of experiments to be done to study the complete range and
combination of process parameters in minimal number of experiments. Hence,
Taguchi methodology is used to for selecting optimum levels of process
parameters and number of experiments required to ensure the quality of
experimentation. Employing this statistical method to design the experiments
and analyze the result sets enables the researcher to find the optimal levels of
process parameters qualitatively. Estimation of the experimental error greatly
helps to improve the quality of experiments conducted.
The result of analysis using ANOVA is highly effective in deriving
inferences regarding the optimum combination of process parameters for
maximum MRR. The use of GA helps to optimize the set of process
parameters under the process constraints. In this research work Taguchi and
ANOVA are utilized to design, experiment, analyze and confirm the
results. The GA is used to optimize the process parameters.
The different phases of experiments and the techniques used for the
experimentation are given in the following paragraphs.
Phase -I Development of experimental setup providing varying range of
input parameters in ECMM and measuring the various
responses.
Investigation of the working ranges and the levels of the
ECMM process parameters (pilot experiments) affecting the
selected quality characteristics, by using one factor at a time
approach.
40
Phase -II Investigation of the effects of ECMM process parameters on
Material Removal Rate (MRR).
Prediction of optimal combinations of ECMM process
parameters.
Experimental verification of optimized characteristics using
Taguchi’s parameter design approach.
Phase -III The Taguchi L18 orthogonal array has been used to plan the
experiments and to find the effects of process parameters on
MRR.
Phase -IV Development of ANOVA optimization model.
Determination of optimal combination of ECMM process
parameters for maximum MRR.
Phase -VDevelopment of optimization model for optimization using
Genetic Algorithms.
Determination of optimal sets of ECMM process parameters.
Verification of coherences between the experimental, ANOVA
and GA results.
41
3.2 TAGUCHI EXPERIMENTAL DESIGN AND ANALYSIS
3.2.1 Taguchi’s Philosophy
Taguchi’s comprehensive system of quality engineering is one of
the greatest engineering achievements of the 20 th century. His methods
mainly focus on the effective application of engineering strategies. It
includes both upstream and shop-floor quality engineering. Upstream methods
efficiently use small-scale experiments to reduce variability and remain cost-
effective, and robust designs for large-scale production. Shop-floor techniques
provide cost-based, real time methods for monitoring and maintaining
quality in production. The farther upstream a quality method is applied, the
greater leverages it produces on the improvement.
Taguchi’s philosophy is founded on the following three very simple
and fundamental concepts (Phillip J. Ross 1988):
Quality should be designed into the product and not inspected into it.
Quality is best achieved by minimizing the deviations from the target.
The product or process should be so designed that it is immune to
uncontrollable environmental variables.
The cost of quality should be measured as a function of deviation from
the standard and the losses should be measured system-wide.
Taguchi proposes an “off-line” strategy for quality improvement as
an alternative to an attempt to inspect quality into a product on the production
line. Taguchi observes that poor quality cannot be improved by the process of
inspection, screening and salvaging since no amount of inspection can put
42
quality back into the product. Taguchi recommends a three-stage process:
System Design, Parameter Design and Tolerance Design (Phillip J. Ross 1988).
In the present work Taguchi’s Parameter Design approach is used to study the
effect of process parameters on the various responses of the ECMM process.
3.2.2 Experimental Design Strategy
Taguchi recommends orthogonal array (OA) for layout of
experiments. These OA’s are generalized Graeco-Latin squares. To design an
experiment, suitable OA is to be selected. Then the parameters and interactions
of interest are to be assigned to appropriate columns. Use of linear graphs and
triangular tables suggested by Taguchi makes the assignment of parameters
simple. The array forces all experimenters to design almost identical
experiments (Roy R.K 1990).
In the Taguchi method the results of the experiments are analyzed to
achieve one or more of the following objectives (Phillip J. Ross 1988):
To establish the best or the optimum condition for a product or
process
To estimate the contribution of individual parameters and
interactions
To estimate the response under the optimum condition
The optimum condition is identified by studying the main effects of
each of the process parameters. The main effects indicate the general trends of
influence of each parameter. The knowledge about individual parameters and its
contributions is a key in deciding the nature of control to be established on
43
a production process. The analysis of variance (ANOVA) is a statistical
treatment most commonly applied to the results of the experiments in
determining the percent contribution of each parameter against a stated level
of confidence. Study of ANOVA table for a given analysis helps to determine
which of the parameters need control (Phillip J. Ross 1988).
Taguchi suggests (Roy R.K 1990) two different routes to carry
out the complete analysis. First, as a standard approach, the results of a single
run or the average of repetitive runs are processed through main effect and
ANOVA analysis. In the second approach, as per Taguchi’s recommendations,
multiple runs are used to analyze Signal-to-Noise ratio (S/N). The S/N ratio is
a concurrent quality metric linked to the loss function (Barker T.B 2005). By
maximizing the S/N ratio, the loss associated can be minimized. It is sufficient
to generate repetitions at each experimental condition of the controllable
parameters and analyze them using an appropriate S/N ratio.
In the present investigation, the S/N data analysis has been performed.
The effects of the selected ECMM process parameters for maximum MRR have
been investigated through the plots of the main effects. The optimum condition
for maximum MRR has been established through S/N data. No outer array has
been used and instead, experiments have been conducted two times at each
experimental condition.
Loss Function
The heart of Taguchi method is his definition of the nebulous and
elusive term quality as the characteristic that avoids loss to the society from the
time the product is shipped. Loss is measured in terms of monetary units and
44
is related to quantifiable product characteristic. Taguchi defines quality loss via
his loss function. He unites the financial loss with the functional specification
through a quadratic relationship that comes from a Taylor series expansion.
The quadratic function takes the form of a parabola. Taguchi defines the loss
function as a quantity proportional to the deviation from the nominal quality
characteristic. He has found the following quadratic form to be a workable
function (Roy R.K 1990):
L(y) = k (y-m)2 (3.1)
Where,
L = Loss in monetary units
m = value at which the characteristic should be set
y = actual value of the characteristic
k = constant depending on the magnitude of the characteristic and
the monetary unit involved
Figure 3.1: Taguchi Loss Function
The characteristics of the loss function are (Roy R.K 1990):
45
The farther the product’s characteristic varies from the target
value, the greater is the loss. The loss must be zero when the
quality characteristic of a product meets its target value.
The loss is a continuous function and not a sudden step as in
the case of traditional goal post approach. This characteristic of
the continuous loss function illustrates the point that merely
making a product within the specification limits does not
necessarily mean that product is of good quality.
The loss-function can also be applied to product characteristics
other than the situation where the nominal value is the best value (m). The loss-
function for a smaller is better type of product characteristic (LB) is shown in
figure 3.2. The loss function is identical to the “nominal is the best” type of
situation when m=0, which is the best value for “smaller the better”
characteristic (no negative value). The loss function for a “larger the better”
type of product characteristic (HB) is also shown in figure 3.2, where m = 0.
3.2.3 Signal to Noise Ratio
The loss-function discussed above is an effective figure of merit
for making engineering design decisions. However, to establish an appropriate
loss function with its k value to use as a figure of merit is not always cost
effective and easy. In order to address this issue, Taguchi created a transform
function for the loss-function which is named as signal-to-noise (S/N) ratio
(Barker T.B 2005).
46
The S/N ratio, as stated earlier, is a concurrent statistic.
A concurrent statistic is able to look at two characteristics of a distribution
and combine these characteristics into a single figure of merit. The S/N ratio
combines both the parameters (the mean level of the quality characteristic
and variance around this mean) into a single metric (Barker T.B 2005).
A high value of S/N ratio implies that signal is much higher than the
random effects of noise factors. Process operation consistent with highest
S/N ratio always yields optimum quality with minimum variation (Barker T.B
2005). The S/N ratio consolidates several repetitions (at least two data
points are required) into one value.
The mean squared deviation (MSD) is a statistical quantity that
reflects the deviation from the target value. The quality characteristics are
different for MSD expressions. The standard definition of MSD is used for the
“nominal is best” characteristic. The unstated target value is zero for “Lower the
better”. The inverse of each large value becomes a small value and the
unstated target value is zero for “Higher the better”. Hence, the smallest
magnitude of MSD is being sought for all the three expressions.
The equation for calculating S/N ratios for “Lower the better” (LB),
“Higher the better” (HB) and “Nominal is best” (NB) types of characteristics
are as follows (Phillip J. Ross 1988):
47
Characteristic : HB
Loss
(Mon
etar
yU
nit)
L=k(1/y2)
Y
Characteristic : LB
Loss
(Mon
etar
yU
nit)
L=ky2
Y
Figure 3.2: The Taguchi Loss-Function for HB and LB Characteristics
a. Higher the Better:
(S/N)HB = -10log(MSDHB) (3.2)
Where
48
b. Lower the Better:
(S/N)LB= – 10log(MSDLB) (3.3)
Where
c. Nominal the Best
(S/N)NB= – 10log(MSDNB) (3.4)
Where
R = Number of repetitions
Relation between S/N Ratio and Loss Function
Single sided quadratic loss function with minimum loss at the zero
value of the desired characteristic is shown in figure 3.2. As the value of
y increases, the loss grows. Since, loss is to be minimized the target in this
situation for y is zero. The basic loss function (Eq. 3.1) is:
L(y) = k (y–m)2
If m = 0
L(y) = k (y2)
The loss may be generalized by using k=1 and the expected value of
loss may be found by summing all the losses for a population and dividing
by the number of samples R taken from this population. This in turn gives the
following expression (Barker T.B 2005).
EL = Expected loss = ( y2/R) (3.5)
The above expression is a figure of demerit. The negative of
this demerit expression produces a positive quality function. Taguchi adds the
49
final touch to this transformed loss-function by taking the log (base 10) of
the negative expected loss and then he multiplies by 10 to put the metric into
the decibel terminology (Barker T.B 2005). The final expression for “Lower
the better” S/N ratio takes the form of Equation 3.3. The same thought pattern
follows in creation of other S/N ratios.
3.2.4 Selection of orthogonal array (OA)
In selecting an appropriate OA, the pre-requisites are
(Roy .R.K 1990):
Selection of process parameters and/or interactions to be evaluated
Selection of number of levels for the selected parameters
Several methods are suggested by Taguchi to determine the
required parameters for inclusion in an experiment (Phillip J. Ross 1988). They
are:
a) Brainstorming
b) Flow charting
c) Cause-Effect diagrams
The total Degrees of Freedom (DOF) of an experiment is a direct
function of total number of trials. If the number of levels of a parameter
increases, the DOF of the parameter also increases since DOF calculated as
the number of levels minus one. Thus, increasing the number of levels for
a parameter increases the total degrees of freedom in the experiment which in
turn increases the total number of trials. Thus, two levels for each parameter are
recommended to minimize the number of experiment (Phillip J. Ross 1988). If
curved or higher order polynomial relationship between the parameters under
study and the response is expected, at least three levels for each parameter
50
should be considered (Barker T.B 2005). The DOF selected for the process
parameters are given in table 3.1.
Table 3.1: Degree of Freedom
Parameters EC V C DC F Error Total DOF 2 2 2 2 2 7 17
The standard two level and three level arrays (Taguchi 1979) are:
Two level arrays : L4, L8, L12, L16, L32
Three level arrays : L9, L18, L27
The number as subscript in the array designation indicates the
number of trials in that array. The total degrees of freedom (DOF) available
in an orthogonal array are equal to the number of trials minus one
(Phillip J. Ross 1988):
fLN = N – 1 (3.6)
Where, fLN
= Total degrees of freedom of an OA
LN = OA designation
N = Number of trials
When a particular OA is selected for an experiment, the inequality
(fLN> Total degrees of freedom required for parameters and interactions ) must
be satisfied.
In accordance to the total degree of freedom (17), the L18 orthogonal
array has been selected for this experiment. The L18 orthogonal array has
8 columns and 18 rows and it can handle one two-level parameter and seven
three-level process parameters at most. Since, our experiment needs only five
three-level process parameters L18 orthogonal array is most suitable. The array
51
selected has 5 columns and 18 rows and hence 18 experiments are needed to
study the effects of all the five process parameters.
Figure 3.3: Taguchi Experimental Design and Analysis Flow Diagram
Selection of Orthogonal Array (OA)
Decide : Number of parameters Number of Levels Interactions of interest Degrees of freedom (DOF) required
Selection of Orthogonal Array (OA)
Assign parameters and interactions to columns of OA using linear graph and / or Triangular tables
Noise ?
Consider noise factors and use appropriate outer array
Decide the number of repetitions (at least two repetitions)
Run the experiment in random order Record the responses Determine the S/N ratio
Conduct ANOVA on data
Identify control parameters which affect mean of quality characteristics
Classify the factors and select proper levels
Predict the mean at the selected levels Determine confidence intervals Determine optimal range Conduct confirmation experiments Draw conclusions
52
3.2.5 Assignment of parameters and interaction to the OA
The OA’s have several columns available for assignment of
parameters and some columns subsequently can estimate the effect of interactions
of these parameters. Taguchi has provided two tools to aid in the assignment of
parameters and interactions to arrays (Phillip J. Ross 1988):
1. Linear graphs
2. Triangular tables
Each OA has a particular set of linear graphs and a triangular table
associated with it. The linear graphs indicate various columns to which
parameters may be assigned and the columns subsequently evaluate the
interaction of these parameters. The triangular tables contain all the possible
interactions between parameters (columns). Using the linear graphs and / or
the triangular table of the selected OA, the parameters and interactions are
assigned to the columns of the OA.
3.2.6 Experimentation and data collection
The experiment is performed against each trial condition. Each
experiment at a trial condition is repeated. Randomization has been carried to
reduce bias in the experiment. The data are recorded against each trial
condition and S/N ratios of the repeated data points are calculated and
recorded against each trial condition.
53
3.2.7 Data analysis
A number of methods have been suggested by Taguchi for
analyzing the data: observation method, ranking method, column effect
method, ANOVA, S/N ANOVA, plot of average response curves,
interaction graphs etc. (Phillip J. Ross 1988). However, in the present
investigation the following methods have been used:
Plot of mean response curves
ANOVA for data
S/N response graphs
The plot of average responses at each level of a parameter indicates
the trend. It is a pictorial representation of the effect of parameter on the
response. The change in the response characteristic with the change in levels of
a parameter can easily be visualized from these curves. Typically, ANOVA for
OA’s are conducted in the same manner as other structured experiments
(Phillip J. Ross 1988). The S/N ratio is treated as a response of the experiment,
which is a measure of the variation within a trial when noise factors are present.
A standard ANOVA can be conducted on S/N ratio which will identify the
significant parameters (mean and variation).
3.2.8 Parameter classification and selection of optimal levels
When the ANOVA on the data (identifies control parameters which
affect average) and S/N data (identifies control parameters which affect
variation) are completed, the control parameters may be put into four classes
(Phillip J. Ross 1988):
54
Class I : Parameters which affect both average and variation (Significant in ANOVA)
Class II : Parameters which affect variation only (Significant in S/N ANOVA only)
Class III : Parameters which affect average only (Significant in data ANOVA only)
Class IV : Parameters which affect nothing. (Not significant in both ANOVAs)
The parameters design strategy is to select the proper levels of class I
and class II parameters to reduce variation and class III parameters to adjust the
average to the target value. Class IV parameters may be set at the most
economical levels since nothing is affected.
3.2.9 Prediction of the mean
After determination of the optimum condition, the mean of the
response (µ) at the optimum condition is predicted. The mean is
estimated only from the significant parameters. The ANOVA identifies
the significant parameters. Suppose, parameters A and B are significant and
A2B2 (second level of A=A2, second level of B=B2) is the optimal
treatment condition. Then, the mean at the optimal condition (optimal value
of the response characteristic) is estimated as (Phillip J. Ross 1988):
Where, T = Overall mean of the response
A2, B2 = Average values of response at the second levels of parameters A and B respectively
It may also happen that the prescribed combination of
55
parameter levels (optimal treatment condition) is identical to one of those
in the experiment. If this situation exists, then the most direct way to
estimate the mean for that treatment condition is to average out all the
results for the trials which are set at those particular levels
(Phillip J. Ross 1988).
3.2.10 Determination of confidence interval
The estimate of the mean (µ) is only a point estimate based on the
average of results obtained from the experiment. Statistically this provides
a 50% chance of the true average being greater than µ. It is therefore
customary to represent the values of a statistical parameter as a range
within which it is likely to fall, for a given level of confidence
(Phillip J. Ross 1988). This range is termed as the confidence interval (CI). In
other words, the confidence interval is a maximum and minimum value between
which the true average should fall at some stated percentage of confidence.
The following two types of confidence interval are suggested
by Taguchi in regards to the estimated mean of the optimal treatment
condition.
1. Around the estimated average of a treatment condition
predicted from the experiment. This type of confidence
interval is designated as CIPOP (confidence interval for the
population).
2. Around the estimated average of a treatment condition used in
a confirmation experiment to verify predictions. This type of
confidence interval is designated as CICE (confidence interval for
a sample group).
56
The difference between CIPOP and CICE is that CIPOP is for the
entire population i.e., all parts made under the specified conditions, and
CICE is for only a sample group made under the specified conditions. Because
of the smaller size (in confirmation experiments) relative to entire population,
CICE must slightly be wider. The expressions for computing the confidence
intervals are given below (Roy R.K 1990)
3.3 Machining Performance Evaluation
The machining performance is evaluated by material removal rate
(MRR) and Dimensional Deviation. MRR is defined as amount of material
removed per unit machining time. Dimensional deviation of the machined
micro hole has been considered as machining accuracy criteria. It is the
difference between the radius of the machined hole and the radius of the tool
electrode. The diameters of holes drilled were measured with the help of an
optical microscope. Machining time is noted for each experiment. The lower
the dimensional deviation is better the machining performance. The higher the
MRR, is better the machining performance. Therefore, the dimensional
deviation is the “lower the better” and the MRR is the “Higher the better”
performance characteristic respectively.
3.3.1. Material Removal Rate (MRR)
Material removal rate is expressed as the amount of material
removed under a period of machining time (T) in minutes and calculated using
the following equation.
MRR (mm3/min) = Area of the hole (mm2) × depth of the hole (mm) Machining Time (min) (3.7)
57
3.3.2 Signal-to-Noise Ratio (S/N Ratio)
In Taguchi design methodology, basically the experimental results
are converted into a single quality characteristics evaluation index i.e. S/N
ratio. The least variation and the optimal design are obtained by means of the
S/N ratio. The benefits of S/N ratio includes increasing the factor weighting
effect, decreasing mutual action, simultaneously processing the average and
variation, and improving engineering quality. The higher the S/N ratio, the
more stable the achievable quality. Depending on the required objective
characteristics, different calculation methods can be applied as follows:
The smaller the better (SB) where the objective optimal value is the
smaller the better, dimensional deviation.
(3.8)
The larger the better (LB) where the objective optimal value is
larger the better, such as material removal rate.
(3.9)
where = S/N ratio and y = result of experiment (MRR).
3.3.3. Analysis of variance (ANOVA)
The S/N ratio determined from the experimental values were
statistically studied by ANOVA to explore the effects of each machining
parameter on the observed values and to elucidate which machining parameter
significantly affected the MRR. Different software are available to perform
ANOVA such as “DESIGN EXPERT”, “Minitab 15” etc. In this work
58
“Minitab 15” has been used for the analysis purpose. The related equations are
as follows:
Sm = ( i )2 / 18 (3.10)
ST = i2– Sm (3.11)
SA = ( Ai)2 / N – Sm (3.12)
SE = ST – SA (3.13)
VA = SA / fA (3.14)
FAO = VA / VE (3.15)
Where,
Sm = sum of squares based on the mean
ST = sum of squares based on the total variation
SA = sum of squares based on the parameter A (like electrolyte concentration, voltage, current, duty cycle or frequency)
SE = sum of squares based on the error
i = value of in the ith experiment (i = 1 to 18)
Ai = sum of the ith level parameter A (i= 1, 2 or i= 1–3)
N = number of repetition at each level parameter A,
fA = number of degrees of freedom of parameter A
VA = variance of parameter A
FAO = F– test parameter for A
F-test can be used to determine which process parameters have
significant effect on the performance characteristics. P-test is designed to
know whether factor is significant or not depending on its value. If P value is
less than alpha value which is generally taken as 0.05, then factor have
significant effect on performance characteristics.
59
The experimentations are conducted after setting the desired values
of process parameters like voltage, current, duty cycle, and frequency with the
microcontroller based pulsed power supply system. Hence, the calculation of
duty cycle has to be done in advance. In a pulsed power supply, current and
voltage switches between 0 and peak values in a set frequency.
Duty cycle is the ratio between the pulse ON time in relation to the
total experiment time i.e. sum of ON and OFF time. The ON time and OFF
time are calculated using following equations.
Duty Cycle = Ton(ms) / Ttotal(ms) (3.16)
Ttotal = Ton + Toff (3.17)
Ton = Duty cycle * Ttotal (3.18)
Frequency = 1 / Ttotal (3.19)
Table 3.2 and table 3.3 shows ON time and OFF time values
required to set for a particular frequency and duty cycles.
Table 3.2: Calculated ON time and OFF time - NICKEL
Frequency(Hz)
TotalTime (ms)
Duty cycle 33.3% 50.00% 66.66%
On time(ms)
OffTime (ms)
On time(ms)
OffTime (ms)
On time(ms)
OffTime (ms)
30 33.33 11.90 22.23 16.66 16.66 22.23 11.90 40 25 8.33 16.66 12.5 12.5 16.66 8.33 50 20 6.66 13.33 10.0 10.0 13.33 6.66 60 16.66 5.55 11.10 8.33 8.33 11.10 5.55
60
Table 3.3: Calculated ON time and OFF time - SDSS and Inconel 600
Frequency(Hz)
TotalTime (ms)
Duty cycle 33.3% 50.00% 66.66%
On time(ms)
OffTime (ms)
On time(ms)
OffTime (ms)
On time(ms)
OffTime (ms)
30 33.33 11.11 22.22 16.67 16.66 22.22 11.11 40 25 8.33 16.67 12.5 12.5 16.67 8.33 50 20 6.67 13.33 10 10 13.33 6.67
The amplitude of the pulse (ON TIME) is called the peak current.
The level of energy which is equal to D.C. level when time (duty cycle) is
considered, is called as average current or machining current. The relationship
between average and peak current is given by:
Average current = Peak current × Duty cycle (3.20)
Peak current values are to be set accordingly for getting required
machining current i.e. average current. The calculated average current for
Nickel, SDSS, and Inconel 600 are tabulated in table 3.4 and table 3.5
respectively.
Table 3.4: Average Current - NICKEL
Average current (amp)
Peak current (amp) DC 33.33% DC 50.00% DC 66.66%
0.1 0.30 0.20 0.150.3 0.90 0.60 0.450.5 1.50 1.00 0.75
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Table 3.5: Average Current - SDSS and Inconel 600
Average current (amp)
Peak current (amp) DC 33.33% DC 50.00% DC 66.66%
0.6 1.8 1.2 0.90.8 2.4 1.6 1.21.0 3.0 2.0 1.5
Similarly calculation is made for average voltage using equation
Average voltage = Peak voltage × Duty cycle. (3.21)
The required machining voltage (average voltage) can be obtained
by setting appropriate Peak voltage. The calculated machining voltage for
Nickel, SDSS, and Inconel 600 are given in table 3.6 and table 3.7
respectively.
Table 3.6: Average Voltage - NICKEL
Average voltage (volts)
Peak voltage (volts) DC 33.33% DC 50.00% DC 66.66%
3.5 10.50 15.00 19.505.0 7.00 10.00 13.006.5 5.25 7.50 9.75
Table 3.7: Average voltage - SDSS and Inconel 600 Average voltage
(volts) Peak voltage (volts)
DC 33.33% DC 50.00% DC 66.66% 8 24 16 129 27 18 13.5
10 30 20 15
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During machining, a bubble coming from the bottom side of the
work piece indicates that the hole reached the bottom. It should be observed
carefully to get accurate results. Machining to be continued till a circular hole
at the exit side is machined. For each experiment machining time is noted.
With the help of optical microscope, the diameter of the holes drilled is
recorded.
Material Removal Rate (MRR) is calculated by using machining
time, area of hole and sheet thickness for each experimental combination.
Using calculated MRR values S/N ratio for each experiment were calculated.
ANOVA is performed to determine, factor affects the MRR significantly.
Finally the experimental values are validated with Genetic Algorithms.
3.3.4 Confirmation Test
The optimum level of process parameters has been determined by
using S/N ratio values. Once the optimal level of the process parameters has
been selected, the final step is to predict and verify the improvement of the
performance characteristic using the optimal level of the process parameters.
The purpose of conformation test is to validate the conclusions drawn during
analysis phase.
The predicted or estimated S/N ratio using optimal levels of
process parameters can be calculated as;
q = m + i – m) (3.22)
i=1
where,
m = total mean of S/N ratio
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q = number of significant parameters
i = mean of S/N ratio at optimum level
After predicting the response (S/N ratio), a confirmation
experiment is designed and conducted with the optimal levels of the
machining parameters to verify the improvement of performance
characteristic.
3.4. GENETIC ALGORITHMS (GA)
3.4.1 Introduction
Genetic algorithms belong to the larger class of evolutionary
algorithms (EA), which generate solutions to optimization problems using
techniques inspired by natural evolution, such as inheritance, mutation,
selection, and crossover. Decision making situation occurs in all fields like
science, technology and management, etc. where GA is applied with an
objective to maximize or minimize a task. In order to solve the problems
related to inventory, transportation, queuing, scheduling etc., many
optimization procedures have been developed over the past six decades.
Most of the traditional optimization procedures end its search in the
“local optima” rather than finding the “global optima”. To overcome this,
many number of non traditional search and optimization algorithms were
developed over the past four decades. They are,
1. Genetic Algorithm (GA)
2. Simulated Annealing (SA)
3. Tabu Search (TS)
4. Ant Colony Optimization (ACO)
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5. Particle Swarm Optimization (PSO)
6. Scatter Search (SS), etc.
Genetic algorithms (GA) is computerized search and optimization
algorithm based on the mechanics of natural genetics and natural selection
(Goldberg D. 2000). It was inspired by Darwin’s theory about evolution.
Prof. Holland of University of Michigan envisaged the concept of GA.
Number of students and researchers have contributed for the development of
this field.
The optimization model for the ECMM process is multi-variable
non-linear objective function with non-linear constraints and is highly
complicated to solve using the traditional optimization methodologies. The
Genetic Algorithm (GA) in particular have proven to be a powerful tool to
solve such complex optimization problems without any approximation.
Genetic Algorithms are computerized search and optimization algorithms
belonging to the class of evolutionary algorithms (EA) and works with a set of
solutions.
The operation of GA begins with generation of a set of random
solution. The fitness value of each solution has to be evaluated. The higher the
fitness value, the better the solution. The generated population is then operated
by the reproduction, crossover and mutation operators to create the new
population which is evaluated and tested for the termination criterion. One
cycle of population evaluation and subsequent three GA operations constitute
a generation in the GA terminology. The GA operations are continued until
the termination criterion is met for a specified number of generations (Deb
Kalyanmoy 1995).
65
Figure 3.4: Structure of Genetic Algorithm
Start
Generate initial population
Evaluate objective function
Are optimization criteria met?
Selection
Recombination
Mutation
Best Individuals
Result
66
A simple genetic algorithm start with a set of randomly generated
initial population. The basic steps involved in the genetic algorithm are given
below.
1. [Start] Generate random population of n chromosomes (suitable solutions for the problem).
2. [Fitness] Evaluate the fitness F(X) of each chromosome X in the population.
3. [New Population] Create a new population by repeating following steps until new population is complete.
4. [Selection] Select two parent chromosomes from a population according to their fitness (the better fitness, the bigger chance to be selected).
5. [Crossover] With a crossover probability cross over the parents to form new offspring (children). If no crossover was performed, offspring is the exact copy of parents.
6. [Mutation] With a mutation probability mutate new offspring at each locus (position in chromosome).
7. [Accepting] Place new offspring in the new population.
8. [Replace] Use new generated population for a further run of the algorithm.
9. [Test] If the end condition is satisfied, stop, and return the best solution in current population.
10. [Loop] Go to step 2.
3.4.2 Implementation of GA
The principle of natural genetics is that ‘Fit parents would yield fit
offspring’. GA has wide variety of applications in engineering problems
because of simplicity and ease of operation. The minimum or maximum of
a function is found based on the variation of X1, X2, X3 . . . Xn beginning with
one or more starting point. GA evaluates a set of points, and the basic element
of GA consists of a chromosome and fitness value. The fitness value describes
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how well an individual can adapt to survival and mating. In this study, the
basic elements of GA consists of a value of electrolyte concentration,
machining current, machining voltage, duty cycle and frequency.
GA works on the basis of binary code in the form of 0 and 1. An
individual in GA is denoted by I = {EC, C, V, DC, F, f (EC, C, V, DC, F)}.
A set of search individual is called a population and general structure of GA
and convergence GA result depicting. The parameters used in GA are;
population size = 100, length of chromosome = 20, selection operator
= stochastic uniform, crossover probability = 0.8, mutation probability = 0.2,
fitness parameter = MRR. The objective function is given by
MRR = f (EC,C,V,DC,F).
Genetic algorithms can be used to solve the constrained
optimization problems as well as unconstrained optimization problems. GA
can be used to solve maximization problems as well as minimization
problems. In the chapter, a constrained optimization problem is considered to
explain the implementation of genetic algorithm. Let us consider the following
maximization problem.
Subject to the constraints maximize f(x).
XiL Xi Xi
U for i =1, 2, 3……….N (3.23)
The operation of GA begins with a population of encoded solution.
Each string is evaluated to find the fitness value. Then the population is
operated by the three important genetic operators to create a new population.
The performance of GA is mainly influenced by these three operators.
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1. Selection of Reproduction
2. Crossover
3. Mutation
Fitness Function
GA mimics the survival of the fittest principle of nature to make a
search process. Therefore, GA is naturally suitable for solving maximization
problems. Minimization problems are transformed in to maximization
problems by some suitable transformation. Fitness function F(x) is derived
from the objective function and used in successive genetic operations. Certain
genetic operators require that fitness function be non negative, although
certain operators do not have this requirement. Following are the fitness
function for different objective functions.
F(X) = f(X) for maximization problems (3.24)
F(X) = 1 for minimization problems, if f(X) 0 (3.25) f(X)
F(X) = 1 for minimization problems, if f(X) = 0 (3.26) (1+f(X))
Selection or Reproduction
Selection or reproduction is usually the first operator applied on
population. Reproduction operator selects the best chromosomes from the
population to form a matting pool for next operation. Many number of
selection operators were used in the genetic algorithm literature. The essential
ideas in all of them is, the above average strings are picked from the current
population and their multiple copies are inserted in the matting pool in
69
a probabilistic manner. In genetic algorithm, the probability of selection Ps of
each string depends on the fitness of individual string. The probability of
selection is calculated using the following equation:
Probability of selection of ith string ps = Fi (3.27)nj=1 Fj
Where Fi - Fitness value of ith string, n - Population size
The string has more probability of selection will get more chance
for selection.
Crossover or Recombination
Crossover operator produces new offspring in combining the
information contained in two parents. The crossover operation is performed
with a probability of crossover Pc, crossover occurs only if the random
number generated is less than the crossover probability Pc (like flipping of
a coin with a probability) otherwise the two strings repeated without any
change. Depending on the representation of the variables, the offspring will be
subjected to crossover.
Mutation
After crossover operation is performed, the string is subjected to
mutation operation. This is to prevent falling all solutions of the population
into a local optimum of solved problem. Mutation operator alters
a chromosome locally to hopefully create a better string. The bit wise mutation
is performed with a probability of mutation of mutation Pm. Mutation occurs
only if the random number generated is less than the mutation probability Pm
(like flipping of a coin with a probability) otherwise the bit kept unchanged.
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A simple genetic algorithm treats the mutation only as a secondary operator
with roll of restoring lost genetic materials. The mutation is also used to
maintain diversity in the population. For example, consider the following
strings.
1 1 1 0 1 0 0 1 1 1 0 0 1 0 0 0
Notice that all four strings have a ‘1’ in the leftmost bit position. If
the true optimum solution requires as a ‘0’ in the position, the selection or
cross operators will not change the value of the bit. The mutation operator will
change its value. Following are the mutation methods available for different
coded string.
1. Binary Valued Mutation
2. Real Valued Mutation
3.4.3 Experimental Validation (GA)
The optimized parameters obtained for the maximum MRR shows
that as the generation progresses the solutions are approaching optimum.
A validation of experiment is conducted using the optimum process
parameters. It is observed that MRR obtained from validation experiments is
closer to the optimized MRR obtained using GAs. It infers the practical
applicability of the combined use of Taguchi methodology, ANOVA and GAs
for optimizing the ECMM process parameters to obtain maximum MRR.
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CHAPTER 4
EXPERIMENTAL SETUP
4.1 INTRODUCTION
The ECMM system developed to conduct necessary experiments
for this research work has the following five major assemblies. The schematic
diagram of the ECMM setup is shown in figure 4.1.
Work holding platform.
Tool feeding device.
Control system.
Electrolyte flow system.
Power supply system.
Figure 4.1: Schematic Diagram of Experimental Setup
Pulse Generator
Drive Unit
Control System
Servo Motor
Filter Unit
Electrolyte Tank
Machining Chamber
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4.2 MACHINING SETUP STRUCTURE
Mild steel is selected for the structure of the machine body. The
parts made of mild steel are chromium plated for aesthetic looks and corrosion
resistance. For the parts that come into contact with electrical system which
requires insulation, fiber material is used. Parts that come into contact with
electrolyte require noncorrosive materials and hence acrylic material is used in
those places. The dimensions have been arrived based on the specifications
found in published literatures. They were further modified considering the
compactness, functional movements of mating parts, working conditions,
arrangement constraints and space utilization. The machining setup structure
consists of machining base over which a rectangular column is mounted. The
column is mounted with three angle plates with allen screws.
The size of the angle plate fabricated is 120 × 100 × 8 mm. It is
suitable to accommodate the stepper motor and other associated discrete parts.
The other two angle plates support the lead screw with the help of bearings.
The lead screw is keyed with the stepper motor shaft and passes through the
internally threaded hole of the electrode feeding section made of mild steel.
The diameter and the length of the main screw rod are 12 mm and 183 mm
respectively. In order to achieve very fine feed of the electrode, thread has
been made at 30 threads per inch for a length of 75 mm in the mid portion of
the main screw rod. This enables the linear up and downward feed of electrode
to a required level in accordance with the depth of the electrolyte chamber and
work piece placement in it.
When the stepper motor rotates, the lead screw rotates, which in
turn moves the micro tool electrode holding device which provides electrode
feed movements. Just below the tool electrode holding devices, the machining
73
chamber rests on a base plate. The base plate is provided with four bushes at
the bottom for easy handling. The dimensions of other parts are calculated
considering space arrangement and functional requirements. Inside the
machining chamber, a work holding device is mounted. The work piece which
is of few microns thick is held in the fixture, made up of two block of
insulating material fastened with screws. The one side of the fixture is
connected with a wire to the workpiece and made as anode.
4.2.1 Work Holding Platform
A rigid work holding platform made up of non corrosive material is
very essential for this ECMM setup. The rectangular platform consist of two
detachable parts which can be fastened together by means of screws. The work
piece is placed in between these two and it is tightly fastened. The work
holding platform is immersed in the electrolyte while the machining operation
is carried out. Since, we need a non corrosive platform, acrylic material is used
to fabricate the platform setup. The figure 4.2 shows the work holding
platform with its fasteners inside the electrolyte tank and the tool holding
arrangement.
The machining chamber / electrolyte tank is also made up of acrylic
material. The entire work holding platform is placed inside the chamber. The
chamber is filled with electrolyte, according to need. The electrolyte filtration
and re-circulation is carried out by using a pump and filter arrangement. The
level of electrolyte in the electrolyte chamber is maintained in such a way that
the machining zone (work piece and tool electrode) is immersed during the
machining process. Electrically non-conductive and chemically non-corrosive
materials are used at places where the electrolyte directly contacts the setup
74
such as electrolyte tank, connecting tube, electrolyte flushing nozzle, filter,
pump etc.
Figure 4.2: Work Holding Platform, Tool holding arrangement
4.2.2 Tool Feeding Device
The electrode feed system is made up of mild steel and insulating
material. The mild steel section slides along the vertical column through tie
rods and provide the required tool feed movement through the cylindrical
electrode holder. The cylindrical electrode holder is attached rigidly to the
insulated section. The tool holder is made up of copper rod, with necessary
arrangement to hold the micro machining tool at the bottom end. The
machining tool (cathode) is connected to the negative terminal of pulsed
75
power supply via the tool holder. Since, the rotation of stepper motor is
bidirectional; the tool can be moved forward and backward. The number of
steps and the direction of rotation of stepper motor are controlled with the help
of microprocessor based stepper motor driver unit. A stepper motor with
following specifications has been chosen for this experimental setup.
Resolution = 1.8 / step
Voltage = 12 V
Current = 0.6 A
Torque = 3 kg-cm
The pitch of the screw rod (30 Tpi) has been chosen in such a way
that one step rotation of the stepper motor moves the tool by 4 microns. The
Cyanoacrylate is used for providing insulation by coating it along the
circumference of the machining tool. The coating is made to avoid the stray
current effect and to ensure that the machining process takes place only at the
tip of the tool. After coating is completed, the tip of tool is ground to get round
shape by using double disk grinding machine.
4.2.3 Inter Electrode Gap Control System
Inter electrode gap control is a key factor influencing machining
accuracy during EMM. According to the characteristics of EMM, a closed
loop control is designed using microcontroller and current sensor to ensure
stable machining. The position of the micro tool electrode and the workpiece
are determined through contact sensing function and then, tool electrode is
withdrawn about 24 µm to form the minimum machining gap. The current
sensor used in the IEG system has the advantage of excellent accuracy, very
good linearity, optimized response time, no insertion losses and current
76
overload capability. The output of the current sensor is amplified and
converted into digital signal and fed to the micro controller. The machining
current is sampled during feeding of the tool electrode towards the workpiece.
In case of a short circuit, there will be a current jump-up and this is sensed by
the current sensor. Then, a decision is made to withdraw the machining tool
and to maintain the set IEG (Yong L 2003). The withdrawn tool once again
moves forward to maintain the set IEG after stopping several microns away
from the work piece to facilitate through flushing of IEG clear of all debris
according to the programmed time value.
Figure 4.3: Control System
4.2.4 Electrolyte Flow System
Smooth flow of clean electrolyte should be maintained for better
machining. Hence electrolyte cleaning is essential. Electrolyte NaNO3 is
pumped into the machining chamber with a velocity to drive out the material
77
removed during machining. The size of the machining chamber has been
chosen as 200 × 100 × 60 mm in order to accommodate various components
like work holding platform, tool electrode, nozzles with pipe to circulate the
electrolyte at the IEG etc. along with human working constraints. The
electrolyte is passed through a filter fitted with sedimentary filter cartridge to
remove the impurities. The tank, which houses the filter receives the
contaminated electrolyte from the machining chamber, filters and re-circulates
the cleaned electrolyte continuously into the machining chamber using
a centrifugal pump. The electrolyte pump with a capacity of 16 - 18 lit/min at
a head of 2.5 meter has been chosen for this setup. The rate of flow of the
electrolyte at the machining zone is controlled with a valve is fitted near the
nozzle end of the electrolyte delivery line. The proper cleaning of electrolyte
and adequate rate of flow is very important since, the machined material, if not
removed from the machining zone, would create a short circuit between the
electrodes. The electrolyte filter is shown in the figure.4.4.
Figure 4.4: Electrolyte Filter
78
The flushing of electrolyte is not only useful in removing the
machined particles but also to clear the hydrogen gas bubbles generated at the
machining zone during the machining process. The removal of hydrogen gas
bubbles is also equally important because the gas bubbles between the tool and
the work piece acts as a short circuiting medium and creates micro sparks that
can erode the tool material.
Hence, to avoid the micro spark generation, the electrolyte is
pumped in at a moderate pressure to take away the hydrogen gas generated
(Bhattacharyya B 2003). The various possible electrochemical reactions that
can take place during an electrochemical reaction are shown in the figure 4.5.
Figure 4.5: Electro Chemical Reactions
4.2.5 Microcontroller Unit
The Micro movement of machining tool is achieved through
a precision main screw rod’s rotation. The main screw rod is rotated by the
directly coupled stepper motor. The stepper motor is precisely controlled by
the microcontroller unit. A stepper motor with 1.8 /step, 12 V / ph, 0.6A / ph,
Torque : 3 kg-cm has been selected for this experimental setup. Different
79
programmes are stored in the microcontroller unit for 1) forward motion,
2) reverse motion and 3) slow forward. The micro controller is programmed
with provision to control the IEG between 20 to 50 m. The programs and
necessary commands can be entered through the keyboard connected to the
microcontroller unit. The unit is provided with the Reset Button for stopping
the stepper immediately in case of an emergency.
4.2.6 Power Supply System
The electrochemical micromachining requires variable pulsed DC
power supply. In order to have accurate control over the DC voltage, current,
frequency and duty cycle, a microcontroller based digital pulsed DC power
supply system has been chosen for this research work. The chosen power
supply system made by M/s. Dynatronics, USA gives a wide range of controls
over the various aspects of the power supply system with real time digital
readouts and simple to use controls. The pulse rectifier Dynatronics - DP-40-
15-30 is shown in figure 4.6 followed by the specifications of the instrument.
Figure 4.6: Pulse Rectifier
80
The applications of the direct current through a solution of
electrolyte results in redox reaction where as the application of Alternating
Current (AC) leads to conduction only. This is due to very fast change
polarities in electrodes and the electrode reaction occurring in the first half
cycle is reversed in the other half cycle of the AC current. Hence only DC is
used in this application.
Specifications of Pulse Rectifier DP-40-15-30
o Microprocessor-based controls for accuracy and repeatability
o Soft-touch keypad and digital displays
o Minimum pulse width: 0.1 millisecond on/off
o Selectable pulse timing resolutions
o Typical pulse rise and fall times: <50 microseconds
o Minimum suggested setting: 10% of maximum current rating
o Regulation accuracy: +/– 1% of setting or 0.1% of peak
rating
o Ripple: <1% RMS of maximum rated output voltage
o Selectable current, voltage or cross-over regulation modes
o Output voltage and current tolerance limit settings with alarms
o Ampere time and real time cycle control via front panel
o Resettable ampere time totalizer with password protection
o Selectable ampere time and real time counter resolutions
o Audible alarm for end-of-cycle and out-of-tolerance condition
o Save/Recall/Delete settings feature
o Built in fault detection for over-temperature and power failure
o RS485 Serial control standard & Windows-based program
o Powder coated aluminum enclosure 8.75" × 17" × 23"
o Forced air cooling through sealed heat sink tunnel
o Environmentally sealed to protect power supply from harsh environments
81
The complete experimental setup is shown in figure 4.7.
Figure 4.7: Complete Experimental Setup
4.3 MATERIALS FOR RESEARCH
The work materials chosen for this research work are Nickel, Super
Duplex Stainless Steel (SDSS), and Inconel 600. The chosen materials are
excellent materials for shielding against magnetic interference. They offer
high corrosion resistance, fine thermal, and electrical properties along with
excellent mechanical properties. These difficult to cut alloys are largely being
used in biomedical, Communication, Aerospace, Thermal, and Nuclear power
plants.
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4.3.1 Nickel
Nickel is the 5th most common element on earth, exists mainly in
the form of sulphide, oxide, and silicate minerals. Nickel is an extremely
important commercial element, playing a key role in global industrial
development and outpacing almost all other industrial metals. Nickel is
a silvery-white lustrous metal with a slight golden tinge. It is one of only four
elements that are magnetic at or near room temperature, the others being iron,
cobalt and gadolinium.
The factors which make nickel and its alloys valuable commodities
include strength, corrosion resistance, high ductility, good thermal and electric
conductivity, magnetic characteristics and catalytic properties. Nickel, above
355 °C becomes non-magnetic material (Curie temperature). The unit cell of
nickel is a face centered cube with the lattice parameter of 0.352 nm giving an
atomic radius of 0.124 nm. Nickel belongs to the transition metals and is hard
and ductile.
The Nickel's slow rate of oxidation at room temperature qualifies it
as corrosion-resistant. The metal is chiefly valuable in the modern world for
the alloys it forms; about 60% of world production is used in nickel-steels
(particularly stainless steel); 14% in nickel-copper and nickel silver alloys; 9%
to make malleable nickel, nickel clad, Inconel, and other superalloys; 6% in
plating; 3% for nickel cast irons; 3% in heat and electric resistance alloys,
such as Nichrome; 2% for Nickel brasses and bronzes; 3% in all other
applications combined. Tables 4.1, 4.2, 4.3, and 4.4 presents the general,
physical, atomic and miscellaneous properties of Nickel respectively.
83
Table 4.1: General Properties of Nickel
Name, symbol, number Nickel, Ni, 28 Element category transition metal
Group, period, block 10, 4, d Standard atomic weight 58.6934(4)(2) Electron configuration [Ar] 4s2 3d8 or [Ar] 4s1 3d9
Electrons per shell 2, 8, 16, 2 or 2, 8, 17, 1
Table 4.2: Physical Properties of Nickel
Phase SolidDensity (near r.t.) 8.908 g·cm 3
Liquid density at m.p. 7.81 g·cm 3
Melting point 1728 K, 1455 °C, 2651 °F Boiling point 3186 K, 2913 °C, 5275 °F Heat of fusion 17.48 kJ·mol 1
Heat of vaporization 377.5 kJ·mol 1
Molar heat capacity 26.07 J·mol 1·K 1
Table 4.3: Atomic Properties of Nickel
Oxidation states 4[1], 3, 2, 1 [2], –1 (mildly basic oxide)
Electro negativity 1.91 (Pauling scale)
Ionization energies (more)
1st: 737.1 kJ·mol 1
2nd: 1753.0 kJ·mol 1
3rd: 3395 kJ·mol 1
Atomic radius 124 pm Covalent radius 124±4 pm
Van der Waals radius 163 pm
84
Table 4.4: Miscellaneous Properties of Nickel
Crystal structure face-centered cubic
Magnetic ordering ferromagnetic
Electrical resistivity (20 °C) 69.3 n ·m
Thermal conductivity 90.9 W·m 1·K 1
Thermal expansion (25 °C) 13.4 µm·m 1·K 1
Speed of sound (thin rod) (r.t.) 4900 m·s 1
Young's modulus 200 GPa
Shear modulus 76 GPa
Bulk modulus 180 GPa
Poisson ratio 0.31
Mohs hardness 4.0
Vickers hardness 638 MPa
Brinell hardness 700 MPa
Nickel is used in many specific and recognizable industrial and
consumer products, including stainless steel, alnico magnets, coinage,
rechargeable batteries, electric guitar strings, microphone capsules, and special
alloys. It is also used for plating and to produce green tint in glass. Nickel is
preeminently an alloy metal, and its chief use is in the nickel steels and nickel
cast irons, of which there are many varieties. It is also widely used in many
other alloys, such as nickel brasses and bronzes, and alloys with copper,
chromium, aluminium, lead, cobalt, silver, and gold (Inconel, Incoloy, Monel,
Nimonic).
Nickel can be used to shield communication devices, transducers
and gyroscopes due to its high magnetic permeability property. This makes it
an excellent material for shielding against magnetic disturbance. Because
85
Nickel offers good mechanical properties, high corrosion resistance, fine
thermal, and electric properties, it can be used to fabricate micro thermocouple
for transient temperature measurement. It can be used as a mould material for
plastic injection moulding to produce micro planetary gears. In production of
micro and meso lenses, Nickel stamper is used.
Nickel is an excellent alloying agent for certain other precious
metals, and so used in the so-called fire assay, as a collector of platinum group
elements (PGE). Nickel and its alloys are frequently used as catalysts for
hydrogenation reactions. It is also used as a binder in the cemented tungsten
carbide or hard metal industry.
Ferronickel is an alloy containing Nickel and Iron, approximately
35% Nickel and 65% Iron. Ferronickel is primarily used in the manufacture of
Austenitic stainless steels (known as 200 and 300 series). These are
nonmagnetic and contains between 8.5% to 25% Nickel, enhancing their
corrosion resistance. They are the most widely used group of stainless steels,
accounting for 70% - 75% of global stainless output.
4.3.2 SDSS
The first widely available super duplex stainless steel was
developed by Gradwell and co workers in the mid 1980’s. This alloy was
called Zeron 100® and was developed as a casting alloy for pump applications
in the oil and gas industry. The performance of the steel in this application
generated a demand for the alloy in wrought product forms also.
86
As demand for the steel grew, clients called for ASTM, NACE,
British Standards, and other codes to include and cover the Zeron range of
products. In 1993-94 ASTM considered the properties of several heats of
ZERON 100 in a range of product forms and on the basis of this designated
the code UNS S32760 to the alloy and introduced this number into several
standards. The term "Super-Duplex" was first used in the 1980's to denote
highly alloyed, high-performance Duplex steel with a pitting resistance
equivalent of >40 (based on Cr% + 3.3Mo% + 16N%).
Super duplex stainless steels (SDSS) may be defined as a group of
steels having a two phase ferrite-austenite microstructure after heat treatment and
water quenching, with a pitting resistance equivalent number (PREN) higher than
40. The PREN value is linked to the content of the three most important elements
in the alloy, namely, Cr, Mo, and N, with each of them weighted according to its
influence on pitting. The approximately equal volume fractions of ferrite and
austenite are achieved by the simultaneous control of the chemical composition
and the annealing temperature.
Due to their excellent corrosion resistance in chloride environments,
these alloys are widely used as structural materials for chemical plants,
phosphoric acid production plants, hydrometallurgy industries, and as materials
for offshore applications. These alloys also possess superior weldability and
better mechanical properties than austenitic stainless steels.
The combination of high strength and corrosion resistance makes
super duplex stainless steel attractive for a number of applications both in sour
process fluids and seawater. Several super duplex alloys exist and each has its
own proprietary chemical composition.
87
Super-Duplex Stainless Steel provides outstanding resistance to
acids, acid chlorides, caustic solutions, and other such environments. This
alloy with its high level of chromium, often replaces 300 series stainless steel,
high nickel super austenitic steels in the chemical / petrochemical, pulp, and
paper industries. SDSS also provides excellent resistance to inorganic acids,
especially those containing chlorides.
The chemical composition based on high contents of chromium,
nickel, and molybdenum improves inter granular and pitting corrosion
resistance. Additions of nitrogen promote structural hardening by interstitial
solid solution mechanism. Due to this, the yield strength and ultimate strength
values are raised without impairing toughness. Further, the two-phase
microstructure guarantees higher resistance to pitting and stress corrosion
cracking.
BENEFITS
High strength.
High resistance to pitting, crevice corrosion resistance.
High resistance to stress corrosion cracking, corrosion fatigue,
and erosion.
Excellent resistance to chloride stress-corrosion cracking.
High thermal conductivity.
Low coefficient of thermal expansion.
Good sulfide stress corrosion resistance.
Low thermal expansion and higher heat conductivity than
austenitic steels.
Good workability and weldability.
High energy absorption.
88
APPLICATIONS
Heat exchangers, tubes, and pipes for production and handling
of gas and oil.
Heat exchangers and pipes in desalination plants.
Mechanical and structural components.
Power industry FGD systems.
Pipes in process industries handling solutions containing
chlorides.
Utility and industrial systems, rotors, fans, shafts and press rolls
where the high corrosion fatigue strength can be utilized.
Cargo tanks, vessels, piping, and welding consumables for
chemical tankers.
High-strength, highly resistant wiring.
Duplex stainless steels are graded for their corrosion performance
depending on their alloy content. Duplex stainless steel can be divided into
four groups:
Lean Duplex such as 2304, which contains no deliberate Mo
addition.
2205, the work-horse grade accounting for more than 80% of
duplex usage.
25 Cr duplex such as Alloy 255 and DP-3.
Super-Duplex; with 25-26 Cr and increased Mo and N
compared with 25 Cr grades, including grades such as 2507,
Zeron 100, UR 52N+, and DP-3W.
In this study, the SDSS 2205 (UNS S31803) is used for the
experiments. Alloy 2205 is a 22% Chromium, 3% Molybdenum, 5 - 6%
89
Nickel, nitrogen alloyed duplex stainless steel with high general, localized and
stress corrosion resistance properties in addition to high strength and excellent
impact toughness. The table 4.5 shows the chemical compositions of various
types of SDSS.
Table 4.5: Specifications of Super Duplex Stainless Steel
Specification Composition
UNS S32760 Super Duplex Stainless Steel. 25% chromium super duplex (austenitic/ferritic) steel with 0.75% tungsten and copper
UNS S31803 Duplex Stainless Steel. 22% chromium duplex (austenitic/ ferritic) steel (2205 type)
UNS S32750 Super Duplex Stainless Steel. 25% chromium copper-free super duplex (austenitic/ferritic) steel (also known as 2507)
UNS S32550Super Duplex Stainless Steel. High performance 25% chromium super duplex (austenitic/ferritic) steel with 1.75% copper
Alloy 2205 provides pitting and crevice corrosion resistance
superior to 316L or 317L austenitic stainless steels in almost all corrosive
media. It also has high corrosion and erosion fatigue properties as well as
lower thermal expansion and higher thermal conductivity than austenitic. The
yield strength is about twice that of austenitic stainless steels. This allows
a designer to save weight and makes the alloy more cost competitive when
compared to 316L or 317L austenitic stainless steels. Alloy 2205 is
particularly suitable for applications covering the – 45ºC / +315ºF temperature
range. Temperatures outside this range may be considered but need some
restrictions, particularly for welded structures.
90
4.3.3 INCONEL 600
Inconel refers to a family of austenitic nickel-chromium-based
superalloys. Inconel alloys are typically used in high temperature applications.
Common trade names for Inconel include: Inconel 625, Chronin 625, Altemp
625, Haynes 625, Nickelvac 625, and Nicrofer 6020.
The Inconel family of alloys was first developed in the 1940s by
research teams at Wiggin Alloys (Hereford, England), which has since been
acquired by SMC, in support of the development of the Whittle jet engine.
Different Inconels have widely varying compositions, but all are
predominantly nickel, with chromium as the second element. Inconel 600 is
covered by the 1) BS 3075 and BS 3076 NA 14, 2) AMS 5687 and 3) ASTM
B166 standards. Inconel 600 is the trade name of Special Metals Group of
Companies and equivalent to: UNS N06600, W.NR 2.4816 and AWS 010.
The tables 4.6 gives the compositions of various Inconel alloys. The physical
properties of Inconel 600 is tabulated in table 4.7.
Table 4.6: Compositions of Inconel Alloy
Inconel Element (% by mass)
600 Ni 72%, Cr 17%, Fe 10%, Mn 1% with Cu, Si, C, S
617 Ni 56%, Cr 24%, Fe 3%, Mo 10%, with Mn, Cu, Al, Ti, Si, C, S
625 Ni 58%, Cr 23%, Fe 5%, Mo 10%, with Mn, Al, Ti, Si, C, S
718 Ni 55%, Cr 21%, Mo 3%, Nb 5% with Co, Fe, Mn, Al, Cu, Ti, Si, C, S
X-750 Ni 70%, Cr 17%, Fe 9%, Nb 1% with Co, Mn, Al, Cu, Ti, Si, C, S
91
Table 4.7: Physical properties of Inconel 600
Density 8.47 g/cm3
Melting point 1413 °C
Coefficient of Expansion 13.3 µm/m.°C (20-100°C)
Modulus of rigidity 75.6 kN/mm2
Modulus of elasticity 206 kN/mm2
Inconel alloys are oxidation and corrosion resistant materials, well
suited for service in extreme environments subjected to pressure and heat.
When heated, Inconel 600 forms a thick, stable, passivating oxide layer
protecting the surface from further attack. Inconel 600 retains strength over
a wide temperature range, attractive for high temperature applications where
aluminum and steel would succumb to creep as a result of thermally-induced
crystal vacancies. Inconel's high temperature strength is developed by solid
solution strengthening or precipitation strengthening, depending on the alloy.
In age hardening or precipitation strengthening varieties, small amounts of
niobium combine with nickel to form the intermetallic compound Ni3Nb or
gamma prime ( '). Gamma prime forms small cubic crystals that inhibit slip
and creep effectively at elevated temperatures.
Inconel 600 is a difficult metal to shape and machine using
traditional techniques due to rapid work hardening. After the first machining
pass, work hardening tends to plastically deform either the workpiece or the
tool on subsequent passes. For this reason, age-hardened Inconels such as 718
are machined using an aggressive but slow cut with a hard tool, minimizing
the number of passes required. Alternatively, the majority of the machining
can be performed with the workpiece in a solutionized form, with only the
final steps being performed after age-hardening. External threads are
92
machined using a lathe to "single point" the threads, or by rolling the threads
using a screw machine. Holes with internal threads are made by welding or
brazing threaded inserts made of stainless steel. Internal threads can also be
formed using EDM machining.
Cutting of plate is often done with a water jet cutter. Internal
threads can also be cut by single point method on lathe, or by thread milling
on a machining center. New whisker reinforced ceramic cutters are also used
to machine nickel alloys. They remove material at a rate typically 8 times
faster than carbide cutters. 718 Inconel can also be roll threaded after full
aging by using induction heat to 1300 degrees F without increasing grain size.
Apart from these methods, Inconel parts can also be manufactured by
Selective laser melting.
Welding Inconel alloys is difficult due to cracking and
microstructural segregation of alloying elements in the heat affected zone.
However, several alloys have been designed to overcome these problems. The
most common welding methods are gas tungsten arc welding and electron
beam welding. New innovations in pulsed micro laser welding have also
become more popular in recent years.
Inconel 600 is chiefly used in gas turbine blades, seals, and
combustors, as well as turbocharger rotors and seals, electric submersible well
pump motor shafts, high temperature fasteners, chemical processing, and
pressure vessels, heat exchanger tubing, steam generators in nuclear
pressurized water reactors, natural gas processing with contaminants such as
H2S and CO2, firearm sound suppressor blast baffles, and Formula One and
93
NASCAR exhaust systems. Inconel 600 is increasingly used in the boilers of
waste incinerators.
Inconel 600 is used in the construction of higher end firearms sound
suppressors and muzzle devices. This is especially common in suppressors
designed to be especially small or for use with machine guns. Rolled Inconel
600 was frequently used as the recording medium by engraving in black box
recorders on aircraft.
Alternatives to the use of Inconel 600 in chemical applications such
as scrubbers, columns, reactors, and pipes are Hastelloy, perfluoroalkoxy
(PFA) lined carbon steel or fiber reinforced plastic.
Alloy 600 is a nonmagnetic, nickel-based high temperature alloy
possessing an excellent combination of high strength, hot and cold
workability, and resistance to ordinary form of corrosion. This alloy also
displays good heat resistance and freedom from aging or stress corrosion
throughout the annealed to heavily cold worked condition range.
The high chromium content of alloy 600 raises its oxidation
resistance considerably above that of pure nickel, while its high nickel content
provides good corrosion resistance under reducing conditions. This alloy
exhibits high levels of resistance to stress and salt water, exhaust gases, and
most organic acids and compounds.
Alloy 600 is not an age hardening alloy; cold working is the only
available means of hardening. Softening by annealing begins at about 871°C,
94
and is reasonably complete after 10 to 15 minutes of heating at 982°C. Above
this temperature, grain growth may be objectionable, although very brief
heating at 1037°C will cause complete softening without undue grain growth.
Since the rate of cooling has no effect on the softening, the material may be
water quenched or air cooled.
Low sulfur reducing furnace atmospheres should be used in
forging. Major hot working should be done between 1260/1010°C, while light
working may be continued as low as 871°C. No hot working should be
attempted between 871/648°C due to lower ductility in that range.
95
CHAPTER 5
EXPERIMENTAL RESULTS AND ANALYSIS
5.1 INTRODUCTION
In this chapter, the experimental results obtained from Taguchi
experimental design method is discussed elaborately. The scheme of
experiments to investigate the effect of process parameter on MRR has been
selected in line with Taguchi design methodology.
5.2 SELECTION OF ORTHOGONAL ARRAY
In order to identify the true behavior of MRR, five process
parameters each at three levels have been considered for this study. The levels
of the individual process parameter are given in table 5.1, 5.2, and 5.3 for
Nickel, SDSS, and Inconel 600 respectively.
Table 5.1: Process Parameters and their Levels - NICKEL
Factor EC V C DC FLevel 1 0.1 3.5 0.1 33.33 30Level 2 0.2 5.0 0.3 50.00 40Level 3 0.3 6.5 0.5 66.66 50
96
Table 5.2: Process Parameters and their Levels - SDSS
Factor EC V C DC FLevel 1 0.40 8 0.6 33.33 30Level 2 0.45 9 0.8 50.00 40Level 3 0.50 10 1.0 66.66 50
Table 5.3: Process Parameters and their Levels - Inconel 600
Factor EC V C DC FLevel 1 0.40 8 0.6 33.33 30Level 2 0.45 9 0.8 50.00 40Level 3 0.50 10 1.0 66.66 50
EC: Electrolyte Concentration (mol/lit), V: Voltage (Volt), C: Current (Ampere), DC: Duty Cycle (%), F: Frequency (Hz).
A set of three levels assigned to each process parameter with two
degrees of freedom (DOF) as per Taguchi experimental design philosophy.
This gives a total of ten DOF for five process parameters selected in this work.
The five process parameters; Electrolyte concentration,
Machining Current, Machining Voltage, Duty Cycle, and Frequency are
studied in this investigation with each parameter having two degrees of
freedom. As per the standards of Taguchi methodology, 7 degrees of
freedom assigned for Error. Thus we have a total of 17 DOF for the factors
as well as the interactions considered for the present experiments. The
nearest three level orthogonal array available satisfying the criterion of
selecting the OA is L18 having 17 DOF (Phillip J. R. 1988). The layout of
experiments designed using Taguchi Design methodology has been
furnished in table 5.4.
97
Table 5.4: Experiment Layout using L18 Orthogonal Array
Exp. No
Levels of Process Parameters Electrolyte
concentrationMachining
Voltage Machining
Current Duty cycle Frequency
1 1 1 1 1 12 1 2 2 2 23 1 3 3 3 34 2 1 1 2 25 2 2 2 3 36 2 3 3 1 17 3 1 2 1 38 3 2 3 2 19 3 3 1 3 210 1 1 3 3 211 1 2 1 1 312 1 3 2 2 113 2 1 2 3 114 2 2 3 1 215 2 3 1 2 316 3 1 3 2 317 3 2 1 3 118 3 3 2 1 2
The levels of process parameters are selected based on the research
done by various researchers and based on the pilot experiments conducted for
this research work. It is observed that, for pure Nickel, the electrolyte
concentration and machining voltage requirement are less than that of SDSS and
Inconel 600. Similarly, the machining current requirement of other Nickel alloys
are higher than that of pure Nickel. The duty cycle and frequency levels are kept
uniform for Nickel, SDSS, and Inconel 600 throughout this experimental
investigation.
98
The levels of process parameters selected based on the Taguchi’s
design methodology for Nickel to fit L18 orthogonal array is given in table 5.5.
Table 5.5: Orthogonal Array of Process Parameters - NICKEL
Exp. No
Electrolyte concentration
(mol/lit)
Machining Voltage (Volts)
Machining Current (Amps)
Duty cycle(%)
Frequency (Hz)
1 0.1 3.5 0.1 33.33 302 0.1 5.0 0.3 50.00 403 0.1 6.5 0.5 66.66 504 0.2 3.5 0.1 50.00 405 0.2 5.0 0.3 66.66 506 0.2 6.5 0.5 33.33 307 0.3 3.5 0.3 33.33 508 0.3 5.0 0.5 50.00 309 0.3 6.5 0.1 66.66 4010 0.1 3.5 0.5 66.66 4011 0.1 5.0 0.1 33.33 5012 0.1 6.5 0.3 50.00 3013 0.2 3.5 0.3 66.66 3014 0.2 5.0 0.5 33.33 4015 0.2 6.5 0.1 50.00 5016 0.3 3.5 0.5 50.00 5017 0.3 5.0 0.1 66.66 3018 0.3 6.5 0.3 33.33 40
The levels of process parameters selected based on the Taguchi’s
design methodology for SDSS to fit L18 orthogonal array is given in table 5.6.
99
Table 5.6: Orthogonal Array of Process Parameters for SDSS
Exp. No
Electrolyte concentration
(mol/lit)
Machining Voltage (Volts)
Machining Current (Amps)
Duty cycle(%)
Frequency (Hz)
1 0.40 8 0.6 33.33 302 0.40 9 0.8 50.00 403 0.40 10 1.0 66.66 504 0.45 8 0.6 50.00 405 0.45 9 0.8 66.66 506 0.45 10 1.0 33.33 307 0.50 8 0.8 33.33 508 0.50 9 1.0 50.00 309 0.50 10 0.6 66.66 4010 0.40 8 1.0 66.66 4011 0.40 9 0.6 33.33 5012 0.40 10 0.8 50.00 3013 0.45 8 0.8 66.66 3014 0.45 9 1.0 33.33 4015 0.45 10 0.6 50.00 5016 0.50 8 1.0 50.00 5017 0.50 9 0.6 66.66 3018 0.50 10 0.8 33.33 40
The levels of process parameters selected based on the Taguchi’s
design methodology for Inconel 600 to fit L18 orthogonal array is given in table
5.7.
100
Table 5.7: Orthogonal Array of Process Parameters for Inconel 600
Exp. No
Electrolyte concentration
(mol/lit)
Machining Voltage (Volts)
Machining Current (Amps)
Duty cycle(%)
Frequency (Hz)
1 0.40 8 0.6 33.33 302 0.40 9 0.8 50.00 403 0.40 10 1.0 66.66 504 0.45 8 0.6 50.00 405 0.45 9 0.8 66.66 506 0.45 10 1.0 33.33 307 0.50 8 0.8 33.33 508 0.50 9 1.0 50.00 309 0.50 10 0.6 66.66 4010 0.40 8 1.0 66.66 4011 0.40 9 0.6 33.33 5012 0.40 10 0.8 50.00 3013 0.45 8 0.8 66.66 3014 0.45 9 1.0 33.33 4015 0.45 10 0.6 50.00 5016 0.50 8 1.0 50.00 5017 0.50 9 0.6 66.66 3018 0.50 10 0.8 33.33 40
The process parameters for SDSS and Inconel 600 are selected in
similar range as the electro-chemical, physical and mechanical characteristics are
by and large identical. It is inferred from the literature survey that, high density
of current is required for alloys with high steel content.
The machining voltage is chosen in a range from 8 to 10 volts to
achieve an appreciable MRR. It’s observed that higher voltage and moderate
value of pulse on time will produce a more accurate shape with fewer overcuts at
101
moderate MRR (Mithra S, Boro A.K 2002). The moderate electrolyte
concentration with high frequency can reduce the dimensional deviation with
lesser number of micro sparks (Malapati M, Munda J, Sarkar A 2007) and hence
a frequency range of 30 to 50 Hz is selected for this study. High precision is
achieved in ECMM by better monitoring and control of the IEG accurately and
minimizing the micro sparks at IEG. Suitable duty cycle is highly important for
maintaining IEG as the off time is used to clear the debris from the machining
zone.
5.3 EXPERIMENTAL RESULTS
The ECMM experiments are conducted with brass wire tool of
250 microns diameter for Nickel. The tool used for machining SDSS and
Inconel 600 is stainless steel wire of 250 microns diameter. In order to
achieve proper circularity of machined holes, the anode tool is properly
ground. The test job specimens are kept uniform in size for Nickel, SDSS,
and Inconel 600 measuring 50 mm × 25 mm × 0.15 mm. The test
specimens are prepared using WEDM machine and after machining, the
specimens treated to retain their originality. The electrolyte used for
Nickel and its alloys is NaNO3.
The ECMM experiments were conducted twice in each
combination of process parameters to study its effect over MRR. From the
trial 1 and trial 2 experiments, the average MRR is calculated and tabulated for
Nickel in table 5.8. In the present study all the designs, plots and analysis
have been carried out using Minitab statistical software.
102
Table 5.8: Experimental Results for MRR - NICKEL
Exp. No.Machining
Time in Trial 1
Machining Time in Trial 2
MRRTrial 1
MRRTrial 2
Average MRR
1 21.0 15.0 0.001660 0.001604 0.001632 2 8.0 13.0 0.002127 0.002119 0.002123 3 4.0 3.5 0.006223 0.006391 0.006307 4 14.0 22.0 0.001815 0.001863 0.001839 5 6.5 5.5 0.004089 0.003977 0.004033 6 2.8 2.5 0.006767 0.006639 0.006703 7 10.5 19.5 0.002869 0.002879 0.002874 8 2.5 3.8 0.009628 0.009526 0.009577 9 5.5 5.0 0.003399 0.003053 0.003226 10 8.0 11.0 0.003722 0.003726 0.003724 11 12.0 16.0 0.001343 0.001395 0.001369 12 8.0 4.8 0.004621 0.004729 0.004675 13 5.5 8.0 0.004832 0.004782 0.004807 14 4.3 9.8 0.004378 0.004426 0.004402 15 15.0 22.0 0.001873 0.001887 0.001880 16 5.0 5.5 0.003884 0.003978 0.003931 17 5.5 4.3 0.003399 0.003387 0.003393 18 3.0 4.0 0.006474 0.006618 0.006546
The average MRR is calculated in similar fashion for SDSS and
Inconel 600. The respective tables are listed under Appendix 1 as table A 1.1
and table A 1.2. From this point onwards, the Average MRR is termed as
“MRR”.
103
5.3.1 Experimental Results - Nickel
The machining time, MRR, dimensional deviation and
calculated S/N Ratio are given for Nickel in table 5.9.
Table 5.9: Experimental Results - NICKEL
Exp. No
Machining time (min)
MaterialRemoval
Rate(mm3/min.)
Dimensional Deviation (microns)
S/N Ratio
1 18.00 0.001632 29 – 55.7456 2 10.50 0.002123 13 – 53.4610 3 4.00 0.006307 20 – 44.0035 4 18.00 0.001839 21 – 54.7084 5 6.00 0.004033 22 – 47.8874 6 2.63 0.006703 14 – 43.4746 7 15.00 0.002874 25 – 50.8303 8 3.13 0.009577 20 – 40.3754 9 5.25 0.003226 14 – 49.8267 10 9.50 0.003724 25 – 48.5798 11 14.00 0.001369 11 – 57.2719 12 6.38 0.004675 31 – 46.6044 13 6.75 0.004807 22 – 46.3625 14 7.00 0.004402 14 – 47.1270 15 18.50 0.001880 23 – 54.5168 16 5.25 0.003931 15 – 48.1099 17 4.88 0.003393 14 – 49.3883 18 3.50 0.006546 15 – 43.6805
It can be seen from the experimental results of Nickel, the obtained MRR
ranges from 0.001369 to 0.009577 mm3/min, while the dimension deviation
stood between 11 and 31 microns.
104
The eighth combination of levels of process parameters, A3B2C3D2E1
gave maximum MRR of 0.009577 mm3/min. At this combination, the machining
performance is better with a low dimensional deviation of 20 microns. Hence
this combination proved to be the optimum process parameter to produce the
maximum MRR on Nickel. The microscopic image of the outcome of 8th
experiment is shown as figure 5.1.
Figure 5.1: Image of micro hole machined in 8th experiment
Parameters : Electrolyte Concentration : 0.3 mol/lit Voltage: 5 Volts Current: 0.5 amps Duty Cycle: 50% Frequency: 30 Hz MRR: 0.009577 mm3/min Dimensional Deviation: 20 microns
The twelfth combination of process parameters EC1V3C2DC2F1
produced an above average MRR (0.004675 mm3/min) with moderate
machining time. However, the dimensional deviation has peaked with 31
microns. The microscopic image of the outcome of 12th experiment is shown as
figure 5.2.
105
Figure 5.2: Image of micro hole machined in 12th experiment
Parameters: Electrolyte Concentration: 0.1 mol/lit Voltage: 6.5 Volts Current: 0.3 amps Duty Cycle: 50% Frequency: 30 Hz MRR: 0.004675 mm3/min Dimensional Deviation: 31 microns
The process parameters combination EC1V2C1DC1F3 is chosen as
the 11th combination in the experimental investigation. This combination
yielded the least MRR (0.001369 mm3/min) with 31 microns of dimensional
deviation in spite of higher machining time (6.38 min.). The microscopic
image of the outcome of 11th experiment is shown as figure 5.3.
Figure 5.3: Image of micro hole machined in 11th experiment
Parameters: Electrolyte Concentration: 0.1 mol/lit Voltage: 5 Volts Current: 0.1 Amps Duty Cycle: 33.33% Frequency: 50 Hz MRR: 0.001369 mm3/min Dimensional Deviation: 11 microns
106
5.3.2 Experimental Results - SDSS
The machining time, MRR, dimensional deviation and
calculated S/N Ratio are given for SDSS in table 5.10.
Table 5.10: Experimental Results - SDSS
Exp. No
Machining time (min)
MaterialRemoval
Rate(mm3/min.)
Dimensional Deviation (microns)
S/N Ratio
1 22.00 0.0009092 20 – 60.8266 2 21.00 0.0020666 24 – 53.6948 3 24.00 0.0026119 26 – 51.6608 4 10.20 0.0025774 22 – 51.7763 5 13.00 0.0045603 28 – 46.8201 6 14.50 0.0025698 20 – 51.8020 7 26.00 0.0008991 21 – 60.9233 8 11.00 0.0057275 38 – 44.8405 9 9.50 0.0075404 39 – 42.4521 10 11.15 0.0020446 20 – 53.7879 11 24.30 0.0018904 22 – 54.4689 12 13.00 0.0028575 28 – 50.8802 13 13.15 0.0043241 32 – 47.2820 14 20.30 0.0017994 23 – 54.8974 15 12.20 0.0046340 27 – 46.6808 16 11.15 0.0092938 25 – 40.6361 17 8.50 0.0170660 30 – 35.3573 18 14.00 0.0025572 26 – 51.8447
It can be seen from the experimental results of SDSS, the obtained
MRR ranged from 0.000899 to 0.0170660 mm3/min. The dimension deviation
varied between 20 and 39 microns.
107
Figure 5.4: Image of micro hole machined in 17th experiment
Parameters: Electrolyte Concentration : 0.5 mol/lit Voltage: 9 Volts Current: 0.6 Amps Duty Cycle: 66.66% Frequency: 30 Hz MRR: 0.0170660 mm3/min Dimensional Deviation : 30 microns
The optimum combination of process parameters for SDSS has been
identified as the 17th combination of the experiments carried out
(EC3V2C1DC3F1). The maximum MRR resulted was 0.017066 mm3/min while
the dimensional deviation stood at 30 microns. The machining process was an
effective one with less machining time of 8.5 min. The microscopic image of the
outcome of 17th experiment is shown as figure 5.4.
Figure 5.5: Image of micro hole machined in 16th experiment
108
Parameters: Electrolyte Concentration : 0.5 mol/lit Voltage: 8 Volts Current: 1 Amps Duty Cycle: 50 % Frequency: 50 Hz MRR : 0.009294 mm3/min. Dimensional Deviation: 25 microns
The second best result achieved for maximum MRR is with the 16th
combination of levels of process parameters selected. The results obtained were
MRR: 0.009294 mm3/min, dimensional deviation of 25 microns and
a machining time of 11.15 minutes. The microscopic image of the outcome of
16th experiment is shown as figure 5.5.
Figure 5.6: Image of micro hole machined in 7th experiment.
Parameters: Electrolyte Concentration : 0.5 mol/lit Voltage: 8 volts Current: 0.8 Amps Duty Cycle: 33.33% Frequency: 50 Hz MRR : 0.0008991 mm3/min. Dimensional Deviation : 21 microns
The least MRR of 0.0008991 mm3/min. has been exhibited for SDSS
was by the 7th combination (EC3V1C2DC1F3) of levels of experimental
parameters. This was evident with minimum MRR, higher deviation and
maximum machining time posted in this combination. The microscopic image of
the outcome of 7th experiment is shown as figure 5.6.
109
5.3.3 Experimental Results - Inconel 600
Table 5.11: Experimental Results - Inconel 600
Exp. No
Machining time (min)
MaterialRemoval
Rate(mm3/min.)
Dimension Deviation (microns)
S/N Ratio
1 32.5 0.0003606 10 – 68.8583 2 23.5 0.0008293 32 – 61.6253 3 15.0 0.0010358 35 – 59.6945 4 30.0 0.0006629 18 – 63.5700 5 22.0 0.0007723 29 – 62.2446 6 19.0 0.0010680 36 – 59.4286 7 31.5 0.0003672 16 – 68.7015 8 26.0 0.0004046 18 – 67.8593 9 20.0 0.0007331 22 – 62.6969 10 22.0 0.0007638 24 – 62.3397 11 29.0 0.0006052 19 – 64.3616 12 25.0 0.0007172 19 – 62.8867 13 20.0 0.0008402 31 – 61.5119 14 29.0 0.0006183 18 – 64.1759 15 23.5 0.0007549 20 – 62.4419 16 27.5 0.0006044 22 – 64.3737 17 13.0 0.0012926 30 – 57.7707 18 26.0 0.0004046 16 – 67.8595
It can be observed from the experimental results of Inconel 600 (table
5.11) that the MRR varied between 0.000361 mm3/min and 0.001293
mm3/min for Inconel 600. The dimensional deviation ranged from 10 to 36
microns. Based on the S/N Ratio (higher-the-better), it is inferred that the 17th
combination of process parameters are the best for maximum MRR.
110
Figure 5.7: Image of micro hole machined in 17th experiment
Parameters: Electrolyte Concentration : 0.5 mol/lit Voltage: 9 volts Current: 0.6 Amps Duty Cycle: 66.66% Frequency: 30 Hz MRR : 0.0012926 mm3/min. Dimensional Deviation : 30 microns
The Inconel 600 has been machined effectively with the
EC3V2C1DC3F1 combination of levels of process parameters. In this 17th
combination, the MRR obtained was the maximum at 0.0012926 mm3/min
with a dimensional deviation of 30 microns. The microscopic image of the hole
machined with 17th combination is shown in figure 5.7.
Figure 5.8: Image of micro hole machined in 6th experiment
111
Parameters: Electrolyte Concentration : 0.45 mol/lit Voltage: 10 volts Current: 1 Amps Duty Cycle: 33.33% Frequency: 30 Hz MRR : 0.0010680 mm3/min. Dimensional Deviation : 36 microns
The second best result has been produced in the 6th experiment with
a process parameter’s combination level of EC2V3C3DC1F1. The MRR
obtained, 0.0010680 mm3/min is the second best for the Inconel 600 with
a dimensional deviation of 36 microns. The microscopic image of the hole
machined with 6th combination is shown in figure 5.8.
Figure 5.9: Image of micro hole machined in 1st experiment
Parameters : Electrolyte Concentration : 0.4 mol/lit Voltage: 8 Volts Current: 0.6 Amps. Duty Cycle: 33.33% Frequency: 30 Hz MRR : 0.0003606 mm3/min Dimensional Deviation : 10 microns
The 1st combination of levels of process parameters EC1V1C1DC1F1
has resulted in the lowest MRR of 0.0003606 mm3/min. Although the
dimensional deviation was nominal with 10 microns, the machining time has
peaked. The microscopic image of the hole machined with 1st combination is
shown in figure 5.9.
112
5.4 ANALYSIS AND DISCUSSION OF RESULTS
In order to study the significance of the process parameters
towards MRR, analysis was done by Taguchi method using Minitab 15
software. The tables include ranks based on delta statistics, which compare the
relative magnitude of effects. The delta statistic is the highest minus the lowest
S/N Ratio for each factor. Minitab assigns ranks based on delta values; rank 1 to
the highest delta value, rank 2 to the second highest, and so on. The ranks
indicate the relative importance of each factor to the response. The mean value
of MRR and S/N ratio for each parameter at different levels were
calculated. The main effects of means of process parameter for S/N data were
plotted. The response curves have been used to examine the effects of process
parameter on the MRR.
Using ANOVA, Adjusted mean squares, F-Test Value, P-Test value
were calculated based on the S/N Ratio and Percentage of Contributions of each
process parameter on MRR has been arrived. The interaction between process
parameters on MRR has been plotted and analyzed.
5.5 CONFIRMATION TEST
The optimum level of process parameters has been determined by
using S/N ratio values (higher-the-better). Once the optimal level of the
process parameters has been selected, the final step is to predict and verify the
improvement of the performance characteristic using the optimal level of the
process parameters. The purpose of conformation test is to validate the
conclusions drawn from analysis of experimental results. The predicted or
estimated S/N ratio using optimal levels of process parameters can be
calculated with the following equation;
113
= m + i=1q ( i – m ) (5.1)
where m = total mean of S/N ratio,
q = no. of significant parameters,
i = mean of S/N ratio at optimum level
After predicting the response (S/N ratio), a confirmation
experiment has been designed and conducted with the optimal levels of the
machining parameters to verify the improvement of performance
characteristics.
5.5.1 Results and Discussion : Nickel
The means of MRR and Delta value are calculated using Taguchi
methodology. Based on the delta value, the process parameters are ranked for its
influence on MRR. Table 5.12 shows the means, delta value and the ranks of
process parameters for Nickel.
Table 5.12: Response table for Means - NICKEL
Level EC V C DC F1 0.003305 0.003134 0.002223 0.003921 0.005131 2 0.003944 0.004149 0.004176 0.004004 0.003643 3 0.004924 0.004889 0.005774 0.004248 0.003399
Delta 0.001619 0.001755 0.003550 0.000327 0.001732 Rank 4 2 1 5 3
114
The above tabulated mean values are plotted as figure 5.10 to
pictorially represent the contribution of each process parameter on MRR.
0.30.20.1
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0.0026.55.03.5 0.50.30.1 66.6650.0033.33 504030
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Main Effects Plot for MeansData Means
Figure 5.10: Main Effect Plot for Means - NICKEL
The main effect plot for means (Data Means) for Nickel shows that
the major contributor for maximum MRR is Machining Current, followed by
the Machining Voltage (table 5.12). The increase in the machining current
increases the current density at the machining zone. Hence, the high current
density supported with machining voltage has emerged as major contribution
on MRR for Nickel. The following Table 5.13 contains the S/N Ratio calculated
using Taguchi methodology.
115
Table 5.13: Response table for S/N Ratio - NICKEL
Level EC V C DC F1 – 50.95 – 50.72 – 53.64 – 49.69 – 46.93
2 – 49.01 – 49.25 – 48.14 – 49.63 – 49.69
3 – 47.10 – 47.08 – 45.28 – 47.74 – 50.44
The S/N Ratio is used to predict the optimal level of combination of
process parameter for maximum MRR. This predicted level is used to perform
confirmation experiment. The predicted combination is chosen as the maximum
S/N ratio of each parameter. Thus, a combination EC3V3C3DC3F1 has been
achieved for Nickel.
Table 5.14: Results of ANOVA - NICKEL
I II III IV V VI VII VIII
EC 2 45.847 45.847 22.924 7.12 0.021 12.68
V 2 41.763 41.763 20.882 6.48 0.026 11.55
C 2 213.219 213.219 106.609 33.1 0.0 58.98
DC 2 15.757 15.757 7.878 2.45 0.156 4.36
F 2 38.489 38.489 19.244 5.98 0.031 10.65
Error 7 22.544 22.544 3.221 1.78
Total 17 377.619 100.00
EC : Electrolyte Concentration (mol/lit) V : Voltage (volts) C : Current (amps) DC : Duty Cycle (%) F : Frequency (Hz) I : Parameters II : Degrees of Freedom III : Sequential Sum of Squares
116
IV : Adjusted Sum of Squares V : Adjusted mean squares VI : F-Test Value VII : P-Test Value VIII : Contributions %
It is clearly evident from the results of ANOVA (Table 5.14) for
Nickel that the machining current is the dominant factor affecting MRR with
58.98% contribution, which is supported by the frequency and voltage with
a contribution of 12.68% and 11.55% respectively. The graphical
representation of the same is given in figure 5.11.
Figure 5.11: Contribution of Process Parameters on MRR - Nickel
The normal probability plot of residuals shown in figure 5.12
confirms that the experimental results are distributed normally as it follows
a straight line without any outliers.
117
3210-1-2-3
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7060504030
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N orm al Probability Plot(response is SNRA 1)
Figure 5.12: Normal Probability Plot (S/N Ratio) - Nickel
0.009
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Interaction Plot for MRRData Means
Figure 5.13: Process Parameter Interaction Plot (MRR) - Nickel
The interaction plot (Figure 5.13) has been plotted to pictorially
depict the interactions process parameters on MRR. In the above full interaction
118
plot, two panels per pair of process parameters has been shown. The following
are the inference made from the Interaction Plot.
The major contributor, machining current shows interaction with all
other four process parameters.
The maximum MRR has been achieved when Electrolyte
concentration and Current is at 3rd level i.e. 0.3 mol/lit. and 0.5
amps respectively.
The 2nd major contributor for maximum MRR i.e. machining
voltage is at 2nd level (5.0 volts) and machining current at 3rd
level (0.5 amps) produced high MRR. However, when current
and voltage is at 3rd level, the MRR slightly came down.
The combination of 50% duty cycle and 0.5 amps machining
current yielded high MRR. But, when duty cycle and current at
their maximum, the MRR slightly decreased.
The frequency interacted with current and produced a linear
response i.e. when frequency at 30 Hz, it MRR proportionally
increased and reached its maximum with 0.5 amps machining
current.
It is clearly understood from the above interaction plot and the
inferences made, the maximum MRR is resulted only when the machining
current combines with other process parameter at its maximum value of 0.5
amps.
119
Table 5.15: Results of Confirmation Test - NICKEL
Initial Combination Prediction Experiment
Parameter Combination EC1V1C1DC1F1 EC3V3C3DC3F1 EC3V2C3DC2F1
MRR 0.001632 -- 0.009577
S/N Ratio – 55.7482 -- – 40.3754
Since, MRR is the higher the better type quality characteristic, it
can be seen from table 5.15 that for Nickel, the third level of Electrolyte
Concentration (EC3), second level of Machining Voltage (V2), third level of
Machining Current (C3), second level of Duty Cycle (DC2) and first level of
Frequency (F1) provides maximum value of MRR against the predicted
parameter combination of EC3V3C3DC3F1. The deviation between the
predicted and experimental value of S/N Ratio is 4.44%, in other words, the
confidence level of experiments conducted is 95.56%.
Figure 5.14: Image of micro hole machined for confirmation experiment
Parameters: Electrolyte Concentration : 0.3 mol/lit Voltage: 6.5 volts Current: 0.5 Amps Duty Cycle: 66.66% Frequency: 30 Hz MRR : 0.009999 mm3/min. Dimensional Deviation : 18 microns
120
Implementation of GA - Nickel
MRR = – 0.00018 + 0.00810 EC +0.000585 C + 0.00888 V + 0.000010 DC – 0.000087 F
MRR = @(x) – (0.00018 + (0.00810*EC)+(0.000585*C) + (0.00888*V) +(0.000010*DC)–(0.000087*F))
Parameters LevelsElectrolyte concentration (EC): 0.1 EC 0.3 Machining current (C): 0.1 C 0.3 Machining voltage (V): 3.5 V 6.5 Duty cycle (DC): 33.33 DC 66.66 Frequency (F): 30 F 50
Figure 5.15: Comparison between GA and EV for Nickel
The figure 5.15 shows the similarity between the genetically
optimized value (GA) and the experimental value (EV) of process parameters for
Nickel. It can be inferred from the chart that out of five process parameters, two
parameters matches exactly with the GA values. Three parameters i.e.
121
Machining Voltage (V), Machining Current (C), and the Duty Cycle (DC)
differs marginally.
Figure .5.16: Screen Shot of GA output for Nickel
The figure 5.16 shows the screen shot of Genetic Algorithm Tool
used to optimize the process parameters.
5.5.2 Results and Discussion : SDSS
The means of MRR and Delta value are calculated using Taguchi
methodology. Based on the delta value, the process parameters are ranked for its
influence on MRR and tabulated in table 5.16.
122
Table 5.16: Response table for Means - SDSS
Level EC V C DC F1 0.002063 0.003341 0.005770 0.001771 0.005576
2 0.003411 0.005518 0.002877 0.004526 0.003098
3 0.007181 0.003795 0.004008 0.006358 0.003982
Delta 0.005117 0.002177 0.002892 0.004587 0.002478
Rank 1 5 3 2 4
A main effect plot is given in figure 5.17 for the above tabulated
mean values to help easy inference of effects of process parameter on MRR.
0.500.450.40
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0.0011098 1.00.80.6 66.6650.0033.33 504030
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V C DC F
Main Effects Plot for MeansData Means
Figure 5.17: Main Effect Plot for Means - SDSS
123
The main effect plot for means for SDSS shows that the MRR is
greatly influenced by Electrolyte concentration. The second most influencing
process parameter is duty cycle. The greater the electrolyte concentration leads
to more number of ions dissolution and hence it is directly proportional to MRR.
Further, more the ON time will result in more time for electrolysis and hence
more MRR. Thus, these two parameters contribute for achieving maximum
MRR for SDSS.
Table 5.17: Response table for S/N Ratio - SDSS
Level EC V C DC F1 – 54.22 – 52.54 – 48.59 – 55.79 – 48.50
2 – 49.88 – 48.35 – 51.91 – 48.08 – 51.41
3 – 46.01 – 49.22 – 49.60 – 46.23 – 50.20
In order to predict the optimal level of combination of process
parameter for maximum MRR, the S/N Ratio is used by Taguchi methodology.
EC3V2C1DC3F1 has been chosen as the predicted combination process
parameters for SDSS (Table 5.17) based on the higher-the-better of S/N ratio.
The confirmation experiment has been conducted based on the predicted level.
The ANOVA results depicting the percentage contribution of the
process parameters are tabulated in table 5.18. In ECMM of SDSS, the duty
cycle plays the dominant role with 46.76% contribution on MRR. In this
experiment, the maximum MRR achieved with 66% duty cycle. Hence, it can
be inferred that less time is taken for maintaining the IEG and hence it is
possible to increase the duty cycle.
124
Table 5.18: Results of ANOVA - SDSS
Source DF Seq SS Adj SS Adj MS F P Cont. %
EC 2 202.48 202.48 101.24 6.7 0.024 30.66
V 2 58.7 58.7 29.35 1.94 0.213 8.89
C 2 34.62 34.62 17.31 1.15 0.371 5.24
DC 2 308.83 308.83 154.41 10.22 0.008 46.76
F 2 25.66 25.66 12.83 0.85 0.467 3.89
Error 7 105.73 105.73 15.1 4.56
Total 17 736.01 100.00
This characteristic of SDSS also gives room to increase rate of
dissolution by increasing the electrolyte concentration, which is confirmed by
its contribution of 30.66%. The machining voltage comes as third dominant
factor in affecting MRR of SDSS with 8.89%. The graphical representation of
contribution of process parameters on MRR is given in figure 5.18.
Figure 5.18: Contribution of Process Parameters on MRR - SDSS
125
Normal probability plot of residuals is used to analyze the normal
distribution of experimental results. Since, the probability plot for SDSS follows
a straight line without any outliers confirms the normal distribution of the
experimental results.
5.02.50.0-2.5-5.0-7.5
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Normal Probability Plot(response is SNRA1)
Figure 5.19: Normal Probability Plot (S/N Ratio) - SDSS
The figure 5.19 shows that the normal probability plot plotted for
SDSS showing the normal distribution of experimental results.
The interactions between the process parameters can be inferred
from the interaction plot. The full interaction plot (two panels per parameter)
given by Minitab 15 shows all possible interactions of process parameters.
Figure 5.20 shows the full interaction plot of SDSS.
126
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Interaction Plot for MRRData Means
Figure 5.20: Process Parameter Interaction Plot (MRR) - SDSS
The interactions between the Duty Cycle (major contributor) and
other four parameters are discussed.
The MRR for SDSS reached its maximum when the duty cycle is
at 66.66%, and Electrolyte Concentration is at 0.5 mol/lit. This
clearly shows that SDSS allows more dissolution with high
electrolyte concentration for longer period.
The machining voltage of 9 volts (2nd level) in combination with
66.66% duty cycle produced high MRR. However, 3rd level of
voltage i.e. 10 volts gave lesser amount of MRR.
The interaction between duty cycle and machining current
reveals that maximum dissolution has been reached when the
current is at 0.6 amps (1st level) and duty cycle at 66.66%.
The MRR has reached the maximum with 66.66% duty cycle and
30Hz frequency (1st level).
127
The above inferences made from the interaction plot of SDSS clearly
reveal that the Duty Cycle supports maximum dissolution when it is at 66.66%
irrespective of the levels of other parameters.
Table 5.19: Results of Confirmation Test - SDSS
Initial Combination Prediction Experiment
Parameter Combination EC1V1C1DC1F1 EC3V2C1DC3F1 EC3V2C1DC3F1
MRR 0.000909 -- 0.017066
S/N Ratio – 60.8266 -- – 35.3573
The predicted optimum combination of process parameters for
SDSS is EC3V2C1DC3F1. The confirmation experiment (table 5.19) reveals that
the optimum combination of process parameters for maximum MRR for SDSS
is third level of Electrolyte Concentration (EC3), second level of Machining
Voltage (V2), first level of Machining Current (C1), third level of Duty Cycle
(DC3) and first level of Frequency (F1), which is similar to the predicted
combination. The deviation between the predicted and experimental value
reveals a confidence level of 94.57% on the experimental results.
Implementation of GA - SDSS
MRR = – 0.0210 + 0.0512 EC + 0.000227 C – 0.00440 V + 0.000138 DC – 0.000080F
MRR = @(x) – (0.0210 + (0.0512*EC) + (0.000227*C) – 0.00440*V) + (0.000138*DC) – (0.000080*F))
128
Parameters LevelsElectrolyte concentration (EC): 0.4 EC 0.5 Machining current (C): 0.6 C 1.0Machining voltage (V): 8 V 10 Duty cycle (DC): 33.33 DC 66.66 Frequency (F): 30 F 50
Figure 5.21: Comparison between GA and EV for SDSS
The coherence between the genetically optimized value (GA) and the
experimental value (EV) of process parameters for SDSS has been plotted in
figure 5.21. It can be inferred from the chart that out of five process parameters,
the Machining Voltage (V), Machining Current (C), and Duty Cycle (DC)
matches exactly. The other two parameters i.e. Electrolyte Concentration (EC)
and Frequency (F) differ a little.
129
Figure 5.22: Screen Shot of GA output for SDSS
The figure 5.22 shows the screen shot of Genetic Algorithm Tool
used to optimize the process parameters.
5.5.3 Results and Discussion of Inconel 600
Using Taguchi methodology, the means of MRR and Delta value are
calculated. The process parameters are ranked for its influence on MRR based
on the delta value. The means, delta value and ranks of process parameters for
Inconel 600 are tabulated in table 5.20.
130
Table 5.20: Experimental Results - Inconel 600
Level EC V C DC F1 0.000719 0.000600 0.000735 0.000571 0.0007812 0.000786 0.000754 0.000655 0.000662 0.0006693 0.000634 0.000786 0.000749 0.000906 0.000690
Delta 0.000152 0.000186 0.000094 0.000336 0.000112Rank 3 2 5 1 4
0 .5 00 .4 50 .4 0
0 .0 0 0 9 5
0 .0 0 0 9 0
0 .0 0 0 8 5
0 .0 0 0 8 0
0 .0 0 0 7 5
0 .0 0 0 7 0
0 .0 0 0 6 5
0 .0 0 0 6 0
1 098 1 .00 .80 .6 6 6 .6 65 0 .0 03 3 .3 3 5 04 03 0
E C
Mea
nof
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V C D C F
M ain E ffec ts P lo t fo r M ean sData Means
Figure 5.23: Main Effect Plot for Means - Inconel 600
The data means plotted for Inconel 600 (Figure 5.23) shows that the
maximum MRR has been achieved due to the 3rd level duty cycle of 66.66%.
The second major influencing factor on MRR is machining voltage, followed by
Electrolyte Concentration and Frequency. Since the dissolution is directly related
to the pulse ON time and debris removal has been achieved in short span of time
(pulse OFF time) Duty Cycle emerged as a dominant factor affecting MRR for
Inconel 600.
131
The Signal to Noise Ratio calculated using Taguchi methodology for
Inconel 600 is tabled in table 5.21.
Table 5.21: Response table for S/N Ratio - Inconel 600
Level EC V C DC F1 – 63.29 – 64.89 – 63.28 – 65.56 – 63.05
2 – 62.23 – 63.01 – 64.14 – 63.71 – 63.71
3 – 64.88 – 62.50 – 62.98 – 61.04 – 63.64
The optimal level of combination of process parameter for
maximum MRR is predicted using the S/N Ratio. The combination is selected
based on “greater the S/N Ratio, higher the performance”. Hence, the
predicted combination is EC3V3C3DC3F1. This predicted combination of levels
of process parameters has been used to conduct the confirmation experiment.
The results of ANOVA (Table 5.22) reveals that the dominant factor
affecting the MRR for Inconel 600 is Duty Cycle with a contribution of 49.15%.
The contribution by electrolyte concentration .is the second major influencing
factor with 16.81%, closely followed by the machining voltage with 15.04%
contribution.
132
Table 5.22 Results of ANOVA - Inconel 600
Source DF Seq SS Adj SS Adj MS F P Cont. %EC 2 21.305 10.652 10.652 1.17 0.364 16.81
V 2 19.061 9.531 9.531 1.05 0.399 15.04
C 2 4.337 2.169 2.169 0.24 0.794 3.42
DC 25 62.28 31.14 31.14 3.43 0.092 49.15
F 2 1.56 0.78 0.78 0.09 0.919 1.23
Error 7 63.592 9.085 9.085 14.35
Total 17 172.135 100.00
The figure 5.24 shows the percentage of contribution of each
parameter towards the maximum MRR for Inconel 600.
Figure 5.24: Contribution of Process Parameters on MRR - Inconel 600
133
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Figure 5.25: Normal Probability Plot (S/N Ratio) - Inconel 600
The normal probability plot for Inconel 600, plotted with the
residuals is shown in figure 5.25. It reveals that the experimental results are
distributed normally.
The interactions made by the Duty Cycle (major contributor) with
other four parameters are discussed.
The MRR for Inconel 600 reached its maximum when the duty
cycle is at 66.66%, and Electrolyte Concentration is at 0.5 mol/lit.
This clearly shows that SDSS allows more dissolution with high
electrolyte concentration for longer period without creating any
short circuit at IEG.
The machining voltage of 9 volts (2nd level) in combination with
66.66% duty cycle produced high MRR. However, 3rd level of
voltage i.e. 10 volts gave lesser amount of MRR.
134
The interaction between duty cycle and machining current
reveals that maximum dissolution has been reached when the
current is at 0.6 amps (1st level) and duty cycle at 66.66%.
The MRR has reached the maximum with 66.66% duty cycle and
30Hz frequency (1st level).
0.00100
0.00075
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Interaction Plot for MRRData Means
Figure 5.26: Process Parameter Interaction Plot (MRR) - Inconel 600
The above inferences made from the interaction plot (Figure 5.26) of
Inconel 600 clearly reveals that the Duty Cycle supports maximum dissolution
when it is at 66.66% irrespective of the levels of other parameters.
135
Table 5.23: Results of Confirmation Test - Inconel 600
Initial Combination Prediction Experiment
Parameter Combination EC1V1C1DC1F1 EC2V3C3DC3F1 EC3V2C1DC3F1
MRR 0.000361 -- 0.001293
S/N Ratio – 48.8583 -- – 37.7707
The optimum combination of process parameters calculated
statistically for Inconel 600 is EC2V3C3DC3F1, which is very similar to the
experimental values. The optimum combination of process parameters for
maximum MRR obtained with confirmation test for Inconel 600 (Table 5.23)
is third level of Electrolyte Concentration (EC second level of Machining
Voltage (V2), first level of Machining Current (C1), third level of Duty Cycle
(DC3) and first level of Frequency (F1).
Figure 5.27: Image of micro hole machined for confirmation experiment
Parameters: Electrolyte Concentration : 0.45 mol/lit Voltage: 10.0 volts Current: 1.0 Amps Duty Cycle: 66.66% Frequency: 30 Hz MRR : 0.001306 mm3/min. Dimensional Deviation : 11 microns
136
Implementation of GA - Inconel 600
MRR = – 0.00094 – 0.0084 EC + 0.000929 C + 0.00036 V + 0.000101DC – 0.000045F
MRR= @(x) – (0.00094 – (0.0084*EC) + (0.000929*C) + (0.00036*V) + (0.000101*DC) – 0.000045*F))
Parameters LevelsElectrolyte concentration (EC): 0.4 EC 0.5 Machining current (C): 0.6 C 1.0Machining voltage (V): 8 V 10 Duty cycle (DC): 33.33 DC 66.66 Frequency (F): 30 F 50
Figure 5.28: Comparison between GA and EV for Inconel 600
137
The coherence between the genetically optimized value (GA) and the
experimental value (EV) of process parameters for Inconel 600 has been given in
figure 5.28. The only two parameters which differ very little with GA values are
Electrolyte Concentration (EC) and Frequency (F). The other three process
parameters viz. Machining Voltage (V), Machining Current (C) and Duty Cycle
(DC) matches exactly.
Figure 5.29: Screen Shot of GA output for Inconel 600
The figure 5.29 shows the screen shot of Genetic Algorithm Tool
used to optimize the process parameters.
138
5.6 DIMENSIONAL DEVIATION
In this research work, although the main emphasis is given for
studying effects on process parameters on MRR, the Dimensional Deviation
(DD) is also taken for analysis. A comparative study on the effects of process
parameters on MRR Vs. DD is made and the outcome is detailed hereunder.
5.6.1 Dimensional Deviation - Nickel
In order to easily compare the MRR and corresponding DD obtained
is plotted on logarithmic scale for Nickel as given in figure 5.30.
Figure 5.30: MRR Vs Dimensional Deviation - Nickel
139
In general, the DD is directly proportional to the MRR. However, it
can be inffered from the figure 5.30 that due to the effect of levels of various
process parameters, the relationship between MRR and DD is not linear.
The maximum DD reported in the 12th experiment. This may be due
to the peak machining voltage, moderate machining current and moderate duty
cycle. The higher machining voltage leads to micro sparks and thus greater DD.
The maximum MRR achieved in 8th combination gives a moderate DD of 20
microns. This is due to 2nd level of machining voltage and duty cycle combined
with 1st level of Frequency. Although the electrolyte concentration is higher
(3rd level), the DD obtained is at moderate level since all other process
parameters are at moderate level, particularly the machining voltage.
The 11th combination of process parameters (EC1V2C1DC1F2)
resulted in least MRR and DD. In this combination, the major contributors for
MRR i.e. machining current and electrolyte concentration are at minmum level,
backed by the 33.33% duty cycle resulted in least MRR as well as DD.
5.6.2 Dimensional Deviation - SDSS
The comparison made between the MRR and DD obtained for SDSS
is represented in figure 5.31.
The DD obtained for SDSS is ranged from 20 to 39 microns for
a MRR range of 0.0008991 to 0017066.
140
Figure 5.31: MRR Vs Dimensional Deviation - SDSS
The maximum DD is recorded in the 9th combination as 39 microns.
This can be attributed to the 3rd level of machining voltage, 3rd level of
electrolyte concentration and 3rd level of duty cycle. A 3rd best MRR is produced
at this combination.
The maximum MRR is reported in 17th combination with 3rd level of
electrolyte concentration and duty cycle combined with 2nd level machining
voltage. The moderate voltage combined with minimum frequency resulted in a
moderate DD of 30 microns.
The minimum DD and least MRR are achieved with the
1st combination (EC1V1C1DC1F1). In this combination, since all the process
parameters are at minimum level.
141
5.6.3 Dimensional Deviation - Inconel 600
The comparison between MRR and DD made for Inconel 600 reveals
the following inferences. The chart plotted to this effect is given as figure 5.32.
Figure 5.32: MRR Vs Dimensional Deviation - Inconel 600
The maximum DD of 36 microns is resulted in 6th combination due
to 3rd level of machining voltage, 3rd level of machining current and moderate
level of electrolyte concentration. This combination of process parameters has
produced the 2nd best MRR of 0.00107 mm3/min.
The minimum DD of 10 microns is achieved in 1st combination.
The MRR reported in this combination is also lowest i.e. 0.0003606 mm3/min.
since all the process parameters are at their minimum level.
142
It can be inferred from the above comparative analysis between
MRR and DD for Nickel, SDSS, and Inconel 600 that the machining voltage
plays a major role in controlling the DD in the ECMM process. The most
influencing factors on MRR, machining current, electrolyte concentration,
machining voltage and duty cycle also affects the DD in a similar fashion.
However, based on the material, the contribution of each process parameter on
DD varies. A detailed study in this respect will be highly useful to fine tune
the process parameters for maximum MRR and minimum DD.
143
CHAPTER 6
SUMMARY AND CONCLUSIONS
6.1 SUMMARY
The experimental studies already made in the field of
electrochemical micro machining reveals the great potential of this method of
precision machining. However, it is learnt from the literature survey that many
researches were done involving only a few input / output process parameters
at a time. Further, the ECMM process is to be optimized specifically for each
material considering the MRR, dimensional deviation and cost.
Hence, this study is conducted on Nickel based alloys with five
processing parameters viz. Electrolyte Concentration(EC), Machining
Voltage(V), Machining Current(C), Duty Cycle(DC), and Frequency(F). In
order to achieve this objective, an experimental setup is designed and
fabricated consists of a) Work holding platform, b) Tool feeding device,
c) Control system, d) Electrolyte flow system, and e) Power supply system.
Preliminary experiments are conducted (one factor at a time
approach) to identify the levels of process parameters. To study the entire
spectrum of levels of process parameters with least number of experiments,
Taguchi Design methodology is used with L18 orthogonal array. The levels of
process parameters for Nickel are chosen as: electrolyte concentration of 0.1,
0.2 and 0.3 mol/lit, machining voltage of 3.5, 5.0, and 6.5 volts, machining
144
current of 0.1, 0.3, and 0.5 amps. For SDSS and Inconel 600 an electrolyte
concentration of 0.4, 0.45, and 0.5 mol/lit, machining voltage of 8, 9, and 10
volts, machining current of 0.6, 0.8, and 1.0 amps., are chosen as levels of
process parameters. Besides, the levels of duty cycle and frequency are kept
similar for Nickel, SDSS and Inconel 600 as 33.33, 50.00, 66.66% and 30, 40,
50 Hz. respectively.
The statistical analysis of variance (ANOVA) technique is used to
determine the contribution of each parameter towards maximum MRR. Based
on the ANOVA results, the confirmation experiments are conducted to ensure
the coherence of the experimental results with predicted values. Genetic
Algorithms are used to identify the optimized level of process parameters and
the same compared with the experimental results.
6.2 CONCLUSIONS
It is evident from this research work that the dominant process
parameter which affects MRR varies based on the Nickel content in the Nickel
Alloy. The 100% pure Nickel has shown high rate of dissolution for the higher
machining current (C). The Inconel 600 alloy, which has 72% Nickel content,
the duty cycle (DC) contributed for maximum MRR while machining current
become less significant. Further, the duty cycle (DC) was the major parameter
affecting the MRR of the SDSS alloy which has only 5 - 6% of Nickel content.
Hence, it can be inferred that higher the Nickel content, the machining current
is more significant factor affecting MRR and DD.
145
The following are the conclusions derived from the obtained results
for Nickel, SDSS, and Inconel 600. Further, the optimum combination of
process parameters to achieve maximum MRR obtained from this research is
also furnished.
6.2.1 Conclusion on ECMM of Nickel
o It is found from the ANOVA results that machining current,
electrolyte concentration and machining voltage have
significant effect on MRR. The predicted combination of
process parameter for maximum MRR is EC3V3C3DC3F1.
o The optimum combination of levels of process parameter for
maximum MRR is achieved from the 8th combination i.e.
EC3V2C3DC2F1. The maximum MRR obtained is 0.009577
mm3/min.
o A 95.56% of confidence level of experiments conducted is
achieved. Based on the S/N Ratio, an improvement of 27.5%
is achieved.
The predicted combination obtained from ANOVA has been
compared with the optimum combination obtained from GA (EC3V3C2DC3F1)
and found that both are matching exactly for all parameters except machining
current. It is inferred from the analysis of experimental results, for nickel, the
machining current is the dominating factor affecting MRR. The confirmation
experiments conducted using the combination obtained from ANOVA also
revealed the same with a maximum MRR of 0.009999 mm3/min.
146
Hence, the combination selected by ANOVA with 3rd level of
machining current i.e. EC3V3C3DC3F1 is recommended as the optimum level
of process parameter for Nickel.
6.2.2 Conclusion on ECMM of SDSS
o ANOVA results for SDSS revels that duty cycle, electrolyte
concentration and machining voltage have significant effect
on MRR. The predicted combination of process parameter for
maximum MRR is EC3V2C1DC3F1.
o The optimum combination of levels of process parameter for
maximum MRR is achieved from the 17th combination i.e.
EC3V2C1DC3F1. The maximum MRR obtained is 0.017066
mm3/min.
o A 94.57% of confidence level of experiments conducted is
achieved. Based on the S/N Ratio, an improvement of 41.8%
is achieved.
The predicted combination obtained from ANOVA has been
compared with the optimum combination obtained from GA (EC1V3C1DC3F2)
and found that both are matching for the duty cycle and the machining current.
Since the combination of 17th experiment which resulted in maximum MRR
exactly matches with the combination obtained from ANOVA with an MRR
of 0.017066 mm3/min.
Hence, the combination selected by ANOVA with EC3V3C3DC3F1
is recommended as the optimum level of process parameter for SDSS.
147
6.2.3 Conclusion on ECMM of Inconel 600
o Results obtained from ANOVA for Inconel 600 exhibits that
duty cycle, electrolyte concentration, and machining voltage
have significant effect on MRR. The predicted combination of
process parameter for maximum MRR is EC2V3C3DC3F1.
o The optimum combination of levels of process parameter for
maximum MRR is achieved from the 17th combination i.e.
EC3V2C1DC3F1. The maximum MRR obtained is 0.001293
mm3/min.
o Based on the S/N Ratio, an improvement of 22.6% is achieved.
The predicted combination obtained from ANOVA has been
compared with the optimum combination obtained from GA (EC2V3C2DC3F2)
and found that both are matching for the duty cycle, electrolyte concentration
and the machining voltage. It is inferred from the analysis of experimental
results, for Inconel 600, the duty cycle is the dominating factor affecting
MRR. The confirmation experiments conducted using the combination
obtained from ANOVA also revealed the same with a maximum MRR of
0.001293 mm3/min.
Hence, the combination selected by ANOVA with a combination
EC2V3C3DC3F1 yielded a maximum MRR of 0.001306 mm3/min is
recommended as the optimum level of process parameter for Inconel 600.
148
6.3 SUGGESTIONS FOR FUTURE WORK
This thorough investigation to optimize the ECMM machining
parameters for Nickel and its alloys paves way for further studies. The
following suggestions may prove useful for future research work:
1. The effects of machining parameters on dimensional deviation may be investigated with variation in electrolyte flow rate, dynamic IEG, tool feed, tool shape, and tool vibrating frequency.
2. The significance to be assigned to MRR, dimensional deviation, surface roughness in multi objective optimization models to meet the growing requirements.
3. Development of revolving micro-helical tool with tool vibration to improve accuracy and MRR.
4. Further research is required to device robust methods and equipments to monitor the inter electrode gap.
5. More research is recommended to accurately monitor the purity, temperature and velocity of electrolyte at IEG.
6. Efforts may be made to investigate the effects of the ECMM process parameters on performance measures in a Cryogenic environment.
7. More research is to be done to prevent over voltage and optimize the total energy used for ECMM.
8. Further research is to be done to study the possibility of gang drilling for high quality mass production.
9. Further research is recommended to exploit the ECMM capabilities in machining Composite materials and Powder metallurgical components.
149
APPENDIX 1
Table A 1.1: Experimental Results for MRR - SDSS
Exp. No.Machining
Time in Trial 1
Machining Time in Trial 2
MRRTrial 1
MRRTrial 2
Average MRR
1 19.00 25.00 0.000799 0.001020 0.000909
2 23.20 18.80 0.002289 0.001845 0.002067
3 19.70 28.30 0.002342 0.002882 0.002612
4 8.60 11.80 0.002844 0.002311 0.002577
5 14.80 11.20 0.004133 0.004987 0.004560
6 13.50 15.50 0.002304 0.002836 0.002570
7 22.70 29.30 0.000790 0.001009 0.000899
8 9.00 13.00 0.006241 0.005214 0.005728
9 8.20 10.80 0.006900 0.008181 0.007540
10 12.65 9.65 0.001825 0.002265 0.002045
11 29.40 19.20 0.001684 0.002096 0.001890
12 11.20 14.80 0.003148 0.002567 0.002858
13 11.55 14.75 0.004733 0.003915 0.004324
14 23.00 17.60 0.001602 0.001997 0.001799
15 10.40 14.00 0.004201 0.005067 0.004634
16 9.95 12.35 0.010049 0.008538 0.009294
17 9.10 7.90 0.018273 0.015859 0.017066
18 12.00 16.00 0.002292 0.002822 0.002557
150
Table A 1.2: Experimental Results for MRR - Inconel 600
Exp. No.Machining
Time in Trial 1
Machining Time in Trial 2
MRRTrial 1
MRRTrial 2
Average MRR
1 30.5 34.5 0.0003110 0.0004100 0.0003607
2 24.1 22.9 0.0007271 0.0009320 0.0008293
3 16.2 13.8 0.0011595 0.0009121 0.0010358
4 28.2 31.8 0.0005787 0.0007470 0.0006630
5 24.7 19.3 0.0008684 0.0006761 0.0007723
6 17.4 20.6 0.0011949 0.0009411 0.0010680
7 33.3 29.7 0.0004177 0.0003168 0.0003672
8 31.1 20.9 0.0003497 0.0004600 0.0004046
9 18.5 21.5 0.0008250 0.0006412 0.0007331
10 20.7 23.3 0.0008591 0.0006686 0.0007639
11 31 27 0.0006831 0.0005273 0.0006052
12 28.3 21.7 0.0006270 0.0008070 0.0007172
13 19 21 0.0009436 0.0007369 0.0008402
14 27.2 30.8 0.0006977 0.0005389 0.0006183
15 25.1 21.9 0.0008492 0.0006607 0.0007549
16 31.8 23.2 0.0006822 0.0005266 0.0006044
17 10.8 15.2 0.0011433 0.0014420 0.0012926
18 23 29 0.0003497 0.0004600 0.0004046
151
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157
CURRICULUM VITAE
D. SARAVANAN
Serving as Principal for Sri Ramakrishna College of Engineering,
Perambalur. Served in various Institutions: 1) Professor Mechanical Engg., for
Sri Ranganathar Institute of Engineering and Technology Coimbatore
(June 2011-June 2012). 2) Professor Mechanical Engg., for Jayaram College
of Engineering and Technology, Thuraiyur (August 2010-May 2011). 3)
Principal for MIT, Musiri (June 2009-August 2010). 4) Prof. & Head, Roever
Engineering College, Perambalur. (June 2001-May 2009). 5) Head of
Department Mechanical Engg. at Roever Polytechnic College, Perambalur.
(June 1986-June 2001) and also served as Vice-Principal.
Have completed Professional Degree in Mechanical Engineering
(B.E.) at Regional Engineering College (Presently NIT), Trichy and Post
Graduate Degree- M.E., Thermal Plant Engg at Shanmuga College of
Engineering (Presently SASTRA), Thanjavur.
Published two Papers in International Journals, two papers in
International Conference, Organized one FDP and attended three FDPs,
Organized one National Conference and attended one National Conference,
Attended two International Conferences.