Investigation of Variables Affecting Kerf Width Surface ...

Investigation of Variables Affecting Kerf Width Surface Roughness and Material Removal Rate in Wire Electrical

Discharge Machining ____________________________________________________

Ph.D. Dissertation (Session 2006)

Submitted By

Mr. Aqueel Shah 2006-Ph.D-MNF-05

Supervised By Prof. Dr. Nadeem Ahmad Mufti

Department of Industrial and Manufacturing Engineering University of Engineering and Technology

Lahore-Pakistan 2012

Department of Industrial and Manufacturing Engineering

I

Investigation of Variables Affecting Kerf Width Surface Roughness and Material Removal Rate in

Wire Electrical Discharge Machining

Ph.D. Dissertation (Session 2006)

Submitted By

Mr. Aqueel Shah

2006-Ph.D-MNF-05

Supervised By

Prof. Dr. Nadeem Ahmad Mufti

___________________________________________________________________

Department of Industrial and Manufacturing Engineering University of Engineering and Technology

Lahore-Pakistan 2012


II

Dedication

To my deceased Mother Mrs. Jannat Shah

To my family


III

Investigation of Variables Affecting Kerf Width Surface Roughness and Material Removal Rate in

Wire Electrical Discharge Machining

Mr. Aqueel Shah 2006-Ph.D-MNF-05

Supervisor

Prof. Dr. Nadeem Ahmad Mufti

A dissertation submitted for the degree of Doctor of Philosophy

in Manufacturing Engineering

Internal Examiner External Examiner

Dr. Nadeem Ahmad Mufti Dr. Syed Amir Iqbal Department of Industrial & Department of Industrial & Manufacturing Engg., Manufacturing Engg., NED University of Engineering & Technology, University of Engineering & Lahore Technology, Karachi

Chairman Dean Department of Industrial & Manufacturing Faculty of Mechanical Engg, University of Engineering &Technology, Engineering, University of Lahore Engg. & Technology, Lahore


IV

Names of Ph.D. Thesis External Examiners

From within the Country

Dr. Syed Amir Iqbal, Professor Chairman, Department of Industrial and Manufacturing Engineering, NED University of Engineering and Technology, Karachi, Pakistan.

From Abroad 1 Dr. Xun Chen, Professor,

Professor of Manufacturing General Engineering Research Institute Liverpool John Moores University Liverpool L3 3AF, UK

2 Dr. Asif Iqbal, Assistant Professor,

ME122, Department of Mechanical Engineering, Eastern Mediterranean University, Gazimagusa, Turkish Republic of North Cyprus, Via Mersin 10, TURKEY

3 Dr. Ye Li, Assistant Professor

Office: Morgan Hall Room 109B, Department: Industrial & Manufacturing Engineering & Technology, Bradley University, 1501 West Bradley Ave., Peoria, IL 61625, USA.


V

Declaration

None of the material contained in this thesis has been submitted in support of an

application for another degree or qualification of this or any other university or the

institution of learning.


VI

Acknowledgements The author is indebted to The Department of Industrial and Manufacturing

Engineering University of Engineering (UET), Lahore, Pakistan, The Higher

Education Commission (HEC) of Pakistan, and Pakistan Navy for having made this

research possible.

Aqueel Shah


VII

Abstract

Wire-Electrical Discharge Machining (WEDM) is one of the non-conventional

machining processes for machining hard to machine electrically conductive materials.

It has been increasingly used in industry owing to its distinct advantages over the

other cutting technologies. The process can only be employed effectively when all its

properties and complexities are well understood. In addition many aspects of this

technology require to be fully explored in order to increase its capabilities and cutting

performance. This thesis contains an extensive literature review and an experimental

work on the investigations of various variables in Wire-EDM. It is a fact that the

substantial amount of work has been carried out on Wire-EDM, but a very little

research has been reported on the influence of the variables such as the work piece

thickness and hardness on various machining responses such as surface roughness,

kerf width and material removal rate. Accordingly a detailed experimental

investigation is presented in this thesis to study the various cutting performance

measures in Wire-EDM over a wide range of variables or process parameters

including workpiece thickness and hardness. The influence of all these variables/

control factors/ process parameters on the major cutting performance measures in

Wire-EDM have been comprehensively discussed and analyzed under two sets of

experiments.

In the first set of experiments, the influence of eight variables including thickness has

been studied on the machining responses such as kerf width, surface roughness, and


VIII

material removal rate. The workpiece material used was Tungsten Carbide. Eight

variables including thickness have been taken with three levels each to determine

their influence on the machining responses. In this the Taguchi Orthogonal Array has

been used to reduce the number of runs for meaningful results. Tungsten Carbide

workpieces were machined and the requisite response variables were measured.

Likewise, in the second set of experiments the same material was taken and hardened

to obtain two levels of hardness. The workpiece hardness was taken instead of

thickness with four other variables having two levels each. This was done to validate

the results of first experiment and also to see the influence of hardness. In both the

experiments, ANOVA was carried out after obtaining the responses to determine the

significant factors for each response. The result was consistent with the available

literature however new facts were discovered in the case of workpiece thickness and

hardness. Workpiece thickness appeared to be significant in case of surface roughness

only and hardness was found significant in all the three cases. Finally the

optimization of the machining responses was carried out using S/N ratio as specified

by Taguchi method for the purpose of research papers publications.

Key words: Taguchi method; kerf; surface roughness; S/N ratio; ANOVA;

optimization


IX

List of Publications

o Aqueel Shah, Nadeem A. Mufti, Dinesh Rakwal, and Eberhard Bamberg,” Material Removal Rate, Kerf, and Surface Roughness of Tungsten Carbide Machined with Wire Electrical Discharge Machining”, Journal of Materials Engineering and Performance, 2011, Volume 20, Number 1, Pages 71-76.

o Aqueel Shah, Nadeem A. Mufti, “Influence of Machine Control Variables and

Material Hardness on machining responses in Wire Electrical Discharge Machining”. The paper is under review in International Journal of Advance Manufacturing Technology for publication.

o Aqueel Shah, Nadeem A. Mufti, “Multiple Response Optimization of Process

Parameters in Wire Electrical Discharge Machining of Tungsten Carbide Using Various Optimization Techniques”. The paper is being reviewed for publication.


X

Table of contents

Dedication ........................................................................................................................... II

Declaration .......................................................................................................................... V

Acknowledgement ............................................................................................................ VI

Abstract ............................................................................................................................ VII

List of Publications ........................................................................................................... IX

Contents .............................................................................................................................. X

List of Figures ............................................................................................................... XIV

List of Tables ................................................................................................................... XV

CHAPTER 1

INTRODUCTION TO WIRE-EDM ................................................................................... 1

1.1 COMPARISON WITH OTHER NON CONVENTIONAL MANUFACTURING PROCESSES ................................................................................ 1

1.1.1 PHYSICAL PARAMETERS ........................................................................ 2

1.1.2 SHAPING CAPABILITY ............................................................................. 4

1.1.3 APPLICABILITY TO VARIOUS MATERIALS ......................................... 4

1.1.4 THE MACHINING CHARACTERISTICS .................................................. 5

1.1.5 THE ECONOMICS OF THE PROCESS ...................................................... 6

1.2 WIRE EDM APPLICATIONS ............................................................................. 7

1.2.1 MODERN TOOLING APPLICATIONS ...................................................... 7

1.2.2 ADVANCED CERAMIC MATERIALS ...................................................... 8

1.2.3 MODERN COMPOSITE MATERIALS ....................................................... 9


XI

1.3 THESIS OVERVIEW ......................................................................................... 10

CHAPTER 2

LITERATURE REVIEW .................................................................................................. 12

2.1 INTRODUCTION ............................................................................................... 12

2.2 WIRE-EDM ........................................................................................................ 13

2.3 MAJOR AREAS OF WIRE EDM RESEARCH ................................................ 15

2.3.1 OPTIMIZATION OF WIRE-EDM PROCESS ........................................... 16

2.3.1.1 PROCESS PARAMETERS DESIGN ..................................................... 16

2.3.1.2 PROCESS MODELING .......................................................................... 22

2.3.2 PROCESS MONITORING AND CONTROL OF WIRE-EDM ................ 23

2.3.2.1 FUZZY CONTROL SYSTEM ................................................................ 23

2.3.2.2 WIRE-INACCURACY ADAPTIVE CONTROL SYSTEMS ................ 25

2.3.2.3 SELF-TUNING ADAPTIVE CONTROL SYSTEMS ............................ 29

2.4 GAP ANALYSIS AND PROBLEM STATEMENT .......................................... 30

CHAPTER 3

DESIGN OF EXPERIMENTS .......................................................................................... 31

3.1 TAGUCHI METHOD ......................................................................................... 32

3.1.1 PHILOSOPHY OF THE TAGUCHI METHOD ......................................... 33

3.1.2 TAGUCHI METHOD DESIGN OF EXPERIMENTS ............................... 33

3.1.2.1 TAGUCHI LOSS FUNCTION ................................................................ 34

3.1.2.2 DETERMINING PARAMETER DESIGN ORTHOGONAL ARRAY .................................................................................................................. 35

3.1.2.3 ANALYSIS OF THE EXPERIMENTAL DATA .................................. 36

3.2 ADVANTAGES AND DISADVANTAGES OF TAGUCHI METHOD .......... 38

3.3 RECENT RESEARCH WORK INVOLVING TAGUCHI METHOD .............. 40

CHAPTER 4

EXPERIMENTATION, STATISTICAL ANALYSES AND OPTIMIZATION FOR WORKPIECE THICKNESS ............................................................................................. 43

4.1 INTRODUCTION ............................................................................................... 43

4.2 EXPERIMENTATION ....................................................................................... 45


XII

4.2.1 DESIGN OF EXPERIMENT ...................................................................... 45

4.2.2 EXPERIMENTAL SETUP .......................................................................... 47

4.2.3 DATA COLLECTION ............................................................................. 50

4.2.4 RESPONSE VARIABLES ...................................................................... 50

4.2.4.1 KERF WIDTH (KF) ................................................................................ 51

4.2.4.2 SURFACE ROUGHNESS (Ra) .................................................................. 55

4.2.4.3 MATERIAL REMOVAL RATE (MRR) ................................................... 61

4.3 RESULTS AND ANALYSES ............................................................................ 62

4.3.1 ANALYSIS OF SIGNAL TO NOISE RATIO ............................................ 63

4.3.1.1 KERF WIDTH (KF) ................................................................................... 65

4.3.1.2 SURFACE ROUGHNESS (Ra) .................................................................. 66

4.3.1.3 MATERIAL REMOVAL RATE (MRR) ................................................... 68

4.3.2 ANOVA FOR MATERIAL REMOVAL RATE (MRR), KERF WIDTH (KF), AND SURFACE ROUGHNESS (Ra) ................................................ 69

4.4 DISCUSSION ..................................................................................................... 71

4.4.1 KERF WIDTH (KF) .................................................................................... 71

4.4.2 SURFACE ROUGHNESS (Ra) ................................................................... 72

4.4.3 MATERIAL REMOVAL RATE (MRR) .................................................... 74

4.5 OPTIMIZATION OF RESPONSE VARIABLES .............................................. 75

4.5.1 KERF WIDTH (KF) .................................................................................... 75



4.6 RELATIONS AND MATHEMATICAL MODELS FOR APPROXIMATION ....................................................................................................... 78

4.6.1 KERF WIDTH (KF) .................................................................................... 78

4.6.2 SURFACE ROUGHNESS (Ra) .................................................................. 81


CHAPTER 5

EXPERIMENTATION FOR WORKPIECE HARDNESS .............................................. 85

5.1 INTRODUCTION ............................................................................................... 86


XIII

5.1 EXPERIMENTATION ....................................................................................... 87

5.2.1 DESIGN OF EXPERIMENT ...................................................................... 87

5.2.2 EXPERIMENTAL SETUP AND DATA ACQUISITION ......................... 88

5.3 STATISTICAL ANALYSES .............................................................................. 91

5.3.1 ANALYSIS OF SIGNAL TO NOISE RATIO ............................................ 91

5.3.2 ANOVA FOR KF, Ra and MRR .................................................................. 96

5.4 DISCUSSION ..................................................................................................... 97

5.4.1 KERF WIDTH (KF) .................................................................................... 98



5.5 OPTIMIZATION OF MACHINING RESPONSES ........................................ 100

5.5.1 KERF WIDTH (KF) .................................................................................. 100

5.5.2 SURFACE ROUGHNESS (Ra) ................................................................. 101

5.4.3 MATERIAL REMOVAL RATE (MRR) .................................................. 101

CHAPTER 6

CONCLUSION AND RECOMMENDATIONS ............................................................ 103

6.1 CONCLUSION ................................................................................................. 103

6.2 RECOMMENDATIONS .................................................................................. 105

References ........................................................................................................................ 106


XIV

List of Figures

Figure 1.1: Relationship between the rate of metal removal and power consumption ..... 3

Figure 3.1: Steps involved in the Taguchi Method ......................................................... 34

Figure 4.1: Wire-EDM machine G43S .......................................................................... 47

Figure 4.2: Samples preparation .................................................................................... 48

Figure 4.3: Samples of 1, 2, and 3 inches thickness ...................................................... 48

Figure 4.4: Clamping of workpiece ................................................................................ 49

Figure 4.5: Photomicrographs of the cut workpieces ..................................................... 50

Figure 4.6: Schematic presentation of Kerf Width ........................................................ 51

Figure 4.7: Mean S/N ratio graph for KF ...................................................................... 66

Figure 4.8: Mean S/N ratio graph for Ra ....................................................................... 67

Figure 4.9: Mean S/N ratio graph for MRR .................................................................. 68

Figure 4.10: Relationship between OV and KF ................................................................ 79

Figure 4.11: Relationship between ONT and KF ............................................................. 79

Figure 4.12: Relationship between OFT and KF ............................................................. 80

Figure 4.13: Relationship between WT and KF .............................................................. 80

Figure 4.14: Relationship between TH and Ra ................................................................ 81

Figure 4.15: Relationship between OV and Ra ................................................................ 81

Figure 4.16: Relationship between ONT and Ra .............................................................. 82

Figure 4.17: Relationship between TH and MRR ............................................................ 82

Figure 4.18: Relationship between ONT and MRR ........................................................ 83

Figure 4.19: Relationship between OFT and MRR ......................................................... 83

Figure 4.20: Relationship between SV and MRR ........................................................... 84

Figure 5.1: Mean S/N ratio graph for KF ........................................................................ 94


XV

Figure 5.2: Mean S/N ratio graph for Ra ......................................................................... 95

Figure 5.3: Mean S/N ratio graph for MRR .................................................................... 95


XVI

List of Tables

Table 1.1: Comparison of the Physical Parameters .......................................................... 3

Table 1.2: Shape application of Non-Conventional Processes ......................................... 4

Table 1.3: Metals and Alloys ............................................................................................ 5

Table 1.4: Cutting Performance for Non-Metals .............................................................. 5

Table 1.5: Comparison of Machining Characteristics among Various Processes ............ 6

Table 1.6: The Economics of the process ......................................................................... 7

Table 3.1: Taguchi L9 Orthogonal array ........................................................................ 36

Table 3.2: Calculating Mean S/N Ratio .......................................................................... 38

Table 3.3: Mean S/N Ratio ............................................................................................. 38

Table 4.1: Data Summery of Experiments ...................................................................... 45

Table 4.2: Experimental Runs Specified by Taguchi L27(3x13)) Orthogonal Array ...... 46

Table 4.3a: Measurement of KERF WIDTH (KF) ........................................................... 52

Table 4.3b: Kerf width measured at top ........................................................................... 53

Table 4.3c: Kerf width measured at side .......................................................................... 54

Table 4.3d: Kerf width measured at bottom ..................................................................... 55

Table 4.4a: Measurement of Surface Finish (Ra) ............................................................. 56

Table 4.4b: Ra measured at top ...................................................................................... 58

Table 4.4c: Ra measured at center .................................................................................. 59

Table 4.4d: Ra measured at bottom .................................................................................. 60

Table 4.5: Measurement of Material Removal Rate (MRR) ......................................... 61

Table 4.6: Calculated S/N ratio ...................................................................................... 64

Table 4.7: S/N response table for Kerf Width (KF) ....................................................... 65

Table 4.8: S/N response table for Surface Roughness (Ra) ........................................... 67

Table 4.9: S/N response table for Material Removal Rate (MRR) ................................ 68

Table 4.10: Results of ANOVA-Kerf Width (KF) .......................................................... 69

Table 4.11: Results of ANOVA-Surface Roughness (Ra) ................................................ 70

Table 4.12: Results of ANOVA-Material Removal Rate (MRR) ..................................... 70


XVII

Table 4.13: Results of conformation experiment for Kerf Width (KF) ........................... 76

Table 4.14: Results of conformation experiment for Surface Roughness (Ra) ................. 77

Table 4.15: Results of conformation experiment for Material Removal Rate (MRR) ..... 78

Table 5.1: Data Summery of Experiments ...................................................................... 87

Table 5.2: Experimental Runs Specified by Taguchi L32(2x31) Orthogonal Array ....... 89

Table 5.3: Specifications of workpiece material ............................................................. 90

Table 5.4: Calculated S/N ratio ....................................................................................... 92

Table 5.5: S/N response table for Kerf Width (KF) ........................................................ 93

Table 5.6: S/N response table for Surface Roughness (Ra) ............................................ 93

Table 5.7: S/N response table for Material Removal Rate (MRR) ................................. 94

Table 5.8: Results of ANOVA-Kerf Width (KF) ............................................................ 96

Table 5.9: Results of ANOVA-Surface Roughness (Ra) .................................................. 96

Table 5.10: Results of ANOVA-Material Removal Rate (MRR) ..................................... 97

Table 5.11: Results of conformation experiment for Kerf Width (KF) .......................... 101

Table 5.12: Results of conformation experiment for Surface Roughness (Ra) ............... 101

Table 5.13: Results of conformation experiment for Material Removal Rate (MRR) ... 102

1

CHAPTER1INTRODUCTIONTOWIRE‐EDM

Electrical discharge machining (EDM) is one of the earliest non-traditional machining processes.

EDM process is based on thermoelectric energy between the work piece and an electrode [1]. A

pulse discharge occurs in a small gap between the work piece and the electrode and removes the

unwanted material from the parent metal through melting and vaporizing. The electrode and the

workpiece must have electrical conductivity in order to generate the spark. There are various

types of products which can be produced using EDM such as dies and moulds. Parts of

aerospace, automotive industry and surgical components can be finished by EDM.

This thesis is primarily concerned with presenting the research that has been carried out on Wire-

EDM machine; however, providing an understanding of the capabilities of this machining

process is also very necessary. Such an effort becomes more viable when wire-EDM is compared

to other similar non-conventional machining processes. Therefore in this chapter a comparative

analysis on the basis of different technical aspects is presented. In addition to the comparison, the

applications of the process have also been discussed in detail to have an overview of the process.

This chapter gives an idea of the feasibility, versatility and applicability of the process.

1.1 COMPARISON WITH OTHER NON CONVENTIONAL MANUFACTURING

PROCESSES

There is a wide range of non-conventional machining processes present in today’s age. The

researchers have proposed different means and techniques for selecting them for a particular

2

application. In most of the cases [2-5], Chemical machining (CHM), Ultrasonic machining

(USM), Electrochemical machining (ECM), Abrasive jet machining (AJM), Electric discharge

machining (EDM), Laser beam machining (LBM), Electron beam machining (EBM), Plasma arc

machining (PAM), Water jet machining (WJM) and Wire electric discharge machining (W-

EDM) have been considered. For the selection of appropriate process, the techniques such as

QFD-based expert system, analytic network process and data envelopment analysis (DEA) were

used. Some other researchers [7-10] have used hybrid wire EDM process to enhance the

capabilities of the process for a specific application such as (CWEDT) cylindrical wire-electrical

discharge turning.

A specific manufacturing process that proves its suitability under various given conditions may

not necessarily be equally good under other conditions [11]. Therefore, extreme care must be

taken while selecting a process for a given manufacturing task. The analysis can be made from

the following technical point of views:

a. The physical parameters involved

b. Capability in machining various different shapes

c. Applicability of various processes to different types of materials

d. The operational characteristics of the manufacturing

e. Economics involved.

1.1.1 PHYSICAL PARAMETERS

The physical parameters of the non-conventional machining processes directly affect the rate of

the material removal and energy consumed in different processes as given in table 1.1

3

The relation between the rate of Metal removal and power consumed by various non-

conventional machining processes is shown by the figure 1.1

Figure 1.1: Relationship between rate of metal removal and the power consumption [11]

It can be seen that some of the processes like AJM, EBM and ECM are above the mean power

consumption line and consume greater amount of power than the other processes that are below

the mean power consumption line. Hence, the running cost involved in those processes that are

Table 1.1:- Comparison of the Physical Parameters [11] Parameters EDM EBM LBM PAM USM AJM ECM CHM Potential (V)

45 150000 4500 100 220 220 10 -

Current (Amp)

50 (Pulsed

DC)

0.001 (Pulsed

DC)

2.0 500 (DC)

12 (AC)

1.0 10000 (DC)

-

Power (W) 2700 150 - 50000 2400 220 100000 - Gap (m.m.) 0.025 100 150 7.5 0.25 0.75 0.2 - Medium Liquid di-

electric Vacuum Air Argon or

Hydrogen Abrasive In water

Abrasive In gas

Electrolyte

Liquid Chemical

4

lying above the mean is high on the other hand for the processes below that line is comparatively

lesser.

1.1.2 SHAPING CAPABILITY

The shaping capability of the different processes can be judged on the basis of different

machining operations that they can perform. This includes drilling, micro-drilling, cavity

sinking, shallow and deep pocketing, contouring and through cutting etc. Comparison is shown

in table 1.2. Laser beam machining is the only process that has enough capability to micro drill

and for the drilling of shapes having slenderness ratio less than 20, USM, ECM and EDM

processes will be more suitable. EDM and ECM processes have a better capability of pocketing

operation. ECM is suitable process for contouring operations, but no other process has the

capability of contouring operation except for EDM.

Table 1.2:- Shape application of Non-Conventional Processes [11]

Process

Holes Trough cavities

Surfacing Trough cutting

Precision small Standard Precision Standard

Double Contour

Surface of revolution Dia

<0.025 Dia >0.025

Length <20 mm

Length >20 mm

Shallow Deep

EDM - - Better OK Better Better OK - Bad - USM - - Better Bad Better Better Bad - Bad - AJM - - OK Bad Bad OK - - Better - CHM OK OK - - Bad OK - - Better - LBM Better Better OK Bad Bad Bad - - Better OK PAM - - OK - Bad Bad - Bad Better Better

1.1.3 APPLICABILITY TO VARIOUS MATERIALS

Materials applications for various machining processes have been shown at the tables 1.3 and

1.4. The table 1.3 is concerned with metals and alloys while table 1.4 is showing non-metals. It

5

can be noticed that both the ECM and EDM are unsuitable in machining of the electrically non-

conducting materials. In these types of cases the desired results can be obtained by mechanical

methods. USM and AJM are the most suitable processes for the machining of hard material.

Table 1.3:- Metals and Alloys [11] PROCESS Titanium Super alloy Steel Refractory Aluminum EDM Better Better Better Better OK AJM OK Better OK Better OK ECM OK Better Better OK OK CHM OK OK Better OK Better USM OK Bad OK Better Bad EBM OK OK OK OK OK LBM OK OK OK OK OK PAM OK Better Better OK Better

Table 1.4:- Cutting Performance for Non-Metals [11] PROCESS CERAMICS PLASTIC GLASS EDM - - - AJM Better OK Better ECM - - - CHM Bad Bad OK USM Better OK Better EBM Better OK OK LBM Better OK OK PAM - Bad -

1.1.4 THE MACHINING CHARACTERISTICS

Machining characteristics can be analyzed by considering the surface finish, the rate of metal

removal, depth of surface damage, tolerance, and power requirement for machining. The process

capabilities have been compared in table 1.5. The metal removal rate by EDM

6

Table 1.5:- Comparison of Machining Characteristics among Various Processes [11]

PROCESS Power (watts)

Tolerance (µ)

Depth of surface

damage (µ) Surface

(µ) CLA MRR

(mm3 /min) EDM 2700 15 125 0.2–1.2 800 USM 2400 7.5 25 0.2–0.5 300 AJM 250 50 2.5 0.5–1.2 0.8 ECM 100000 50 5.0 0.1–2.5 15000 CHM — 50 50 0.5–2.5 15

EBM 150 (avge) 2000 (peak)

25 250 0.5–2.5 1.6

LBM 2 (avge) 25 125 0.5–1.2 0.1 PAM 50000 125 500 Rough 75000

is far too low as compared to ECM and PAM. When ECM and PAM are compared to

conventional machining, they are quarter and 1.25 times respectively. Power requirement for

EDM is in the middle zone but it is very high for ECM and PAM in comparison to the rest of the

non-traditional processes of machining. EDM and ECM have very low tool wear rate but ECM

has drawbacks of the contaminating the electrolyte and the corroding the machine parts. The

surface finish for EDM is at par with other non-conventional processes.

1.1.5 THE ECONOMICS OF THE PROCESS

The economics of the various processes have been analyzed and given in table 1.6. They were

analyzed on the basis of capital cost, consumed power cost, tooling cost, the tool wear and the

rate of metal removal. Capital cost of the ECM is too high comparison to the other non-

traditional processes of machining and the capital costs for AJM and PAM are less in

comparison. The tooling cost for EDM is higher than most of the other non-traditional processes

of machining however it consumes less power. The metal removal efficiency for LBM and EBM

is very high than for the other processes and it is high for EDM. It can be concluded

7

that the suitability of application of any process depends on all these factors and they must be

considered before choosing a non-conventional processes.

1.2 WIRE EDM APPLICATIONS

In this section the feasibility of the process in the machining of the various materials is discussed

that are used mainly in tooling applications.

1.2.1 MODERN TOOLING APPLICATIONS

Wire-EDM has obtained wide popularity in the machining of some materials that are utilized in

the modern tooling applications. Various researchers [12,13] have attempted to investigate the

performance of the process by wafering of the silicon and by machining the compacting dies. For

the dressing of a rotating metal bond diamond wheel that is used for ceramics precision form

grinding, the viability of using cylindrical Wire-EDM has been also studied [14]. The analysis

has proven the capability of the process in generating intricate and precise profiles. These

Table 1.6:- The Economics of the process [11]

PROCESS Capital

Cost MRR

Efficiency Tool Wear

Power Consumption Tooling Cost

EDM Medium High High Less High USM Less High Medium Less Less AJM Too Less High Less Less Less ECM Too High Less Too less Too High Medium CHM Medium Medium Too less High Less

EBM High Too High Too less Less Less

LBM Less Too High Too less Too less Less

PAM Too Less Too Less Too less Too less Less

MCG Less Too Less Less Less Less

8

profiles have very small radii at the corners but comparatively the wear rate is high on the

diamond wheel after the first grinding pass. Such a high wear rate of the wheel at initial pass is

attributed to over protruding grains that are not bonded very rigidly to wheel when observed on

completion of the process [15]. A comparative study was carried out on the laser-cutting process

[16] in machining of soft MnZn ferrite magnetic and permanent NdFeB materials. They are used

in the miniature systems. It was ultimately found that the Wire-EDM process yielded better

surface finish quality and dimensional accuracy but the cutting rate was very low. Another study

was carried out to investigate in depth the micro Wire-EDM machining performance in

machining a component of high aspect ratio using variety of various materials that included

nitronic austentic stainless, stainless steel, titanium and beryllium copper [17].

1.2.2 ADVANCED CERAMIC MATERIALS

For the machining operation of the advanced ceramics, WIRE-EDM has emerged as one of the

most feasible substitutes. Sanchez et al. [18] carried out research on the machining of advanced

ceramics that were usually machined by lapping/diamond grinding. Feasibility to machine the

silicon infiltrated silicon carbide and boron carbide with the use of EDM and WIRE-EDM was

also studied. In another study, Cheng et al. [19] explored the feasibility of cutting ZrB2 based

materials with EDM and WIRE-EDM. The response of conductive carbide content, for example

titanium carbide and niobium carbide, upon the surface roughness and cutting rate of zirconia

ceramics after WIRE EDM was examined by Matsuo and Oshima [20]. Lok and Lee [21]

successfully carried out WIRE EDM of Sialon 501 and aluminum oxide titanium carbide. In this,

they found that the material removal rate was very low in comparison to the machining of the

metals like SKD-11 alloy steel and the surface roughness generally remained on the lower side

9

as compared to the EDM process. Dauw et al. [22] also reported that the material removal rate

and surface roughness do not only depend upon the machine variables but they are also

dependant on material. The method dealing with the WIRE-EDM technological limitations has

recently been explored that requires the materials electrical resistivity with specific threshold

values that are about 100 X/cm [23] and 300 X/cm [24]. Engineering ceramics have different

grades. Konig et al. [23] has classified ceramics as non conductor, natural conductor and the

conductor that is obtained by doping nonconductors with conducting materials. In this, Mohri et

al. [25] has been able to bring a new perspective in traditional EDM by using single assisting

electrode to facilitate the spark produced in ceramics which have a high electrical resistivity

value. Both the machining processes of EDM and WIRE EDM have been successfully tested.

Conductive particles from the assisting electrodes were diffused on to the surface of the

particular ceramics in which feeding of the electrode through the insulating material is assisted.

This procedure was also applied on the other various kinds of insulating ceramic materials with

the inclusion of oxide ceramics like Al2O3 and ZrO2. They fall in the category of limited

electrical conductive properties [26].

1.2.3 MODERN COMPOSITE MATERIALS

Among the various material removal processes for machining of the modern composite materials

the WIRE-EDM is considered an effective and very economical source. Many comparative

studies [27,28] have already been carried out between laser cutting and WIRE-EDM especially

in the metal matrix composites processing, carbon fibre and reinforced liquid crystal polymer

composites. These studies revealed that the process provides better quality as far as cutting edge

is concerned and there is a better control over process parameters resulting in lesser damages on

10

the surface of the workpiece. However, it showed lower material removal rate in the case of

every composite material which was tested. Gadalla and Tsai [29] compared the conventional

diamond sawing with WIRE-EDM. They found out that the process produced more surface

roughness as compared to the diamond sawing and the material removal rate was also higher.

Yan et al. [30] studied the different machining processes that are used to machine metal matrix

composites. Their experiment consisted of machining Al2O3/6061Al composite. They used

rotary EDM which was done with the help of electrode which had a shape of a disk. Few more

studies [31,32] have been carried out on WIRE EDM of particulate reinforced composites Al2O3

to investigate the affects of process variables on its performance. As a result of these studies it

was established that the process parameters have a very small affect on the surface roughness but

show a considerable affect on the cutting rate.

1.3 THESIS OVERVIEW

This thesis has been divided into various chapters that will describe the entire working starting

from introduction till the recommendation part. Chapter 1 provides the introduction to the

process. It includes the introduction to the non-conventional machining processes especially

Wire Electrical Discharge Machining. In this chapter, various non conventional machining

processes have been compared to the process on the basis of their inherent advantages. Chapter 2

comprises of the literature survey that has lead to the identification of the problems where a very

little research work has been carried out and on the basis of which the design of experiment has

taken place. The DOE for methodology has been explained in chapter 3. In this thesis basically

two sets of experiments were carried out. One of them was for the workpiece material thickness

and the other for its hardness. The aim was to identify the variables or process parameters that

11

affected the kerf width, surface roughness and the material removal rate in both the sets of

experiments. Besides thickness and hardness machine-specific process parameters were also

investigated. The experimental work for both the workpiece thickness and hardness has been

presented in chapters 4 and 5 respectively. The final chapter contains conclusion and

recommendations.

12

CHAPTER2LITERATUREREVIEW

This chapter provides a review on the various research areas related to the process. The main

section of the chapter focuses on the major research efforts that include the process optimization

along with the process monitoring and control.

2.1 INTRODUCTION

Wire-electrical discharge machining usually known as WIRE EDM, is a widely used non-

conventional process of material removal. With the help of this process manufacturing of parts

with intricate profiles and shapes can be carried out [29]. It is also a form of the EDM process

which uses an electrode for initiation of the spark. However, the process uses a wire electrode

that usually made of tungsten, brass or copper. The wire diameters range between 50 to 300

microns. This enhances the capability of the process in obtaining small corner radii. To avoid the

production of inaccurate parts this wire is kept under tension with the help of a mechanical

device. With the wire travelling ahead through the workpiece material without making any

physical contact, the material ahead of wire is eroded slowly. As there is no contact between the

wire and the material, the chances of production of mechanical stresses are minimized.

Moreover, the process can machine high strength materials. The process has the capability of

machining high temperature resistive materials also. It also eliminates the change in geometry

that is observed while machining steels after heat treatment. The initial introduction of the

process to the manufacturing industries took place around late 1960s. The process evolved as a

result of research being conducted for developing a technique to replace electrode used in

conventional EDM that requires machining before use. Dule-bohn[30], carried out a study in

13

which they were successful to control the shape of part automatically. In this study a system of

optical line follower was used. At this point of time, its popularity rapidly increased, as the

industry had well understood the process and its capabilities [31]. In the late 70s, computer

numerical control (CNC) system was introduced into the process. This was a major breakthrough

in the further development of this machining process. Ultimately, the process capabilities of

wire-EDM were explored extensively for through hole machining that was required because the

wire has to be passed through the workpiece before the hole can be machined. Most common

applications are the manufacturing of fixtures and gauges, the extrusion tools, prototypes, dies,

aircraft components, grinding wheel form tools and medical parts.

2.2 WIRE-EDM

In this section the information regarding the process variations when combined with other

material removal techniques and the basic principles of the process are provided. The material

removal process is same as that in conventional EDM technique where the erosion affect is

evolved with the help of sparks or continuous electrical discharges. In the process, a series of

discrete discharges occur between the wire and the workpiece material and the material erosion

takes place from the workpiece. The wire and the workpiece are segregated by a continuous flow

of dielectric fluid, which is in the machining area [32]. However, now a day, the process is

usually carried out where work material is fully submerged. It is a tank which is filled with

dielectric fluid to support machining. This submerged type of process has few advantages. It

results in temperature stabilization and an efficient flushing when the workpiece thickness is not

uniform. Using electrical energy a channel of plasma is generated by the process between

cathode and anode [33], and then it is transformed it into thermal energy [34] at the temperature

14

range 8000 to 12,000º C [35] or according to some other researchers it is reported as high as

20,000º C [26]. A noticeable amount of heat is generated and melting of material is initialized.

This melting starts on each pole’s surface. The plasma channel breaks down when pulsating

power direct current supply that occurs between 30,000 and 20,000 Hz is stopped [37], the

plasma channel will break down. Therefore a quick reduction in the temperature is caused and

the dielectric hits the plasma channel. This dielectric flushes away the microscopic debris from

the pole surfaces. Although the procedure of material removal in EDM and the process is same

but their functional characteristics are different. The wire is fed through the workpiece

continuously with the help of a microprocessor that provides the ability to machine parts with

exceptionally high accuracy and complex shapes. The achievement of tapper varying degrees

ranging from 30º for 400 mm thick and 15º for 100 mm thick components has become possible.

The gap is maintained constantly between workpiece and the wire with the help of

microprocessor. The gap normally can be controlled between the ranges of 0.025 to 0.05 mm

[31]. Pre-shaped electrode in the process is no more required as in the case of conventional EDM

for the processes of cutting and finishing. For obtaining the required dimensional accuracy and

surface finish the wire is required to make few passes along the profile of the workpiece.

Kunieda and Furudate [38] conducted a study on dry WIRE-EDM in a gaseous atmosphere and

the dielectric was not used. The purpose of the study was to enhance the efficiency of the

finishing process. In case where the workpiece material was D2 tool steel, the typical cutting rate

of the process is 300 mm2/min for cutting thickness of 50 mm and 750 mm2/min for cutting

Aluminum which is 150 mm [39], and the produced surface finish quality was between 0.04 to

0.25µ Ra. Moreover, instead of the hydrocarbon oils the process uses de-ionized water as di-

electric fluid. This de-ionized water is kept in the spark zone. The electrode wears very rapidly if

15

de-ionized water is used in conventional EDM [40], but due to its rapid cooling rate and low

viscosity it is suitable at the highest degree for the Wire-EDM process.

2.3 MAJOR AREAS OF WIRE-EDM RESEARCH

Wire-EDM is a specialized machining process which can machine the parts to the requisite

accuracy [35]. These parts also include the ones with complex shapes and varying hardness.

Generally parts having sharp edges cannot be machined easily, but this process provides the

facility of machining such parts also. The process utilizes basically the same non-contact

sparking phenomenon used in the conventional EDM. Since its introduction, it has become a

simple mean of making tools and dies. It is the best alternative for production of micro scale

parts with better surface finish and dimensional accuracy.

For many past years, this process has proven to be an economical and competitive machining

option. It is capable of fulfilling the most crucial and challenging machining requirements which

have surfaced due to the requirements of the present age that demand shorter product

development cycles and low costs. Research of a tremendous order has been carried out to

successfully explore the ways for obtaining optimized process parameters for different

machining responses analytically. Efforts have also been made by researchers to minimize wire

breakage for improving overall machining reliability. This section gives the review of WIRE-

EDM research that involves the optimization of the process variables along with the survey of

the affect of the various control factors that affect the performance of machining in terms of

response variables. It will also show the controlling of process, and its adaptive monitoring, with

an investigation into viability of different strategies for achieving the optimized conditions of

16

machining. With the development of many hybrid type of machining processes a vast range of

applications has been observed and reported. For easily understanding the process the research

has been classified into two major groups namely “optimization” and the “monitoring/control of

the process”.

2.3.1 OPTIMIZATION OF WIRE-EDM PROCESS

Process optimization is generally a difficult task due to efforts required to regulate control

variables. A change in single variable may affect the machining process in a very complicated

manner [41]. Therefore, variables that can affect the machining process should be studied for

determining the pattern of the process variation. Modeling the machining process is considered

to be an affective tool of addressing the problems associated with co-relation of the control

variables with the machining responses. However, the complex nature of the material erosion

process during machining really needs the application of deterministic and stochastic techniques

[42]. It is therefore, that the optimization of the process has remained a key research area. This

section will provide an over-review of various machining strategies that includes process

parameter design and the appropriate modeling of the processes.

2.3.1.1 PROCESS PARAMETERS DESIGN

The selection of appropriate value for a process parameter is very necessary. It plays an

important role in optimization of the required machining responses. In this section various

statistical and analytical methods are shown that are applied to investigate the affects of these

variables on the machining responses or performance measures like kerf, material removal

rate/cutting rate and the surface finish.

17

DRY WIRE-EDM

Furudate and Kunieda [43] carried out research on dry WIRE EDM. The reaction force of the

process proved to be negligibly small, the wire electrode vibration was minute and gap distance

was narrower to that in the conventional process that uses dielectric liquid. These factors enable

the dry WIRE-EDM to obtain high amount of accuracy in the finish cutting. The workpiece is

not corroded which is an advantage in the manufacturing of molds and dies of high precission.

Wang and Kunieda [44] concluded that the dry WIRE-EDM is suitable for improvement in the

machined surface straightness and for finish cut. Debris around working gap are removed by the

travelling tool and those even in the atmosphere. The straightness in workpiece thickness

direction in this dry process is much better than the process that is carried out in water [45]. In

another study Kunieda and Furudate [46] discovered some flaws of dry WIRE-EDM that

includes lower rate of material removal when compared to the conventional process. In addition

a big flaw during precision finishing cut by the dry EDM is the generation of streaks on the

finished surface. The flaws can be addressed by keeping the depth of cut to a smaller value and

increasing wire speed.

WIRE-EDM IN WATER

Levy [47] carried out experimentation for a high-volume dielectric regeneration and environment

friendly system for the process. In this, a filtration unit that consisted of the membrane

technology was used to obtain a very efficient system of dielectric regeneration. Diane [48]

emphasized on appropriate resin and mix which is utilized in de-ionizing the water used in the

process. Minami et al. [49] have proposed a procedure of applying color to the titanium alloy

while machining to curtail the requirement of any post treatment. Using conventional water

18

WIRE-EDM, the interference phenomenon of light with the anodic oxide film forming due to

electrolysis reaction, forced direct coloring a surface that was re-solidified after being molten.

Average working voltage was responsible for controlling the thickness of the captioned oxide

film. The film thickness was helpful in determining the color tone.

FACTORS AFFECTING THE MACHINING RESPONSES.

It is a complicated machining process that is governed by a vast number of control variables such

as the current intensity, pulse duration and the discharge frequency. Smallest of variations in any

of the control variables will affect the machining responses such as surface roughness and the

cutting rate, which have been declared as the most important aspects of the machining process

[50]. Suziki and Kishi [51] found out that for obtaining a better surface finish the discharge

energy needs to be reduced. Whereas Luo [52] discovered that additional energy was required to

efficiently maintain a higher rate of material removal, but the amount of energy should not also

be that high to damage the wire or cause wire breakage. Few of the authors [53] have carried out

research on the performance of the wire tool that directly affects the accuracy of machining and

various other performance measures. The selection of suitable control variables for the process is

largely based upon the relationship data that plays an important role in relating various control

variables to few machining responses namely the surface finish and material removal rate.

Normally, this was undertaken by consulting the technical data which was rendered by

equipment manufacturer or by relying on the operator’s experience. All of this has lead to the

experiencing of inconsistent performance of machining. Maggi [54] in a study has shown that the

specific control variables settings which were provided by the manufacture were applicable to

common steel grades only and there was a considerable difference when applied to other

19

materials. It proves that the manufacturer data cannot be blindly relied upon. The optimum

control variables settings for cutting of new materials have to be explored for obtaining

optimization of machining responses and this is to be achieved experimentally.

AFFECTS OF THE PROCESS PARAMETERS ON THE CUTTING RATE

Numerous analytical tools have been used for problem-solving and to establish the variables that

are significant. They were also used to study their inter-relationships for obtaining an optimal

machining rate. Konda et al. [55] has classified few of the potential variables that affect the

machining responses in five major categories. They are machine characteristics, the properties of

workpiece materials, component geometry, dielectric fluid and adjustable machining variables.

In addition the technique of the design of experiments was also used for obtaining data and

optimization of the expected possible responses of control variables in design and development

phase of the process. Signal to noise (S/N) ratio technique was applied to validate the results of

the experiment. Neural network system was employed by Tarng et al. [56] coupled with the

simulated annealing algorithm so that a problem of multi response optimization could be solved.

Hence, this was observed that machining variables like open circuit voltage, pulse on/off time,

peak current, table speed, servo voltage, and electrical capacitance were critical factors for

controlling cutting rate and surface finish. Huang et al. [57] after literature survey found out that

much of the published research [41,56,58] was mostly related with the optimization of control

variables for rough machining/cutting operations. They proposed process planning approach for

proceedings to finishing operations from the roughing operations. The results of their

experimentation show that the pulse on time affects the machining rate and surface finish. In

addition, for these responses the distance between the surface of the workpiece and the periphery

20

of the wire is also significant. The affects of discharge energy on the machining rate and surface

finish of the metal matrix composites have also been experimentally investigated by some

researchers [59].

AFFECTS OF THE PROCESS PARAMETERS ON THE SURFACE FINISH

Much of the literature and the published works are available entirely on study of the affects of

control variables settings upon the machined surfaces. Go Kler and Ozano Zgu [62] carried out

research for selecting the most feasible offset and cutting parameter combination for obtaining

the requisite surface quality when both the dielectric flushing pressure and the wire speed were

kept constant. Tosun et al. [63] in an investigation studied the affect of open circuit voltage, wire

speed, the pulse duration and the dielectric pressure upon the surface quality of the machined

workpiece. The results showed that the increase in wire speed, pulse duration and the open

circuit voltage caused an increase in the value of surface roughness, whereas a decrease in the

value of surface roughness was observed with the increase in dielectric flushing pressure. Anand

[64] in another study used fractional factorial design to achieve the most desirable control

variables settings for improving surface roughness and the dimensional accuracy. Spedding and

Wang [65] carried out optimization of control variables settings while applying artificial neural

network modeling technique for characterization of the machined surfaces.

AFFECTS OF THE PROCESS PARAMETERS ON THE KERF WIDTH

Nihat et al. 66 in an experimentation selected wire speed, pulse duration, open circuit voltage,

and flushing pressure to investigate their influence on kerf. They found out that the most

significant parameters were pulse duration and open circuit voltage. The di-electric flushing

21

pressure and wire speed were found to be non significant. Di Shichun et al. 67 carried out

research to analyze the Kerf in micro-WEDM. In this they found out that the kerf was being

affected by two components that is amplitude of wire vibration and breakdown distance. They

built models and relationships among the wire vibration amplitude and control variables were

also analyzed. A. Okada et al. 58 studied the affect of dielectric flushing nozzle stand-off

distance. They found that the flow velocity increased in the kerf by decreasing the stand-off

distance. However, the stagnation area was almost the same for about each stand-off distance,

when the upper and lower distances were same. Stagnation area was formed around the wire with

low velocity of the flow disregarding the flushing pressure values form both the upper and lower

nozzles. In this area the expulsion of debris was not very efficient and therefore flushing from

both the upper and lower nozzles will not be affective always for expulsion of debris from the

kerf. When the flow rate is kept constant, most of the debris is expelled out of the kerf from the

same area. The debris distribution influences the state of the electrical discharges taking place in

the kerf. Secondary discharges occur in the kerf when stagnation of too many debris takes place.

This occurrence of the secondary discharges is concentrated in the same location and leads to

unstable machining performance. W.Y. Peng and Y.S. Liao 69 in their study of kerf found out

that the kerf size depends upon the discharge energy and the cutting wire diameter. On time must

be controlled accordingly to get optimal results. They also proposed to set the servo voltage to a

higher value in order to avoid wire breakage due to the accidental workpiece and wire contact.

AFFECTS OF MACHINING PARAMETERS ON MATERIAL REMOVAL RATE

The influence of control variables upon the material removal rate (volumetric) has been also

considered for measuring the quality of the machining responses. Factorial design was applied by

22

Scott et al. [41] that requires several experimental runs for establishing the most suitable

combination of the control variables. As a result, they realized that dielectric flow rate, wire

tension, and wire speed were not significant, while the pulse frequency, duration and discharge

current were most significant process parameters that were affecting the material removal rate a

great deal. Based on Taguchi method where analysis of variance is also used, Liao et al. [58]

came with a comparatively new approach for determining the control variables optimal setting.

In this research it was found out that surface finish and material removal rate are heavily affected

by the pulse on time and the rate of table feed. The discharging frequency can be controlled by

controlling pulse on time in order to prevent sudden breakage of wire that causes unnecessary

delays in the process and affects its reliability. Similar kinds of results have been produced in

another study in which S/N ratio analyses were used which was carried out by Huang and Liao

[60]. Aiming for the determination of material removal rate and surface finish at different control

variables settings, another study has been carried out by few researchers [61]. The results

obtained have been utilized using a thermal model for analyzing wire breakage phenomena.

2.3.1.2 PROCESS MODELING

Furthermore, the modeling of the process with the help of various mathematical techniques also

has been applied by many researchers in order to co-relate efficiently the various different

machining performance measures of WIRE-EDM to vast number of process parameters.

Modeling techniques have been developed by Spedding and Wang [70] with the use of response

surface methodology and the artificial neural network technology for predicting machining

performance measures such as surface finish, cutting rate and surface waviness. In this the

control variable levels range was kept large in order to achieve better results. A solid modeling

23

method that can accurately represent a geometry produced by the WIRE-EDM process has been

proposed by Liu and Esterling [71]. On the other hand Hsue et al. [72] have succeeded in

developing a model that estimates material removal rate in case of geometrical cutting by taking

into account the deflection of the wire and with the wire centre transformed exponential

trajectory. Spur and Schonbeck [73] carried out WIRE-EDM of a work piece for studying the

workpiece material influence. Anodic polarity was used for the workpiece, and a theoretical

model was proposed by studying influence of the type of pulse and that of the workpiece

material. In order to develop a simulation system for the accurate reproduction of the discharge

phenomenon, Han et al. [74] carried out another study in which they were successful in

achieving the objective. High precision optimal machining conditions are established

automatically as this system also takes in to account the application of adaptive control.

2.3.2 PROCESS MONITORING AND CONTROL OF WIRE-EDM

Affective monitoring and control of the process requires efficient application of adaptive control

system. An overview of the modern monitoring and control systems is provided in this section

which includes the wire breakage, the fuzzy and the self tuning adaptive control systems.

2.3.2.1 FUZZY CONTROL SYSTEM

Generally servo feed control system are comprise of the proportional controllers. This has been

done for evaluating and monitoring the condition of the gap during the process of machining.

However, it was found out that the machining conditions were the main cause for limiting the

performance of these controllers. Machine conditions are expected to have major variations as

the applied control variables settings are varied. A study was carried out by Kinoshita et al. [75]

24

on the gap state for different settings of wire speed, feed rate, tension, and the electrical variables

during the machining process. As a result of this, based on explicit statistical and mathematical

models, a number of conventional control algorithms were developed for the process [76–80].

Various other have carried out research on discrimination system of pulse [81,82]. They have

developed a system in which under different conditions of machining analysis and monitoring of

the pulse trains can be carried out quantitatively. These kinds of control systems will not be able

to cater for the gap condition in case an unexpected disturbance occurs but can be applicable to a

wide range of the conditions of machining [83].

For achieving the optimization of the machining process fuzzy logic has been applied

successfully in the recent past. According to the various authors it has been derived by

implementing a control strategy that basically captures the experience of the operator for the sake

of carrying out the desired machining operation [84]. Furthermore, fuzzy logic controller does

not need very complex mathematical models that adapt to WIRE-EDM operation’s dynamic

behavior [85]. Several authors [83,86] proposed adaptive control systems and sparking frequency

monitoring for better operation of the machining process. Most are based on few strategies of

adjusting that are applied to wide range of the machining conditions and the fuzzy logic. Another

research by Liao and Woo [87] was carried out in which they were able to design a fuzzy

controller to control the different characteristics of the discharge pulse. The discharge noise was

isolated with the use of an online monitoring system for pulse. The ignition delay discrimination

for each pulse was also applied. Fuzzy control systems have been applied successfully by these

researchers to obtain the desired results in order to improve this process of machining.

25

2.3.2.2 WIRE-INACCURACY ADAPTIVE CONTROL SYSTEMS

The most unwanted characteristics in machining is the wire breakage during the operation [88] as

discussed earlier also. The component quality, performance of machining, and accuracy are

affected a great deal by this flaw or phenomenon. Development of adaptive control system for

wire breakage has been attempted by many researchers. Adaptive control system should be such

that it can monitor the online conditions of machining and can identify any abnormalities. The

system should have a solid strategy that can prevent wire breakage without having any adverse

affects on the performance of machining or the requisite machining responses. This section

presents the research collected from the available literature survey that provides information

about the different characteristics of the tool wire like wire vibration, wire breakage and wire lag.

WIRE BREAKAGE

Many control strategies have been designed in order to address the phenomenon of wire

breakage. Few of them will be discussed in this section to have an overview of the available

literature on the problem. Kinoshita et al. [88] in a study have shown that prior to the wire

breakage there is a sudden rise in the frequency of pulse for the gap voltage and continues from 5

to 40 micro seconds. In this they incorporated a monitoring and control system. The function of

this system was to switch off the pulse generator and the servo system before wire breakage.

Thus they were able to increase the efficiency of machining. Several authors [89,90] have shown

that the increase in the localized temperature is the outcome of electrical discharges

concentration at one point and this results in the form of wire breakage. In these studies the main

area of focus was the spark location and the systems were developed for reducing the amount

discharge energy but it is pertinent to mention here that the material removal rate was either

26

ignored or was not given the due consideration. Some researchers have related the breakage of

wire to the short circuits quantity. Prior to the breakage of the wire these short circuits duration

should prolong to 30 micro seconds for the wire breakage to occur [91]. Some of the researchers

have come to a conclusion that a sudden increase of the spark energy causes the wire breakage

[92]. It was also revealed that the material removal rate was affected by their proposed system of

monitoring and control. This system was based upon the online analysis of real time regulation

of the factors like pulse off time and sparking frequency. The remedy to this was provided by

Liao et al. [93] by means of incorporating a newer pulse discrimination system which was

computer aided and relating the material removal rate to machining parameters. For improving

machining rate the discrimination system was based on the pulse train analysis. A self-learning

fuzzy control system which was applied by Yan and Liao [94,95] which not only served as a

means of controlling the sparking frequency but it also maintained a better material removal rate

by constant feed rate and incorporating few adjustments in the online pulse off time while the

feed-rate was kept constant.

Excessive thermal loading is also responsible for the wire breakage. It results in the unwarranted

production of heat on the wire. During machining most of the generated thermal energy is

transferred to the wire while the remaining is being taken away by the flushing fluid or radiated

away [92]. Depending upon the wire’s thermal properties whenever there is an increase in the

instantaneous energy rate beyond some specific limit, the wire shall break. Some of the authors

[96–98] carried out studies on the thermal loading of the wire. In most cases they have analyzed

the relation between the used control variables and the thermal loading of the wire. Thermal

models were developed for simulating the process in real time. Mechanical strength of wire

27

material cannot be neglected as it has a significant effect on frequency of wire breakage in

addition to the sparking characteristics and temperature distribution.

The sparking frequency monitor has been developed by Rajurkar and Wang [99]. This monitor

had a thermal load detector and was meant for on-line monitoring and control for preventing

wire breakage. Thermal model was used for analyzing the phenomena of wire breakage. The

multi objective model was used for establishing relationship between surface finish and material

removal rate under optimal settings. Vibrational behavior of the wire in another study was

considered by Puri and Bhattacharya [100]. Using an analytical approach, they have proposed

the consideration of multiple discharges as a solution to equation of wire vibrational behavior.

They concluded that wire vibration will increase between the wire guides in machining of a

thicker workpiece as compared to a comparatively thinner workpiece.

WIRE LAG AND WIRE VIBRATION

These are the main factors that contribute to the geometrical inaccuracy. There are few forces

that act on the wire during cutting operation. In this case the preplanned programmed path cannot

be followed by the wire and a deviation is experienced. Plasma of erosion mechanism causes the

gas bubbles to be produced frequently and this production of bubbles creates these forces. Some

hydraulic forces are produced due to flushing [101,102]. To cater for almost all these forces, the

application of the axial forces keeps the wire straight. Few more forces act on the wire tool that

is spark inherent electro dynamic forces and electro static forces. Static deflection is obtained as

a consequence of all these forces. It is translated in to the wire lag affect during machining and

has been studied critically for producing accurate path of cutting tool. Many authors [103,104]

28

have made an attempt to present accurate mathematical models of the process and have

performed research on geometrical inaccuracy caused due to wire lag. In this, using a control

algorithm, Beltrami and Dauw [105] attempted to monitor and control the wire position online

with the help of an optical sensor. This enabled machining virtually any contour at a relatively

higher speed. Many geometrical tool motion compensation techniques [106,107] have also been

developed to prevent wire breakage. It includes increasing of the machining gap while machining

small radii or high curvature profiles. Based on fuzzy logic, for improving concentrated sparking

at the corners and the machining accuracy without affecting rate of material removal, Lin et al.

[108] developed a control strategy. Guo et al. [109] carried out another study on ultrasonic wire

vibration. They found out that the hybrid form of the process with ultrasonic vibrations can

facilitate the production of multiple channel discharge. In this way the energy is utilized

efficiently to support more material removal rate and the surface finish is also improved.

Discharge concentration conditions are improved due to vibration of wire at higher frequencies

that helps in minimization of the probability of the wire breakage. Another study was carried out

by Guo et al. [110] on material removal rate and surface finish. They focused on the ultra sonic

aid and concluded that the surface roughness and material removal rate can be improved with use

of this aid. An improvement of 30 % in the material removal rate was reported by the

researchers.

The dynamics of the cutting wire are controlled during machining for avoiding the inaccuracies

that may be experienced during cutting. Some of the discussions [22,111] are available on the

development of control system for providing compensation against the behavior of wire. It has

been found out by Dauw et al. [112] that when the entire wire and the guides are fully submerged

29

in water, the amount of vibrations is reduced. A few authors [113] carried out research by

considering the forces produced in single discharge. They carried out the analysis of the wire

vibration and also produced mathematical models.

2.3.2.3 SELF-TUNING ADAPTIVE CONTROL SYSTEMS

In machining of the workpieces of varying height, some control strategies have been explored in

the recent past for controlling the power supplied to wire tool. In order to control the sparking

frequency while taking into account the height of the workpiece measured online, Rajurkar et al.

[114,115] have produced multiple inputs model with an adaptive control system. Some of the

researchers have found out that during machining if the workpiece height is varying, it will

cause sudden variations in thermal density [88,91] and ultimately will result in wire breakage.

Few researchers carried out to many experiments and statistical analysis to develop explicit

mathematical models [80]. For addressing the problem of wire breakage, Yan et al. in a study

used fuzzy control logic [116] and the neural networks in order to estimate the height of

workpiece being machined.

In order to control the worst machining situation the idea of developing knowledge based control

system has been also undertaken. A system comprising of three modules has been proposed by

Snoeys et al. [117]. These modules included the process control, work preparation and operator

assisted fault diagnosis that enabled the control and monitoring of WIRE-EDM process. In this

the work preparation module determined the optimal control variables settings, whereas the fault

diagnostics and operator assistance databases are meant to diagnose the machining fault and

advise the operators accordingly. Huang and Liao [118] studied and emphasized on the need of

30

the fault diagnostics system in the WIRE-EDM. In their study they recommended artificial

neural network based expert system. This system was developed for the fault diagnosis and

routine maintenance. A thermal model was produced by Dekeyser et al. [119] for precisely

controlling and predicting thermal load on the wire during machining operation.

2.4 GAP ANALYSIS AND PROBLEM STATEMENT

In this literature survey, it has been noticed that much work has been carried out on optimization

of machining responses but there still is a room for improvement especially in case of material

removal rate. Although many researches were available on the machining parameters for

optimizing machining responses but most of the researchers have chosen some specific

parameters to define the machining outcomes. Therefore the need to use a larger number of

machining parameters was felt. Furthermore, very little research work was found on Tungsten

Carbide and it was decided to use the same material in this research work. Apart from selection

of machining parameters some other non-machine factors were also considered in this research

work such as thickness and hardness as very little work was available on these two aspects. So

this research was focused on the areas where the literature contained a very little research. In

short, the research was focused on machine parameters, non-machine parameters like thickness

and hardness, and Tungsten carbide as workpiece material.

31

CHAPTER3DESIGNOFEXPERIMENTS

A technique in applied statistics which includes planning, interpretation, analysis and conduct of

specific experimental runs is known as design of experiments [120]. Its objective is the

evaluation of variables that affect the response variables or the required output of any

experimentation. Manipulation of the input variables can be achieved with this tool for observing

the influence on the required machining response in this case. Interactions of the variables, if not

always, can play a major role some time on the output or response. DOE can also help in

identifying and determining the affects produced by these interactions on the response or the

output. DOE is a flexible tool due to versatility of its use in a vast variety of situations or

experiments. It can be used not only in full factorial designs where all of the experimental runs

are required to be executed but can be used in fractional factorial designs also where a part or

portion of the experimentation is required to be carried out.

DOE is often used if the response variable is suspected to be affected by multiple variables

instead of a single variable. DOE is used not only to establish the relationship between the input

variables and the response but also serves as a means of developing mathematical models for

these relationships. The affects of the control variables on the output or response variable can be

obtained efficiently and effectively if the experimentation is logically planned. In most of the

experimentation the levels of few variables are kept constant while the level of only one variable

is varied at a time to observe its affect.

32

In the present research, for statistical analysis, many researchers are following the technique

introduced by Fisher in 20th century. Fisher emphasized on spending more time on the design of

experiment execution rather than wasting more time in tackling or solving the problems at

analysis phase when the experiment has already been executed.

3.1 TAGUCHI METHOD

The Taguchi method was developed by Dr. Genichi Taguchi of Japan. Applying robust design of

experiments, this technique has been used to reduce process variations [121]. The mean and the

variance of process performance determine the functioning quality of a process. A model was

developed by Taguchi for experimental design in order to investigate the affect of different

control variables on the mean and variance. Taguchi technique involves the identification of the

levels at which the control variables should be operated and the use of the orthogonal array to

observe the affects of control variables on the response variable. In this method execution of all

the experimental runs or the combinations is not mandatory but selected pairs of experimental

runs can be executed to save time and efforts. This technique can always be used in situations

when the contribution of few variables is significant, the variables are from 3 to 50 in number

and incase where interactions of variables is encountered.

The selection of the arrays is governed by the number of levels and the number of control

variables. After the selection of the array the experimental runs are executed as the array

specifies. The collected data is then analyzed for determination of most significant variables by

ANOVA and then selection of the optimal conditions or levels of the control variables by using

S/N ratio analysis.

33

3.1.1 PHILOSOPHY OF THE TAGUCHI METHOD

a. Introduction of quality in a process means the quality of tolerance design, parameter

design, and system design. Parameter design is the identification of the control variables

that have a major affect on the product’s quality. After this they are designed to produce

the requisite quality.

b. Deviation from the target should be minimized for obtaining quality. It means high value

of signal to noise ratio.

c. The cost of quality should be measured as a function of deviation from the standard and

the losses should be measured system wide.

In the next section, the specific steps involved in the application of the Taguchi method will be

described and examples of using the Taguchi method to design experiments will be given.

3.1.2 TAGUCHI METHOD DESIGN OF EXPERIMENTS

Generally following steps are involved in Taguchi Method:

a. Process objective for a response variable must be defined; it can be a target value.

b. Selection of appropriate control variables is required that are expected to affect the

response variable.

c. This follows the selection of orthogonal arrays which will depend upon the number of

control variables and their levels.

d. Execution of experimental runs to measure the response variable.

e. Statistical analysis of the data to select most significant control variables.

34

Figure 3.1 describes the pictorial depiction of these steps and additional possible steps,

depending on the complexity of the analysis.

Figure 3.1: Steps involved in the Taguchi Method 121

3.1.2.1 TAGUCHI LOSS FUNCTION

Minimizing the variance of process performance to reduce the losses to the manufacturer and the

society is the primary objective of Taguchi method. The loss function is defined as the difference

35

between the target value, τ, and the measured value, y, of a performance characteristic and is

shown as below [121]:

l (y) = kc ( y - τ )2 ……………………….(3.1)

The kc is a constant and can be calculated by using acceptable interval, delta.

kc = C / ∆2 ………………………………(3.2)

The kc is difficult to be determined as the τ and C are difficult to define at times. For minimizing

the performance characteristic value, the loss function will be calculated as follows:

l (y) = kc y2 …………………………….(3.3) where τ = 0

For maximization of the performance characteristic value, the loss function will be calculated as

follows:

l (y) = kc / y2 …………………………….(3.4)

3.1.2.2 DETERMINING PARAMETER DESIGN ORTHOGONAL ARRAY

The feasible orthogonal array can be selected after the selection of appropriate control variables

and their levels. Taguchi developed an algorithm for the creation of these orthogonal arrays that

has made the testing and analysis of each variable possible. The additional columns of an array

can be ignored if they are in excess to the number of control variables used.

36

3.1.2.3 ANALYSIS OF THE EXPERIMENTAL DATA

The next step after the selection of an appropriate orthogonal array is the execution of

experimental runs and collection of data by measuring the response variable. This measurement

can be used to analyze the affect of each control variable at individual levels. Here an example is

being presented for demonstrating the procedure of data analysis, L9 array has been selected, as

shown in table 3.1. This procedure is applicable to all other arrays.

Table 3.1: Taguchi L9 Orthogonal array 121 Experiment Number P1 P2 P3 P4 T1 T2 … TN

1 1 1 1 1 T1,1 T1,2 … T1,N

2 1 2 2 2 T2,1 T2,2 … T2,N 3 1 3 3 3 T3,1 T3,2 … T3,N 4 2 1 2 3 T4,1 T4,2 … T4,N 5 2 2 3 1 T5,1 T5,2 … T5,N

6 2 3 1 2 T6,1 T6,2 … T6,N 7 3 1 3 2 T7,1 T7,2 … T7,N 8 3 2 1 3 T8,1 T8,2 … T8,N 9 3 3 2 1 T9,1 T9,2 … T9,N

It can be noticed that the experimental runs can be repeated more than once as desired by the

situation. These different repetitions are presented by Ti,j, where i represents the experiment

number and j represents the trial number.

The signal-to-noise ratio (SN) value is then calculated to identify the affect of each control

variable on the response variable. For the target or preset value of the response measure the value

of signal to noise ratio has been calculated for the initial experimental run. yi is the mean value

and si is the variance in the following equation:

………………………..(3.5)

where

37

………………..(3.6)

si2 ….(3.7)

i = Experiment number, u = Trial number, Ni = Number of trials for experiment i.

SN ratio is calculated for both the cases of minimization or maximization. In the case of

minimization following relation will apply:

) …………(3.8)

And in the case of maximization following equation will apply:

) ……..(3.9)

The average SN ration is calculated after the individual SN ratios have been calculated for each

experimental run. For a parameter that is 3(P3), it is shown in table 3.2.

S NP3,1 = ……………(3.10)

S NP3,2 = ……………(3.11)

S NP3,3 = ………..…..(3.12)

38

Table 3.2:- Calculating Mean S/N Ratio121

After the calculation of SN ratios for all experimental runs and for each level of control variable

they are put in a form of table as table 3.3. Then the lowest value is subtracted from the highest

value in a row to obtain the range for each level.

Table 3.3:- Mean S/N Ratio121

The affect of a control variable on response variable depends on this value of range and both are

directly proportional. If the range value of that control variable is high the affect is more and if

the value is low the affect of that control variable is less.

3.2 ADVANTAGES AND DISADVANTAGES OF TAGUCHI METHOD

Taguchi method has many advantages that are described below:

39

The Taguchi method has a very significant advantage that it considers setting a target

value and then bringing the mean performance characteristic value closer to this value for

improving the quality. In general, apart from Taguchi method, there is hardly any set

target value but the limits are defined that are to be focused.

Taguchi method has been extensively used for identifying problems of certain machining

processes on the basis of held data and in downsizing the scope of a research to save time

and cost.

It is a simple but powerful tool of DOE.

In addition, its application in DOE is very simple and straightforward.

The analysis of vast number of control variables without the need of long exhaustive

experimentation has been made possible by the application of Taguchi method. As an

example this research can be considered where experimentation is required to be carried

out for a process in which 8 control variables have been selected with 3 levels each. If the

number of runs are calculated, then it will require 6561 (38) experimental runs to be

carried out if the full factorial design is used. In this case if the Taguchi method is

selected, the requirement of experimental runs to be executed can be limited to only 18

also, that is not more than 0.3% of total number of experimental runs.

So the Taguchi method is a powerful and simple tool that helps in reduction in the

amount of experimental work and costs of experimentation.

40

It also helps in the identification of most significant control factors that have an effect on

the response variable in a process. The significant control variables can be further

investigated and used for optimization of the process.

The Taguchi method has various disadvantages also that are listed as follows:

The entire control variable combinations are not tested in Taguchi orthogonal array and

therefore the use of this method should be avoided when the relations between all control

variables are to be tested.

According to the literature, this method has a flaw in accounting for the control variables

interactions.

In addition, Taguchi method does not deal with correction of poor quality but with

designing quality.

This method is most affectively applied in beginning of the process development phase.

In this case, when the design variables specification has been made, the cost effectiveness

of the design of experiment may be less.

Simulation studies of the process that is changing dynamically cannot be carried out off

line whereas this method is applicable to off line study only.

3.3 RECENT RESEARCH WORK INVOLVING TAGUCHI METHOD

Yan-Cherng Lin et.al 122 developed a process of EDM. In this a detailed study on the

influence of magnetic forces on the process was carried out. Taguchi method was used to carry

out experimental runs. The selected control variables included peak current, machining polarity,

41

auxiliary current, voltage, servo voltage and the pulse duration. Surface roughness and material

removal rate were selected as the response variables. L18 orthogonal array was selected for the

purpose and the data were collected. ANOVA was applied to analyze the obtained data. Salman

and Kayacan 123 also used this method to carry out a research on wire electrode materials like

copper tungsten, graphite and copper. Pulse on time, current, the arc voltage and pulse off time

were selected as control variables. In their study the surface roughness was selected as the output

or response variable. Modeling of the surface roughness was carried out after the analysis of the

obtained data by Taguchi method. H. Zarepour et.al 124 in a study on EDM, experimentally

investigated the affects of the control variables that included on-time, peak current, voltage, the

time of engagement between the workpiece and electrode. In this an L50 (21 ×511) Taguchi’s

orthogonal array was used as experimental design. Y.S. Liao et al. 125 also performed

experimentation for obtaining better surface finish using Taguchi method. A.B. Puri and B.

Bhattacharyya 126 employed Taguchi methods to study the phenomenon of wire lag in Wire

EDM and the trend of variation of geometrical inaccuracies caused due to wire lag with various

selected machine control parameters. In this study, most of the control variables were

simultaneously selected for an in depth study. This study was based on Taguchi method and the

aim was to carry out an investigation. An orthogonal array L27 (3x13) was used and involved

various machining parameters having three levels each. Optimum parametric settings were

established. The selected control factors were pulse off time, on time, corresponding duty factor,

wire feed velocity, pulse peak voltage, pulse peak current, wire tension, servo voltage, di-electric

flow rate, cutting speed and the wire offset. Nihat et al. 127 used Taguchi design of

experiments to investigate the affects and optimization of control variables upon material

removal rate and the kerf. The selected control variables were wire speed, pulse duration,

42

dielectric flushing pressure and the open circuit voltage. The appropriate levels for these control

variables were established. Then the influence of these control variables was found out with

ANOVA. The optimal control variable setting was obtained by the analysis of signal to noise

ratio.

43

CHAPTER4EXPERIMENTATION,STATISTICALANALYSESAND

OPTIMIZATIONWITHAFOCUSONWORKPIECETHICKNESS

In this chapter information on DOE and experimental setup has been provided. The collected

data has been presented. It also includes the affects of variables, such as Wire-EDM process

parameters, on the machining responses that are kerf width, surface roughness and the material

removal rate of tungsten carbide samples machined by Wire-EDM. Workpiece thickness was

also taken as a process parameter along with various other machine-specific process parameters.

Rationale behind the selection of workpiece material for this experimentation has also been

discussed in this chapter. So, in total 08 process parameters including thickness were taken with

three levels each. Taguchi technique has been applied for design of experiment. ANOVA was

carried out after obtaining the machining responses to determine the significant factors or

process parameters. Finally optimization of the machining responses was carried out.

4.1 INTRODUCTION

S. H. Lee and X. P. Li 128 carried out a research to study the influence of process parameters

of EDM on the machining responses. They evaluated the effectiveness of the EDM process,

while using tungsten carbide as workpiece material, in terms of the material removal rate, the

relative wear ratio and surface finish of the end product. M.P. Jahan et al. 129 in the study

investigated the influence of major process parameters on the performance of micro-EDM of

tungsten carbide with a focus on obtaining quality micro-holes. However it was strongly felt that

wire EDM of tungsten carbide was also required to be carried as sufficient published material

44

was not available on the relationship of these input and output responses. So, more work was

required to be carried out in order to affectively contribute to the available research literature.

Tool making is the most typical application of the wire EDM 130,132. Wire EDM is applicable

to all kinds of mould development, intricate shapes, special parts, stamping dies, extrusion dies

prototype parts and machining of hard materials such as tungsten-carbide machining136,141.

Many research papers are available on materials such as Tool Steels, Die Steels, Germanium and

Titanium alloys 131-135 etc. However, very little work has been carried out on tungsten-

carbide machining.

A.B. Puri and B. Bhattacharyya 126, in 2003, performed wire EDM on a workpiece of Die

Steel in order to investigate the affects of machining parameters on surface roughness and cutting

speed. In this they were successful in determining some most significant control factors. In their

work they used large number of machine-specific process parameters in order to have a broader

view of the process. However, they did not consider the affect of the workpiece thickness as it

was not in their scope of this study.

Dinesh and Eberhard131, in 2009, carried out kerf width analysis of germanium wafers that

were machined by Wire-EDM. They developed mathematical model for predicting the kerf

width against various process parameters. In these models the workpiece thickness was kept

constant. Hence the affect of workpiece thickness on kerf width was not available.

45

Therefore, in the current experimentation tungsten-carbide has been selected as workpiece

material and the thickness has also been selected as a process parameter in addition to machine-

specific process parameters.

4.2 EXPERIMENTATION

The experimental design, experimental equipment, instrumentation, and the data acquisition

procedures to meet the objectives of this investigation will be discussed in this section.

4.2.1 DESIGN OF EXPERIMENT

In this study large number of process parameters was taken in order to enhance the accuracy of

the results126. There were a total of 08 parameters with 03 levels each. The parameters along

with their levels are listed in table 4.1.

Table 4.1:- Data Summery of Experiments Factor Code Process parameters Units Level 1 Level 2 Level 3

A TH Thickness mm 25.4 50.8 76.2 B OV Open Voltage Volts 75 90 105 C ONT On Time μs 3 4 5 D OFT Off Time μs 20 25 30 E SV Servo Voltage Volts 40 50 60 F WF Wire Feed Velocity mm/sec 30 60 90 G WT Wire Tension Grams 1000 1600 2200 H WL Dielectric Pressure Kg/Sq.cm 10 12 14

Since there were 08 process parameters each with three levels in the experiment, if a full factor

experimental design had been used, there would be 6561 runs which are too many to be

conducted. It would have been time consuming and too expensive to go about. The solution was

46

to use only a fraction of these runs as specified by the full factorial design. There are various

strategies that ensure an appropriate choice of runs, as discussed in chapter 3, for example the

Taguchi's orthogonal scheme 126. Since each input parameter in this experiment was

considered independent, an orthogonal experimental scheme could be used to reduce the total

number of runs. Therefore an orthogonal array L27(3x13) has been used. In this, out of 13

columns, 08 columns were assigned to 08 process parameters and the rest 05 columns were left

alone for interactions. Thus the Taguchi Orthogonal Array in this case appears as table 4.2.

Table 4.2:- Experimental Runs Specified by Taguchi L27(3x13) Orthogonal Array EXP.NO. A B C D E F G H I J K L M

1 25.4 75 3 20 40 30 1000 10 x x x x x

2 25.4 75 3 25 50 60 1600 12 x x x x x

3 25.4 75 3 30 60 90 2200 14 x x x x x

4 25.4 90 4 20 40 30 1600 12 x x x x x

5 25.4 90 4 25 50 60 2200 14 x x x x x

6 25.4 90 4 30 60 90 1000 10 x x x x x

7 25.4 105 5 20 40 30 2200 14 x x x x x

8 25.4 105 5 25 50 60 1000 10 x x x x x

9 25.4 105 5 30 60 90 1600 12 x x x x x

10 50.8 90 5 20 50 90 1000 12 x x x x x

11 50.8 90 5 25 60 30 1600 14 x x x x x

12 50.8 90 5 30 40 60 2200 10 x x x x x

13 50.8 105 3 20 50 90 1600 14 x x x x x

14 50.8 105 3 25 60 30 2200 10 x x x x x

15 50.8 105 3 30 40 60 1000 12 x x x x x

16 50.8 75 4 20 50 90 2200 10 x x x x x

17 50.8 75 4 25 60 30 1000 12 x x x x x

18 50.8 75 4 30 40 60 1600 14 x x x x x

19 76.2 105 4 20 60 60 1000 14 x x x x x

20 76.2 105 4 25 40 90 1600 10 x x x x x

21 76.2 105 4 30 50 30 2200 12 x x x x x

22 76.2 75 5 20 60 60 1600 10 x x x x x

23 76.2 75 5 25 40 90 2200 12 x x x x x

24 76.2 75 5 30 50 30 1000 14 x x x x x

25 76.2 90 3 20 60 60 2200 12 x x x x x

26 76.2 90 3 25 40 90 1000 14 x x x x x

27 76.2 90 3 30 50 30 1600 10 x x x x x

47

4.2.2 EXPERIMENTAL SETUP

A Wire-EDM machine G43S (CHMER EDM CHING HUNG Machinery & Electric Industrial

Co. Ltd. TAIWAN) was used for conducting the experiments as shown in figure 4.1.

Figure 4.1: Wire-EDM machine G43S

This machine uses de-ionized water as dielectric. A brass wire of 250 μm diameter (tensile

strength 800-1000 N/Sq.mm) was used as the tool electrode (cathode). The workpiece material

(anode) used was Tungsten Carbide (0.88% WC, 12% Co, grade DK 500 UF, 92.4 HRA, density

14.15 g/cm3). The tungsten carbide bars were available in square cross section of 12mm x 12 mm

and of about 7 inches length. They were first lengthwise cut to obtain three levels of thickness

that is 1 inch, 2 inches and 3 inches as shown in figure 4.2 and 4.3. It is pertinent to mention here

that this pre-experimentation cutting of samples to desired thickness level helped a lot to

determine the workable levels of machine process parameters. This is because in Wire-EDM the

wire breakage is a biggest hurdle in completing a run and that happens when a too large or small

value for process parameters is selected at individual levels. During this cutting the limitations of

48

Figure 4.2: Samples preparation

Figure 4.3: Samples of 1, 2, and 3 inches thickness

49

the process were well understood and process parameter levels were selected accordingly to

efficiently conduct the runs and save time that is wasted in case of wire breakage. According to

design of experiments, 27 runs were required to be carried out. Each run was repeated three

times to ensure good repeatability. So in total 81 samples were prepared, 27 for each of the three

thickness levels. A special clamp was made for holding the workpieces during cutting as shown

in figure 4.4.

Figure 4.4: Clamping of workpiece

The samples were machined with a total depth of cut of 12 inches in “L” shape, 7 mm in x-axis

and 5 mm in y-axis. This was done intentionally to increase the cutting time and material

removal for obtaining better and reliable data for onward analysis. The photomicrographs of few

of the workpieces are shown in figure 4.5a, b and c.

50

a

b

C

Figure 4.5: Photomicrographs of the cut workpieces

4.2.3 DATA COLLECTION

In this section ways and means that were used for data collection will be discussed for the three

machining responses. After the data were obtained, the statistical analysis of the obtained data

was carried out for validity of the obtained data. In this the standard deviation and the confidence

limits were calculated. The standard deviation of a set of “N” numbers X1, X2, X3, …….., XN is

denoted by “s” and is defined by [138]:

N

s = √ ∑ (Xj – Xm)2 / N ………………………..(4.1) j=1

Here, N is the sample size, Xm is the arithmetic mean, Xj is the jth reading or number. Thus “s” is

the root mean square of the deviations from the mean, or, it is sometimes called the root mean

square deviation. In the case of KF, Ra and MRR, N=3. Standard deviation for each run for the

three responses was calculated. Some fixed relations were used to establish the level of

confidence that are 68.27 % cases are included between Xm + s, 95.45 % of the cases are

included between Xm + 2s and 99.73 % of the cases are included between Xm + 3s.

4.2.4 RESPONSE VARIABLES

There were three response variables that were measured as per the scope of this thesis. This was

the most difficult part of this research work as any single mistake in the measurements could

51

change the results of the study drastically. Utmost care was taken in obtaining the data. The data

acquisition process for the three response variables will be discussed one by one in the following

sections.

4.2.4.1 KERF WIDTH

Kerf width is the width between each side of the cut as shown in the figure 4.6. The kerf width

was measured on a Universal Profile Projector (UK) of the accuracy 0.001 mm. It was measured

at three different points of the kerf width in each run and each run was repeated three times.

Since there were two replicates for each run, therefore total of 09 readings for each run were

obtained. These readings were then divided into top, side and bottom measurements for the

purpose of analysis. The combined data for kerf width along with all the 09 readings for each

experiment is shown in table 4.3a.

Figure 4.6: Schematic presentation of Kerf Width

52

Table 4.3a:- MEASUREMENT OF KERF WIDTH (KF) EXP.NO.

KERF WIDTH (KF) µm 1st Run 2nd Run 3rd Run

Top Bottom Side Top Bottom Side Top Bottom Side 1. 333 332 334 337 336 334 338 335 336 2. 321 324 322 322 322 319 323 321 324 3. 313 310 312 312 313 313 312 313 310 4. 314 316 317 316 315 315 314 315 313 5. 323 321 323 323 323 320 320 323 322 6. 336 336 337 338 337 338 338 336 337 7. 333 334 332 333 333 335 332 333 332 8. 341 339 341 340 342 342 343 340 341 9. 332 331 331 332 330 331 330 331 331 10. 349 349 349 353 352 354 352 352 349 11. 332 331 332 332 330 331 330 331 330 12. 316 315 314 315 313 312 314 314 313 13. 307 307 308 305 304 304 307 306 306 14. 296 298 297 299 297 296 299 301 299 15. 317 315 314 318 319 318 317 317 318 16. 327 325 324 326 325 327 328 327 325 17. 322 323 324 324 323 322 324 322 323 18. 328 331 330 330 329 330 331 331 330 19. 336 335 336 336 337 336 336 335 337 20. 322 321 320 318 322 320 319 319 319 21. 298 301 301 299 299 298 298 299 298 22. 338 339 339 338 339 337 339 337 336 23. 325 327 326 326 327 327 325 325 326 24. 343 345 344 346 344 345 346 345 347 25. 315 314 316 314 314 315 316 316 315 26. 313 311 312 311 312 313 313 312 311 27. 308 306 307 307 306 307 308 305 309

53

The obtained data were then re-grouped according to the location of measurement. Then it was

analyzed for its confidence levels at the three points of measurements that were top, side and

bottom as shown in tables 4.3b, 4.3c and 4.3d

Table 4.3b KERF WIDTH MEASURED AT TOP (µm) EXP. NO.

MEASUREMENTS CALCULATION CONFIDENCE INTERVAL

CONFIDENCE LEVEL % 1 2 3 MEAN STD. DEV

1. 333 337 338 336.00 2.646 2.0962 96 2. 321 322 323 322.00 1.000 1.0107 92 3. 313 312 312 312.33 0.577 0.6841 96 4. 314 316 314 314.67 1.155 1.3695 96 5. 323 323 320 322.00 1.732 2.0536 96 6. 336 338 338 337.33 1.155 1.3695 96 7. 333 333 332 332.67 0.577 0.7229 97 8. 341 340 343 341.33 1.528 1.7290 95 9. 332 332 330 331.33 1.155 1.3695 96 10. 349 353 352 351.33 2.082 2.3559 95 11. 332 332 330 331.33 1.155 1.3695 96 12. 316 315 314 315.00 1.000 1.0858 94 13. 307 305 307 306.33 1.155 1.3695 96 14. 296 299 299 298.00 1.732 2.0536 96 15. 317 318 317 317.33 0.577 0.6841 96 16. 327 326 328 327.00 1.000 1.0461 93 17. 322 324 324 323.33 1.155 1.3695 96 18. 328 330 331 329.67 1.528 1.7290 95 19. 336 336 336 336.00 - - - 20. 322 318 319 319.67 2.082 2.3559 95 21. 298 299 298 298.33 0.577 0.6841 96 22. 338 338 339 338.33 0.577 0.6841 96 23. 325 326 325 325.33 0.577 0.6841 96 24. 343 346 346 345.00 1.732 2.0536 96 25. 315 314 316 315.00 1.000 1.0107 92 26. 313 311 313 312.33 1.155 1.3695 96 27. 308 307 308 307.67 0.577 0.6841 96

respectively. In these three cases the confidence level was observed to be in the range of 92-97

%. The main advantage of this analysis was that the arithmetic means of the kerf width at top,

side and bottom were calculated as 323.95, 323.70 and 323.68 µm respectively. This shows that

54

wire wear has been responsible for this change in kerf width as the wire is fresh at the top and

wears as it travels down. This may imply that this wear has contributed to the reduction in the

kerf as viewed from the top of the workpiece to the bottom.

Table 4.3c KERF WIDTH MEASURED AT SIDE (µm) EXP. NO.

MEASUREMENTS CALCULATION CONFIDENCE

INTERVAL

CONFIDENCE

LEVEL % 1 2 3 MEAN STD. DEV

1. 334 334 336 334.67 1.155 1.3695 96 2. 322 319 324 321.67 2.517 2.7332 94 3. 312 313 310 311.67 1.528 1.7291 95 4. 317 315 313 315.00 2.000 2.0215 92 5. 323 320 322 321.67 1.528 1.7291 95 6. 337 338 337 337.33 0.577 0.6842 96 7. 332 335 332 333.00 1.732 2.0537 96 8. 341 342 341 341.33 0.577 0.6842 96 9. 331 331 331 331.00 - - - 10. 349 354 349 350.67 2.887 3.4232 96 11. 332 331 330 331.00 1.000 1.0108 92 12. 314 312 313 313.00 1.000 1.0461 93 13. 308 304 306 306.00 2.000 2.0215 92 14. 297 296 299 297.33 1.528 1.7291 95 15. 314 318 318 316.67 2.309 2.7379 96 16. 324 327 325 325.33 1.528 1.7291 95 17. 324 322 323 323.00 1.000 1.0461 93 18. 330 330 330 330.00 - - - 19. 336 336 337 336.33 0.577 0.6842 96 20. 320 320 319 319.67 0.577 0.6842 96 21. 301 298 298 299.00 1.732 2.0537 96 22. 339 337 336 337.33 1.528 1.7291 95 23. 326 327 326 326.33 0.577 0.6842 96 24. 344 345 347 345.33 1.528 1.7291 95 25. 316 315 315 315.33 0.577 0.6842 96 26. 312 313 311 312.00 1.000 1.0108 92 27. 307 307 309 307.67 1.155 1.3695 96

55

Table 4.3d KERF WIDTH MEASURED AT BOTTOM (µm) EXP. NO.

MEASUREMENTS CALCULATION CONFIDENCE INTERVAL

CONFIDENCE LEVEL % 1 2 3 MEAN STD. DEV

1. 332 336 335 334.33 2.082 2.3560 95 2. 324 322 321 322.33 1.528 1.7291 95 3. 310 313 313 312.00 1.732 2.0537 96 4. 316 315 315 315.33 0.577 0.6842 96 5. 321 323 323 322.33 1.155 1.3695 96 6. 336 337 336 336.33 0.577 0.6842 96 7. 334 333 333 333.33 0.577 0.6842 96 8. 339 342 340 340.33 1.528 1.7291 95 9. 331 330 331 330.67 0.577 0.6842 96 10. 349 352 352 351.00 1.732 2.0537 96 11. 331 330 331 330.67 0.577 0.6842 96 12. 315 313 314 314.00 1.000 1.0108 92 13. 307 304 306 305.67 1.528 1.7291 95 14. 298 297 301 298.67 2.082 2.3560 95 15. 315 319 317 317.00 2.000 2.0215 92 16. 325 325 327 325.67 1.155 1.3695 96 17. 323 323 322 322.67 0.577 0.6842 96 18. 331 329 331 330.33 1.155 1.3695 96 19. 335 337 335 335.67 1.155 1.3695 96 20. 321 322 319 320.67 1.528 1.7291 95 21. 301 299 299 299.67 1.155 1.3695 96 22. 339 339 337 338.33 1.155 1.3695 96 23. 327 327 325 326.33 1.155 1.3695 96 24. 345 344 345 344.67 0.577 0.6842 96 25. 314 314 316 314.67 1.155 1.3695 96 26. 311 312 312 311.67 0.577 0.6842 96 27. 306 306 305 305.67 0.577 0.6842 96

4.2.4.2 SURFACE ROUGHNESS (Ra)

Surface roughness average (Ra), also known as arithmetic average is rated as the arithmetic

average deviation of the surface valleys and peaks expressed in micro inches or micro meters

56

[139]. ISO standards use the term CLA (Center Line Average). Both are interpreted identical.

The overall measurements of the surface roughness are given in table 4.4a. The surface

roughness characteristic was measured in terms of CLA values (Ra). The measurement of other

surface finish characteristics like Rt and Rz were not undertaken because their trends of

Table 4.4a:- MEASUREMENT OF SURFACE FINISH (Ra) EXP.NO.

SURFACE FINISH (Ra) µm 1st Run 2nd Run 3rd Run

Top Centre Bottom Top Centre Bottom Top Centre Bottom 1. 1.16 1.24 1.1 1.3 1.38 1.38 1.44 1.42 1.42 2. 1.38 1.42 1.4 1.34 1.36 1.36 1.12 1.2 1.18 3. 1.3 1.28 1.28 1.22 1.24 1.24 1.2 1.24 1.2 4. 1.76 1.82 1.76 1.58 1.52 1.5 1.62 1.56 1.66 5. 1.56 1.7 1.64 1.54 1.58 1.6 1.54 1.38 1.48 6. 1.42 1.46 1.38 1.4 1.44 1.4 1.5 1.38 1.4 7. 1.9 1.96 1.78 1.94 1.84 1.9 1.78 1.6 1.6 8. 1.48 1.44 1.46 1.62 1.64 1.62 1.7 1.68 1.7 9. 1.78 1.8 1.76 1.72 1.62 1.7 1.72 1.78 1.84 10 1.44 1.48 1.48 1.5 1.5 1.56 1.5 1.52 1.54 11 1.56 1.54 1.56 1.58 1.54 1.58 1.54 1.52 1.54 12 1.78 1.7 1.68 1.6 1.99 1.76 1.66 1.6 1.62 13 1.42 1.54 1.42 1.44 1.48 1.44 1.7 1.48 1.68 14 1.6 1.62 1.72 1.52 1.52 1.52 1.5 1.58 1.42 15 1.48 1.4 1.54 1.48 1.54 1.46 1.48 1.44 1.36 16 1.3 1.24 1.28 1.3 1.36 1.34 1.4 1.34 1.36 17 1.56 1.44 1.54 1.28 1.3 1.26 1.56 1.46 1.4 18 1.52 1.52 1.52 1.62 1.52 1.4 1.36 1.42 1.38 19 1.46 1.38 1.4 1.84 1.76 1.76 1.6 1.58 1.58 20 1.46 1.58 1.48 1.6 1.1 1.6 1.44 1.42 1.42 21 1.44 1.46 1.46 1.36 1.38 1.4 1.54 1.54 1.52 22 1.5 1.54 1.5 1.5 1.52 1.54 1.5 1.4 1.4 23 1.66 1.66 1.66 1.6 1.66 1.64 1.54 1.66 1.56 24 1.66 1.44 1.64 1.32 1.32 1.32 1.36 1.36 1.5 25 1.24 1.3 1.26 1.36 1.38 1.32 1.3 1.42 1.34 26 1.22 1.2 1.16 1.38 1.38 1.34 1.4 1.4 1.4 27 1.2 1.24 1.22 1.2 1.24 1.18 1.3 1.32 1.3

57

variation are almost similar as with Ra in wire EDM process. Ra was measured with surface

texture meter by Taylor Hobson, UK. The surface finish was measured at the set cut off length

and evaluation length of 0.8 mm and 4.0 mm respectively. Ra was measured three times for each

of the three runs and an average was obtained.

The confidence levels for Ra measurements, as in the case of kerf, at top, center and bottom were

also calculated separately to assess the accuracy of the data and are given at tables 4.4b, 4.4c and

4.4d respectively. The confidence generally falls in the range of 91-98 %. The overall averages

for top, center and bottom have shown a very little difference but still these are consistent with

the available literature. The averages of Ra are 1.489, 1.484 and 1.481 microns for top, center

and bottom. This again can be attributed to the tool wire wear when the wire travels down during

cutting, and as it wears, the spark intensity decreases thereby reducing Ra at the bottom.

58

Table 4.4b Ra MEASURED AT TOP EXP. NO.

MEASUREMENT (µm) CALCULATION CONFIDENCE INTERVAL

CONFIDENCELEVEL % 1 2 3 MEAN STD. DEV

1. 1.16 1.3 1.44 1.30 0.140 0.1415 92 2. 1.38 1.34 1.12 1.28 0.140 0.1660 96 3. 1.3 1.22 1.2 1.24 0.053 0.0628 96 4. 1.76 1.58 1.62 1.65 0.095 0.1126 96 5. 1.56 1.54 1.54 1.55 0.012 0.0114 90 6. 1.42 1.4 1.5 1.44 0.053 0.0628 94 7. 1.9 1.94 1.78 1.87 0.083 0.0901 94 8. 1.48 1.62 1.7 1.60 0.111 0.1205 94 9. 1.78 1.72 1.72 1.74 0.035 0.0415 96 10. 1.44 1.5 1.5 1.48 0.035 0.0415 96 11. 1.56 1.58 1.54 1.56 0.020 0.0209 93 12. 1.78 1.6 1.66 1.68 0.092 0.1041 95 13. 1.42 1.44 1.7 1.52 0.156 0.1850 96 14. 1.6 1.52 1.5 1.54 0.053 0.0628 96 15. 1.48 1.48 1.48 1.48 - - - 16. 1.3 1.3 1.4 1.33 0.058 0.0727 97 17. 1.56 1.28 1.56 1.47 0.162 0.1921 96 18. 1.52 1.62 1.36 1.50 0.131 0.1422 94 19. 1.46 1.84 1.6 1.63 0.192 0.2173 95 20. 1.46 1.6 1.44 1.50 0.087 0.1032 96 21. 1.44 1.36 1.54 1.45 0.090 0.0910 92 22. 1.5 1.5 1.5 1.50 - - - 23. 1.66 1.6 1.54 1.60 0.060 0.0606 92 24. 1.66 1.32 1.36 1.45 0.186 0.2105 95 25. 1.24 1.36 1.3 1.30 0.060 0.0606 92 26. 1.22 1.38 1.4 1.33 0.099 0.1120 95 27. 1.2 1.2 1.3 1.23 0.058 0.0727 97

59

Table 4.4c Ra MEASURED AT CENTER EXP. NO.

MEASUREMENTS (µm)

CALCULATION CONFIDENCE INTERVAL

CONFIDENCE LEVEL %

1 2 3 MEAN STD. DEV 1. 1.24 1.38 1.42 1.35 0.095 0.1126 96 2. 1.42 1.36 1.2 1.33 0.114 0.1352 96 3. 1.28 1.24 1.24 1.25 0.023 0.0309 98 4. 1.82 1.52 1.56 1.63 0.163 0.1933 96 5. 1.7 1.58 1.38 1.55 0.162 0.1759 94 6. 1.46 1.44 1.38 1.43 0.042 0.0526 97 7. 1.96 1.84 1.6 1.80 0.183 0.2071 95 8. 1.44 1.64 1.68 1.59 0.129 0.1530 96 9. 1.8 1.62 1.78 1.73 0.099 0.1120 95 10. 1.48 1.5 1.52 1.50 0.020 0.0209 93 11. 1.54 1.54 1.52 1.53 0.012 0.0117 91 12. 1.7 1.99 1.6 1.76 0.203 0.2407 96 13. 1.54 1.48 1.48 1.50 0.035 0.0415 96 14. 1.62 1.52 1.58 1.57 0.050 0.0505 92 15. 1.4 1.54 1.44 1.46 0.072 0.0815 95 16. 1.24 1.36 1.34 1.31 0.064 0.0724 95 17. 1.44 1.3 1.46 1.40 0.087 0.1032 96 18. 1.52 1.52 1.42 1.49 0.058 0.0727 97 19. 1.38 1.76 1.58 1.57 0.190 0.1920 92 20. 1.58 1.1 1.42 1.37 0.244 0.2761 95 21. 1.46 1.38 1.54 1.46 0.080 0.0809 92 22. 1.54 1.52 1.4 1.49 0.076 0.0901 96 23. 1.66 1.66 1.66 1.66 - - - 24. 1.44 1.32 1.36 1.37 0.061 0.0723 96 25. 1.3 1.38 1.42 1.37 0.061 0.0723 96 26. 1.2 1.38 1.4 1.33 0.110 0.1112 92 27. 1.24 1.24 1.32 1.27 0.046 0.0521 95

60

Table 4.4d Ra MEASURED AT BOTTOM EXP. NO.

MEASUREMENTS (µm) CALCULATION CONFIDENCE

INTERVAL

CONFIDENCE

LEVEL % 1 2 3 MEAN STD. DEV

1. 1.1 1.38 1.42 1.30 0.174 0.2063 96 2. 1.4 1.36 1.18 1.31 0.117 0.1324 95 3. 1.28 1.24 1.2 1.24 0.040 0.0418 93 4. 1.76 1.5 1.66 1.64 0.131 0.1422 94 5. 1.64 1.6 1.48 1.57 0.083 0.0901 94 6. 1.38 1.4 1.4 1.39 0.012 0.0130 94 7. 1.78 1.9 1.6 1.76 0.151 0.1640 94 8. 1.46 1.62 1.7 1.59 0.122 0.1381 95 9. 1.76 1.7 1.84 1.77 0.070 0.0708 92 10. 1.48 1.56 1.54 1.53 0.042 0.0526 97 11. 1.56 1.58 1.54 1.56 0.020 0.0209 93 12. 1.68 1.76 1.62 1.69 0.070 0.0708 92 13. 1.42 1.44 1.68 1.51 0.145 0.1719 96 14. 1.72 1.52 1.42 1.55 0.153 0.1731 95 15. 1.54 1.46 1.36 1.45 0.090 0.0910 92 16. 1.28 1.34 1.36 1.33 0.042 0.0526 97 17. 1.54 1.26 1.4 1.40 0.140 0.1415 92 18. 1.52 1.4 1.38 1.43 0.076 0.0901 96 19. 1.4 1.76 1.58 1.58 0.180 0.1819 92 20. 1.48 1.6 1.42 1.50 0.092 0.1041 95 21. 1.46 1.4 1.52 1.46 0.060 0.0606 92 22. 1.5 1.54 1.4 1.48 0.072 0.0815 95 23. 1.66 1.64 1.56 1.62 0.053 0.0628 96 24. 1.64 1.32 1.5 1.49 0.160 0.1737 94 25. 1.26 1.32 1.34 1.31 0.042 0.0526 97 26. 1.16 1.34 1.4 1.30 0.125 0.1414 95 27. 1.22 1.18 1.3 1.23 0.061 0.0723 96

61

4.2.4.3 MATERIAL REMOVAL RATE (MRR)

The calculations of MRR are given in table 4.5. The material removal rate was calculated by

measuring difference in weights that is weighing the workpieces before and

Table 4.5:- MEASUREMENT OF MATERIAL REMOVAL RATE (MRR)

EXP.NO.

MATERIAL REMOVAL RATE(MRR) 1st Run g/min

2nd Run g/min

3rd Run g/min

MEAN g/min

MRR mm3/min

1. 0.0383 0.0380 0.0398 0.0387 2.7369 2. 0.0624 0.0614 0.0628 0.0622 4.3973 3. 0.0214 0.0211 0.0208 0.0211 1.489 4. 0.1034 0.1031 0.1037 0.1034 7.3108 5. 0.065 0.0683 0.0677 0.067 4.7340 6. 0.0454 0.046 0.0457 0.0457 3.2319 7. 0.1097 0.1087 0.1098 0.1094 7.7297 8. 0.0895 0.0903 0.0899 0.0899 6.3535 9. 0.0712 0.0727 0.0712 0.0717 5.0706 10. 0.0923 0.0929 0.0929 0.0927 6.5523 11. 0.0669 0.0656 0.0667 0.0664 4.6899 12. 0.0918 0.0899 0.0919 0.0912 6.4456 13. 0.0641 0.0656 0.0641 0.0646 4.5649 14. 0.0489 0.0479 0.0484 0.0484 3.4235 15. 0.0561 0.0566 0.0571 0.0566 4.0003 16. 0.0551 0.0572 0.0554 0.0559 3.9489 17. 0.0233 0.0242 0.0239 0.0238 1.6787 18. 0.0568 0.0558 0.0557 0.0561 3.9655 19. 0.0753 0.075 0.0747 0.075 5.3004 20. 0.0726 0.0716 0.0718 0.072 5.0862 21. 0.0616 0.0612 0.0605 0.0611 4.3195 22. 0.0517 0.05 0.0513 0.051 3.6028 23. 0.0771 0.0774 0.0792 0.0779 5.5052 24. 0.0511 0.0517 0.0523 0.0517 3.6514 25. 0.0462 0.0468 0.0477 0.0469 3.3139 26. 0.0539 0.0537 0.0529 0.0535 3.7811 27. 0.0432 0.0425 0.0451 0.0436 3.0792

62

after the cut. The cut completion time was directly obtained from the machine computer. After

calculating material removal rate for three readings of the same run an arithmetic mean was

calculated for the sake of obtaining confidence levels and onward analysis.

In the past 137, the material removal rate (MRR) has been calculated by measuring kerf width,

length, thickness and cutting time or feed rate. In this study, to be more accurate, the material

removal rate was calculated by weighing the samples prior and after machining. The samples

were weighed on a weighing machine of the accuracy 0.001 grams. This was repeated three

times for each sample and the average weight was obtained for a better accuracy. This difference

of weights was converted to volume by using density. This volume was then divided by cutting

time and hence the material removal rate (MRR) was calculated in terms of volumetric removal

rate.

Hence, the data for three response variables i.e KF, Ra and MRR was collected and some

validation was carried out for its accuracy in this phase and the data so obtained was ready for

statistical analyses that will be discussed in the following sections.

4.3 RESULTS AND ANALYSES

Statistical analyses/treatment has been divided into three phases. In the first phase S/N ratio has

been calculated for three responses. In this S/N ratio for MRR has been calculated keeping in

mind the larger-the better type argument. Likewise the S/N ratio for KF and Ra has been

calculated for the smaller-the better type argument. In second phase ANOVA has been carried

63

out for determining the significant factors for each machining response. In third or final phase

optimization of the machining responses has been carried out.

4.3.1 ANALYSIS OF SIGNAL TO NOISE RATIO

According to the Taguchi technique 126 as discussed in chapter 3, the characteristic that a

larger value presents the better machining performance, such as material removal rate (MRR), is

called larger-the-better type problem. On the contrary, the characteristic that smaller value

indicates better machining performance, for example surface roughness (Ra) or kerf width (KF),

is termed as smaller-the-better type problem. η, according to equations 3.8 and 3.9 for both the

cases can be defined by the following equations.

(A) Where larger is the better type case:

S/N ratio = η = -10 log[1/n∑1/yi2] ………….(4.2)

(B) Where smaller is the better type case:

S/N ratio = η = -10 log[1/n∑yi2] ……….…...(4.3)

Where, n is the number of readings/replicates and yi is the i-th reading.

Based on this methodology, equation (4.2) has been applied to material removal rate (MRR)

whereas equation (4.3) has been applied to kerf width (KF) and surface roughness (Ra). Signal to

noise ratio has been calculated for KF, Ra and MRR as η1, η2 and η3 respectively. The

experimental results are summarized in table 4.6.

64

Table 4.6:- CALCULATED S/N RATIO (dB) Exp. No. KF(µm) Ra (µm) MRRmm3/min KF η1 Ra η2 MRR η3

1. 335 1.316 2.7369 -50.5009 -2.38219 8.745 2. 322 1.307 4.3973 -50.1571 -2.3233 12.864 3. 312 1.244 1.489 -49.8831 -1.89951 3.458 4. 315 1.642 7.3108 -49.9662 -4.30864 17.279 5. 322 1.558 4.7340 -50.1571 -3.85011 13.505 6. 337 1.420 3.2319 -50.5526 -3.04577 10.189 7. 333 1.811 7.7297 -50.4489 -5.1589 17.763 8. 341 1.593 6.3535 -50.6551 -4.04613 16.059 9. 331 1.747 5.0706 -50.3966 -4.8442 14.101 10. 351 1.502 6.5523 -50.9061 -3.53468 16.328 11. 331 1.551 4.6899 -50.3966 -3.81286 13.423 12. 314 1.711 6.4456 -49.9386 -4.66556 16.185 13. 306 1.511 4.5649 -49.7144 -3.58593 13.189 14. 298 1.556 3.4235 -49.4843 -3.83771 10.689 15. 317 1.464 4.0003 -50.0212 -3.31346 12.042 16. 326 1.324 3.9489 -50.2644 -2.44067 11.93 17. 323 1.422 1.6787 -50.1841 -3.05935 4.499 18. 330 1.473 3.9655 -50.3703 -3.36602 11.966 19. 336 1.596 5.3004 -50.5268 -4.05824 14.486 20. 320 1.456 5.0862 -50.103 -3.26058 14.128 21. 299 1.456 4.3195 -49.5134 -3.26058 12.709 22. 338 1.489 3.6028 -50.5783 -3.45725 11.133 23. 326 1.627 5.5052 -50.2644 -4.22597 14.816 24. 345 1.436 3.6514 -50.7564 -3.1404 11.249 25. 315 1.324 3.3139 -49.9662 -2.44067 10.407 26. 312 1.320 3.7811 -49.8831 -2.41148 11.552 27. 307 1.244 3.0792 -49.7428 -1.89951 9.769

In the present study, as the experimental design is orthogonal, the affect of each parameter at

each of the three levels can be obtained. For example, the mean S/N ratio for the thickness(A) at

levels 1, 2, and 3 can be calculated by taking the average of the S/N ratios for the experiments 1-

9, 10-18 and 19-27 respectively. The mean S/N ratio for all the other process parameters can be

obtained in the similar fashion for a particular response.

65

4.3.1.1 KERF WIDTH (KF)

The mean S/N ratios for each level of the process parameters are summarized in table 4.7. These

types of tables are known as the mean S/N response tables. The tables can provide an idea about

significance of process parameters on a particular machining response. That is possible when we

subtract the lower mean S/N ratio value from the higher one pertaining to any level of that

process parameter. The highest is the difference; the more is significance. From the table 4.7,

focusing the difference, if we proceed as per descending order we get factors C, G, B, D, A, F, E

and H. Now we have an idea that the factors that are expected to be significant will follow the

same order. In ANOVA it will become clear that BCDG are significant or not.

Table 4.7:- S/N response table for Kerf width (KF)

Factor Code Process parameters Mean S/N ratio (dB) Level 1 Level 2 Level 3 Max-Min

A TH Thickness -50.302 -50.1733 -50.1123 0.1897

B OV Open Voltage -50.3288 -50.1677 -50.096 0.2328

C ONT On Time -49.9281 -50.182 -50.4823 0.5542

D OFT Off Time -50.3191 -50.1428 -50.1306 0.1905

E SV Servo Voltage -50.1663 -50.2074 -50.2753 0.1090

F WF Wire Feed Velocity -50.1104 -50.2634 -50.2186 0.1530

G WT Wire Tension -50.4429 -50.1584 -49.9912 0.4517

H WL Dielectric Pressure -50.2022 -50.1528 -50.2374 0.0846

Overall Mean -50.1996

The affects of the levels of the control factor’s mean S/N ratio on the subject response have been

presented by graphs in Figures 4.7. From these graphs, as discussed earlier, it becomes easier to

get an overview and better understanding of the process. It is another way of presenting mean

66

Figure 4.7: Mean S/N ratio graph for KF

S/N ratios. The affect of each process parameter can be seen on individual basis assuming that all

other process parameters were set on their mean values. There are three values for each process

parameter; therefore there is an option to make a non linear equation for each process parameter

but that is only possible when we know exactly about the significant factors. So this will be

done, although not a part of Taguchi method, after ANOVA has been carried out.

4.3.1.2 SURFACE ROUGHNESS (Ra)

The mean S/N ratios for each level of the process parameters are summarized for Ra in table 4.8.

Here it can be seen that the maximum difference between the S/N ratios of any two of the three

levels of process parameters exists in a descending order of C, B, E, A, G, H, F and D. Hence it

is an indicator or estimator for calculating the significant factors in this case. More is the

difference; significant will be the factor in consideration.

A. THICKNESS B. OPEN

VOLTAGE C. ON TIME D. OFF TIME E. SERVO

VOLTAGE F. WIRE FEED G. WIRE

TENSION H. DIELECTRIC

PRESSURE

67

ANOVA will reveal that where the line should be drawn. The affects of the levels of the control

factor’s mean S/N ratio on the Ra have been presented by graphs in Figures 4.8.

Table 4.8:- S/N response table for Surface roughness (Ra) Factor Code Process parameters Mean S/N ratio (dB)

Level 1 Level 2 Level 3 Max-Min A TH Thickness -3.53986 -3.51292 -3.1283 0.41

B OV Open Voltage -2.92163 -3.32992 -3.92953 1.01

C ONT On Time -2.67708 -3.40555 -4.09844 1.42

D OFT Off Time -3.48524 -3.42528 -3.27056 0.21

E SV Servo Voltage -3.67698 -3.38395 -3.12015 0.56

F WF Wire Feed Velocity -3.4289 -3.5023 -3.24987 0.25

G WT Wire Tension -3.2213 -3.4287 -3.53108 0.31

H WL Dielectric Pressure -3.22615 -3.47898 -3.47594 0.25

Overall Mean -3.39369

Figure 4.8: Mean S/N ratio graph for Ra

A. THICKNESS B. OPEN


VOLTAGE F. WIRE FEED G. WIRE

TENSION H. DIELECTRIC

PRESSURE

68

4.3.1.3 MATERIAL REMOVAL RATE (MRR)

The S/N ratio graphs for MRR are presented in Figure 4.9 and the values are given at table 4.9.

Table 4.9:- S/N response table for Material Removal Rate (MRR) Factor Code Process parameters Mean S/N ratio (dB)

Level 1 Level 2 Level 3 Max-Min A TH Thickness 12.6626 12.2501 12.2499 0.4127

B OV Open Voltage 10.0733 13.1819 13.9073 3.8340

C ONT On Time 10.3017 12.2990 14.5619 4.2602

D OFT Off Time 13.4733 12.3928 11.2964 2.1769

E SV Servo Voltage 13.8307 13.0669 10.2650 3.5657

F WF Wire Feed Velocity 11.7917 13.1830 12.1879 1.3913

G WT Wire Tension 11.6832 13.0947 12.3847 1.4114

H WL Dielectric Pressure 12.0919 12.7828 12.2879 0.6909

Overall Mean 12.38752

Like the tables, these figures also give an empirical picture of the affects of process parameters

or control factors on the process responses. If figure 4.7 is closely observed that represents the

mean S/N ratios for material removal rate (MRR), the slope for thickness (Factor A:TH) is

Figure 4.9: Mean S/N ratio graph for MRR

A. THICKNESS B. OPEN VOLTAGE C. ON TIME D. OFF TIME E. SERVO VOLTAGE F. WIRE FEED G. WIRE TENSION H. DIELECTRIC PRESSURE

69

almost negligible and is depicting that it is not so significant. The slopes are greater for open

voltage (Factor B:OV), pulse on time (Factor C:ONT),pulse off time (Factor D:OFT), servo

voltage (Factor E:SV) give an idea about their significance. The slopes for wire feed velocity

(Factor F:WF), wire tension (Factor G:WT) and dielectric pressure (Factor H:WL) also seem

negligible and hence it can be concluded that they are not significant. After the S/N ratio has

been obtained, the next step of the data analysis is to identify the process parameters that have a

significant effect on each of these machining responses. This has been achieved by performing

ANOVA.

4.3.2 ANOVA FOR MATERIAL REMOVAL RATE (MRR), KERF WIDTH (KF), AND

SURFACE ROUGHNESS (Ra)

The results from ANOVA have been shown in tables 4.10-4.12. In case of material removal rate,

the results show that the process parameters such as, open voltage (B), pulse on time (C), pulse

Table 4.10:- Results of ANOVA-Kerf Width (KF) Source Sum of

quares df Mean

Square F-Value Prob > F Significance

A-TH 138.89 1 138.89 2.88 0.1069 Not significant

B-OV 320.89 1 320.89 6.65 0.0189 Significant

C-ONT 1922.00 1 1922.00 39.86 0.0001 Significant

D-OFT 220.50 1 220.50 4.57 0.0465 Significant

E-SV 20.06 1 20.06 0.42 0.5271 Not significant

F-WF 68.06 1 68.06 1.41 0.2503 Not significant

G-WT 1283.56 1 1283.56 26.62 0.0001 Significant

H-WL 6.72 1 6.72 0.14 0.7132 Not significant

Error 868.00 18 48.22

Total 4848.67 26

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Table 4.11:- Results of ANOVA-Surface Roughness (Ra) Source Sum of

quares df Mean


A-TH 0.027 1 0.027 4.47 0.0486 Significant

B-OV 0.13 1 0.13 22.54 0.0002 Significant


D-OFT 5.689 1 5.689 0.96 0.3404 Not significant

E-SV 0.012 1 0.012 2.08 0.1665 Not significant


G-WT 0.016 1 0.016 2.75 0.1143 Not significant


Error 0.11 18 5.931

Total 0.58 26

Table 4.12:- Results of ANOVA-Material Removal Rate (MRR) Source Sum of

quares df Mean


A-TH 1.628 1 1.628 3.137 0.093 Not significant

B-OV 12.287 1 12.287 23.681 0.000 Significant



E-SV 12.104 1 12.104 23.329 0.000 Significant


G-WT 0.730 1 0.73 1.406 0.251 Not significant


Error 9.339 18 0.519 0.000

Total 61.339 26

off time (D) and servo voltage (E) are significant whereas thickness (A), wire feed velocity (F),

wire tension (G) and dielectric pressure (H) are not significant.

71

4.4 DISCUSSION

After carrying out ANOVA we can clearly see that the slope for significant factors is on the

larger side as compared to non-significant factors. Now affects of process parameters on the

machining responses and their possible reasons will be discussed one by one keeping emphasis

on significant factors in particular.

4.4.1 KERF WIDTH (KF)

ANOVA for kerf width shows that the process parameters as open voltage (B), pulse on time

(C), pulse off time (D) and wire tension (G) are significant whereas thickness (A), servo voltage

(E), wire feed velocity (F) and dielectric pressure (H) are not significant. The graphs, at the

Figure 4.5, depict that the kerf width increases with increase in pulse on time (C), whereas a

decrease is experienced with the increase in thickness (A), open voltage (B), pulse off time (D)

and wire tension (G). In a study, Nihat et al. 66 selected wire speed, pulse duration, open circuit

voltage, and flushing pressure to investigate their influence on kerf. They found out that the most

significant parameters were the pulse duration and open circuit voltage. The di-electric flushing

pressure and wire speed were not significant. W.Y. Peng and Y.S. Liao 69 in their study of kerf

found out that the kerf size depends upon the on-time, off-time and wire diameter. They also

proposed to set the servo voltage to a higher value to avoid wire breakage due to the accidental

workpiece and wire contact. Most of the results of the present work are non contradictory to the

available literature; however the process parameters like servo voltage (E) and thickness (A) are

also discussed here regardless of their significance.

72

Now, if we look at Figures 4.7, the decrease in kerf width with the increase in thickness (A) can

be attributed to the fact that the spark energy is uniformly distributed over the engaged length of

cutting wire 140,141. If this length increases, it will decrease the energy per unit length/area of

the engaged wire. Hence, this decrease in energy per unit engaged length of cutting wire has

caused less material removal in cutting direction as well as at its right angles on both sides and

consequently a decrease in the kerf width has been experienced.

Kerf width increases with increase in pulse on time (C). Spark is responsible for material erosion

in Wire EDM. Since the time of spark increases, it will increase the material removal in all

directions. So kerf width will increase as a result. The decrease in kerf width with the increase in

pulse off time (D) is understandable. Off time determines the frequency of the spark. More is the

off time, the less is the frequency. Reduction in frequency reduces the material removal and

hence the kerf width. The servo voltage (E) determines the gap between the cutting wire and the

workpiece and they are directly proportional to each other. So increase in the servo voltage has

caused increase in the gap and this has ultimately caused increase in the kerf width. Wire tension

(G) is one of the significant factors and has shown inverse relation with the kerf width. During

cutting, the wire continuously vibrates between the two faces of the machined groove136. If the

wire does not stay at its ideal position, it may lead to production of unwanted sparks on the sides

of kerf. The increase in wire tension has caused this vibration to reduce and as a result the kerf

width has also decreased.

4.4.2 SURFACE ROUGHNESS (Ra)

ANOVA results in case of surface roughness are given at table 4.8 and they indicate that the

process parameters as thickness (A), open voltage (B) and pulse on time (C) are significant while

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pulse off time (D), servo voltage (E), wire feed velocity (F), wire tension (G) and dielectric

pressure (H) are not significant. The affects of process parameters on response have been shown

by graphs in figure 4.8. The graphs reveal that the increase in the process parameters values like

thickness (A) has caused the surface roughness to reduce and it has increased with the increase in

open voltage (B) and pulse on time (C). G.A. Alekseyev et.al [50] proved in their study that the

process is mainly controlled by current intensity, on-time and off-time. They found out that a

little change in any of these control variables affected surface roughness. They found them the

most important factors. Huang et al. [57] results of the experiment show that the pulse on-time

affects the machining rate and surface finish. Another study [63] showed that the increase in wire

speed, pulse duration and the open circuit voltage caused an increase in the surface roughness.

The significant control variables established in this study are in consistence with the results of all

these studies.

According to few researchers, for obtaining a better surface finish the discharge energy needs to

be reduced [51]. Here, it has been observed that the affect of thickness (A) on surface roughness

is significant. The rationale behind this can be better understood by considering the affect of

spark energy concentration or spark energy distribution140,141. When the thickness of

workpiece was increased, as discussed earlier, the spark energy per unit length/area of the

engaged cutting wire decreased. Hence this loss of energy converted the process from rough

cutting to moderate or fine cutting. As a result a decrease in the surface roughness of the thicker

workpiece was experienced. So the surface roughness has drastically reduced with the increase in

thickness.

74

4.4.3 MATERIAL REMOVAL RATE (MRR)

In addition to ANOVA, from the figure 4.9, if each process parameters is observed, it has also

been established that material removal rate (MRR) decreases with increase in thickness (A),

pulse off time (D) and servo voltage (E) whereas it increases with increase in open voltage (B)

and pulse on time (C). The results from ANOVA have been very much consistent with the

available literature. Luo [52] has shown that more energy was required for higher material

removal rate and the energy is directly related to the factors such as open voltage and pulse on-

time. In another study [41] the dielectric flow rate, wire tension, and wire speed were not

significant, while on-time and off-time were significant for material removal rate. Tarng et al.

[56] observed that the open circuit voltage, pulse on time, pulse off time, peak current, and servo

voltage were the critical factors for controlling the material removal rate. Almost the same

results have been obtained in this study also with an addition of wire feed velocity in the

significant control factors. This increase of material removal rate can also be attributed to the fact

that the wire wear affect was minimized by introducing fresh wire in a lesser time slot.

However, form the findings above, an interesting situation arises, that an increase in the

workpiece thickness (A) reduces material removal rate, although in the present set of

experiments the thickness affect has not been significant. Before the execution of the

experimental runs it was kept in mind that the spark energy contained therein the cutting wire is

responsible for material erosion and this material erosion is dependent on the length of the wire

engaged with the workpiece140. It was also understood that the cutting energy is uniformly

distributed along the length of wire141. On the basis of these assumptions, it was expected that

the cutting time for the workpieces with more thickness will be higher as compared to the

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workpieces with less thickness for a constant wire travel in the cutting direction, which proved to

be true in this experiment.

However the amount of removed material has not been in proportion to produce a constant

material removal rate for all three thickness levels. This, in turn, has caused decrease in material

removal rate with increasing thickness. One of the understandable reason behind this decrease in

material removal rate may be the inadequate flushing of waste material form surroundings of the

spark area because due to increase in thickness the kerf height has also increased requiring an

extra amount of flushing dielectric pressure (H) for affective removal of waste material and

subsequent increase in the material removal rate. If so, the material removal rate for particular

spark energy should be constant for the three thickness levels, provided that the dielectric

pressure is compensated.

4.5 OPTIMIZATION OF RESPONSE VARIABLES

The ANOVA for all the three responses provided the significant factors. Now, the optimal level

of a significant control factor is a level with the greater S/N ratio. In this way, using S/N ratio

analysis, the optimal levels of process parameters for each response were calculated and will be

discussed in this section.


For example, significant process parameters for optimizing KF are open voltage (B) at level 3,

pulse on time (C) at level 1, pulse off time (D) at level 3 and wire tension (G) at level 3. For the

factors that are not significant any level can be selected that is thickness (A) at level 3, servo

76

voltage (E) at level 1, wire feed velocity (F) at level 1 and dielectric pressure (H) at level 2. After

the optimal levels of the process parameters have been determined, the next step is to predict and

verify the improvement of S/N ratio. The S/N ratio ‘ηpre.’ can be predicted as follows [142,143]:

ηpre. = ηa + (ηA- ηa) + (ηB- ηa) + (ηC- ηa) + (ηD- ηa) + (ηE- ηa) + (ηF- ηa) +

(ηG- ηa) + (ηH- ηa) …………………………….(4.4)

where, ηa is the overall mean S/N ratio and ηA, ηB, ηC, ηD, ηE, ηF, ηG and ηH are the S/N ratios of

the factors A, B, C, D, E, F, G and H respectively at the optimal levels. The corresponding

responses can be calculated by using Eq. (4.2). Table 4.13 show the comparison of the predicted

and the actual responses obtained after executing optimal runs for kerf width (KF). The increase

in value of the S/N ratio from the initial cutting parameters levels to the optimal cutting

Table 4.13:- Results of conformation experiment for Kerf width Width (KF) Initial Control Factors Optimal Control Factors

Prediction Experiment Level A2 B2 C2 D2 E2 F2 G2 H2 A3 B3 C1 D3 E1 F1 G3 H2 A3 B3 C1 D3 E1 F1 G3 H2

KF (microns) 318.089 291.44 290.56 S/N ratio(dB) -50.051 -49.291 -49.265

Improvement of S/N ratio = 0.786 dB

parameters levels is 0.786 dB for KF. It should be noted that the cutting wire diameter used was

250 microns and the optimized kerf value is around 290 microns. This amply that if the diameter

of the wire is excluded the remaining value is 40 microns on all sides of wire. So, in any fixed

direction the minimum possible cut of 20 microns has been achieved with the help of Taguchi

method in comparison to the initial conditions that could produce about 34 microns. Hence

increase of 0.786 dB can be considered significant in this case.

77


In the similar way, significant process parameters for optimizing Ra are thickness (A) at level 3,

open voltage (B) at level 1 and pulse on time (C) at level 1. Other factors, that is pulse off time

(D) at level 3, servo voltage (E) at level 3, wire feed velocity (F) at level 3, wire tension (G) at

level 1 and dielectric pressure (H) at level 1 have been established. Tables 4.14 shows the

comparison of the predicted and the actual responses obtained after executing optimal runs for

surface roughness. The increase in value of the S/N ratio from the initial cutting parameters

levels to the optimal cutting parameters levels is 2.623 dB for Ra. This increase at the optimal

level is of about 27 % of the value obtained at the initial level.

Table 4.14:- Results of conformation experiment for Surface Roughness (Ra) Initial Control Factors Optimal Control Factors

Prediction Experiment Level A2 B2 C2 D2 E2 F2 G2 H2 A3 B1 C1 D3 E3 F3 G1 H1 A3 B1 C1 D3 E3 F3 G1 H1

Ra (microns) 1.53 1.13 1.11 S/N ratio(dB) -3.682 -1.059 -0.90646



Significant process parameters for optimizing MRR are open voltage (B) at level 3, pulse on time

(C) at level 3 and pulse off time (D) at level 1. For the factors that are not significant any level

can be selected that is thickness (A) at level 1, servo voltage (E) at level 1, wire feed velocity (F)

at level 2, wire tension (G) at level 2 and dielectric pressure (H) at level 2.

Tables 4.15 shows the comparison of the predicted and the actual responses obtained after

executing optimal runs for material removal rate (MRR). The increase in value of the S/N ratio

from the initial cutting parameters levels to the optimal cutting parameters levels is 6.83 dB for

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MRR. In this case the improvement is more than twice of the value at initial level which is quite

significant.

Table 4.15:- Results of conformation experiment for Material Removal Rate (MRR) Initial Control Factors Optimal Control Factors

Prediction Experiment Level A2 B2 C2 D2 E2 F2 G2 H2 A1 B3 C3 D1 E1 F2 G2 H2 A1 B3 C3 D1 E1 F2 G2 H2 MRR

(mm3/Min) 6.22 13.15 13.64

S/N ratio(dB) 15.87 22.38 22.70 Improvement of S/N ratio = 6.83 dB

4.6 RELATIONS AND MATHEMATICAL MODELS FOR APPROXIMATION

In this experimentation, as discussed, 08 control factors were selected. Although the optimization

of all the three machining responses was carried out, the need was felt for developing relations or

mathematical models. The data were used to obtain an estimate of this relationship by choosing

the most suitable equation that fits the curves. These equations stand good for interpolation only

within the ranges defined by the extreme values of the control variables. Coefficient of co-

relation r2 has also been shown to show the strength of relationship. This development of

mathematical models has been carried out only for the variables that were significant in all the

three machining responses. The same has been presented in form of graphs and equations in this

section. These equations, as discussed earlier, can provide best estimates of machining responses

against the control factor settings.


The graphs and models for kerf width are shown in figures 4.10-4.13 as follows:

79

Figure 4.10: Relationship between Open Voltage and Kerf Width

Figure 4.11: Relationship between On-Time and Kerf Width

KER

F S/N RATIO

OV (Volts)

KER

F S/N RATIO

ONT (μs)

80

Figure 4.12: Relationship between Off-Time and Kerf Width

Figure 4.13: Relationship between Wire Tension and Kerf Width

KER

F S/N RATIO

OFT (μs)

WT (Grams)

KER

F S/N RATIO

81


In the same manner, the graphs and models are shown in figures 4.14-4.16 as follows:

Figure 4.14: Relationship between Thickness and Surface Roughness

Figure 4.15: Relationship between Open Voltage and Surface Roughness

TH (mm)

Ra S/N RATIO

OV (Volts)

Ra S/N RATIO

82

Figure 4.16: Relationship between On-Time and Surface Roughness


The graphs and models are shown in figures 4.17-4.20 as follows:

Figure 4.17: Relationship between Open Voltage and Material Removal Rate

ONT (μs)

Ra S/N RATIO

MR

R S/N

RATIO

OV (Volts)

83

Figure 4.18: Relationship between On-Time and Material Removal Rate

Figure 4.19: Relationship between Off-Time and Material Removal Rate

MR

R S/N

RATIO

MR

R S/N

RATIO

ONT (μs)

OFT (μs)

84

Figure 4.20: Relationship between Servo Voltage and Material Removal Rate

MR

R S/N

RATIO

SV (Volts)

85

CHAPTER5EXPERIMENTATIONFORWORKPIECEHARDNESS

In the previous chapter an extensive experimental work was presented that provided a better

understanding of the Wire-EDM process and helped to achieve the goal of this thesis. The

research work could have been concluded after having carried out the said experiment and its

statistical analysis but a new set of experiment was planned that will be discussed in this chapter.

One of the reasons was that the first experiment revealed that besides various machine variables,

the workpiece thickness was significant in one of the machining responses and was proving as a

vital variable for the Wrie-EDM process. This gave rise to one more question; is a variable such

as workpiece hardness going to play any role in the Wire-EDM process or machining outcomes

like kerf width, the surface roughness and the material removal rate. The answer could be

obtained only by performing another set of experiments that will be discussed in this chapter.

This experimentation has been carried out on the same lines as the first experiment but here the

variable like workpiece thickness was replaced with workpiece hardness and in total 05 process

parameters were selected with two levels each. The selected process parameters were those that

proved significant for most of the machining responses in the previous experimentation. The

machining responses measured were kerf width, surface roughness and the material removal rate.

Taguchi technique has been used for design of experiment. ANOVA was carried out after

obtaining the machining outcomes to determine the significant variables or process parameters.

After establishing the significant variables, the optimization of the machining responses was

accomplished.

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5.1 INTRODUCTION

Wire-EDM uses very high temperatures in excess of 12000o C144 for material removal by

melting and vaporizing 144-146. However in order to carry out Wire-EDM of any material, it

must contain a certain value of electrical conductivity. Wire-EDM 147 is affective for

producing complicated shapes with high accuracy and the process is more affective in terms of

material removal rate if the cutting wire is used in the nearest vicinity of the clamp as it causes

decrease in electrical resistivity. The previously held research literature [148,149] shows that

the EDM can be applied to machine ceramics, that includes single phases and the composites

of ceramic–ceramic and ceramic–metal incase the electrical resistivity is at least below 100 Ω-

cm. EDM has mainly been used for some conductive materials, but a few researchers have

carried out research to show that the EDM of non-conductive materials is possible with the use

of an assisting electrode [150,151]. So, it has now become possible to machine the insulating

ceramics without the need of adding conductive particles. This has helped in preventing any

changes in the properties of the workpiece material.

Jose Duarte Marafona and Arlindo Araujo 152, in 2009, performed experiments to find out the

affect of material hardness on machining responses such as material removal rate and surface

roughness however, the kerf width was not considered in this study. They used the tool steel as

workpiece material; the two levels of hardness were achieved by heat treatment. It was found

that workpiece material hardness has very little influence on material removal rate and the

surface roughness in die-sinking EDM. Therefore a need was felt to machine the same material

that is tool steel, with Wire-EDM in the present experiment having different hardness values so

87

that the hardness factor can be evaluated along with the machine variables. This will be

discussed in detail in few of the up-coming sections.

5.2 EXPERIMENTATION

The experimental design, experimental equipment, instrumentation, and the data acquisition

procedures to meet the objectives of this investigation will be discussed in this section. They

were almost the same as in last experiment except for the preparation of workpiece material.

5.2.1 DESIGN OF EXPERIMENT

In this experiment two levels of 05 process parameters were taken in order to save time and the

cost of experimentation. Out of these 05 parameters, as discussed earlier, 01 was hardness and

the rest of 04 parameters were selected keeping in mind the significant variables of the previous

experimentation. The variables/control factors/process parameters along with their levels are

listed in table 5.1.

Table 5.1:- Data Summery of Experiments FACTORS CODES Process parameters UNITS Level 1 Level 2

A HD Hardness HRA 63 83 B OV Open voltage Volts 75 105 C ONT On time μs 3 6 D OFT Off time μs 20 30 E SV Servo voltage Volts 40 60

The process parameters mentioned in the table 5.1 are workpiece hardness (Factor A:HD), open

voltage (Factor B:OV), pulse on time (Factor C:ONT), pulse off time (Factor D:OFT), and servo

voltage (Factor E:SV). Taguchi's orthogonal scheme has been used to reduce the total number of

88

runs by employing the orthogonal array L32(2x31). In this, out of 31 columns, 05 columns were

assigned to 05 process parameters and the rest 26 columns were spared for interactions. Thus the

Taguchi Orthogonal Array in this case appears as table 5.2 which is showing the machining

results also.

5.2.2 EXPERIMENTAL SETUP AND DATA ACQUISITION

A Wire-EDM machine G43S (CHMER EDM CHING HUNG Machinery & Electric Industrial

Co. Ltd. TAIWAN) was used for carrying out the experiments. The machine uses de-ionized

water as dielectric. A brass wire of 0.250 mm (tensile strength of approximately 800-1000

N/Sq.mm) diameter was used as the tool electrode (cathode). Tool steel AISI 4027 (63 HRA)

was used in this experiment as workpiece material. The specifications of workpiece material are

given in the table 5.3. The second level of the same workpiece material hardness was achieved

by heat treatment in order to keep other properties at same level that affect the machining such as

thermal conductivity and electrical resistivity etc. The workpiece material was heat treated and

the 2nd level of hardness of 83 HRA was obtained. In this the hardness was measured for a total

of 32 times on sixteen bars of 6 inches length. The standard deviation of 0.73 was calculated

with a confidence interval of 0.36 at 95 % confidence limit. Now the workpiece material with

two levels of hardness was available for machining.

The tool steel bars were available in square cross section of 12mm x 12 mm. They were cut

using Wire-EDM to obtain workpiece length/thickness of 1 inch each. According to design of

experiments, 32 runs were required to be carried out. So in total 96 samples were prepared. Each

run was executed three times for taking the average of machining responses for better accuracy

and results. The workable process parameters range was known from the first experiment,

therefore no difficulty was faced in choosing the two levels for each

89

Table 5.2:- Experimental Runs Specified by Taguchi L32(2x31) Orthogonal Array EXP.NO. HD

(HRA) OV

(Volts) ONT (µs)

OFT (µs)

SV (volts)

Kerf (µm)

Ra (µm)

MRR (mm3/min)

1 63 75 3 20 40 376 1.82 8.535 2 83 75 3 30 60 383 0.63 5.605 3 63 75 3 30 60 431 0.64 4.586 4 83 75 6 30 40 363 1.18 12.994 5 83 75 3 20 40 352 1.24 9.172 6 63 105 3 30 60 386 0.7 6.752 7 63 105 3 20 60 387 0.82 7.771 8 63 75 3 20 60 405 0.58 5.478 9 83 105 3 30 40 347 1.02 9.299 10 63 105 6 20 40 371 1.68 18.726 11 83 75 6 20 40 356 1.14 16.051 12 83 75 6 30 60 393 1.4 7.261 13 63 75 6 30 40 379 2.2 14.777 14 83 105 6 30 60 380 1.5 10.191 15 63 75 6 30 60 414 1.1 7.643 16 83 75 3 20 60 403 0.4 5.732 17 63 75 6 20 60 393 1.52 9.809 18 63 105 6 20 60 372 1.44 15.541 19 63 105 3 20 40 363 1.85 8.535 20 83 105 3 20 60 392 0.67 6.115 21 83 75 6 20 60 387 1.48 9.172 22 83 75 3 30 40 357 0.92 7.389 23 83 105 6 20 40 355 1.58 16.943 24 63 105 6 30 40 369 1.56 15.796 25 83 105 3 20 40 344 1.14 11.720 26 83 105 6 30 40 362 1.04 15.159 27 83 105 6 20 60 377 1.36 10.955 28 83 105 3 30 60 385 0.52 5.350 29 63 105 6 30 60 385 1.72 9.554 30 63 75 6 20 40 373 1.74 14.777 31 63 75 3 30 40 380 0.58 6.879 32 63 105 3 30 40 374 0.96 8.408

90

Table 5.3:- Specifications of workpiece material Specifications Tool Steel Grade AISI 4027

Composition 0.3% C, 0.9% Mn, 0.3% Mo, 0.035% P, 0.35% Si, 0.040% S

Hardness HRA 63

Density 7.85 g/cm3

Electrical resistivity 24.5 µohm-cm

Thermal conductivity 45 w/m-k

process parameter. The samples were machined with a total depth of cut of 12 mm in “L” shape,

7 mm in x-axis and 5 mm in y-axis. This was done intentionally to obtain bigger values of

machining times for obtaining reliable data for onward analysis. The machining responses

measured were kerf width, surface roughness and the material removal rate.

The kerf width (KF) was measured on a Universal Profile Projector (UK) of the accuracy 0.001

mm as was done in the last experiment. It was measured at three different points of the kerf

width in each run and was averaged.

The surface roughness was measured in the terms of CLA values (Ra). The characteristics of

surface finish like Rt and Rz were not measured because their trends of variation are always in

step with Ra [130]. Ra was measured with surface texture meter made by Taylor Hobson, UK.

The surface finish was measured at the set evaluation length and cut off length of 4.0 and 0.8 mm

respectively. Ra was measured three times for each run and an average was calculated.

The machine had an option of providing cut time on its monitor. So it was easily obtained

directly from the machine computer. In this study, for the sake of accuracy, the material removal

91

rate was calculated by weighing the samples before and after machining. The samples were

weighed on a weighing machine of the accuracy 0.001 grams. Again, this was repeated three

times for taking averages. The weight was converted to volume and was then divided by cutting

time. In this the material removal rate was obtained. After obtaining the data the next step was

the statistical analysis of each machining response that includes S/N ratio analysis and ANOVA

followed by optimization.

5.3 STATISTICAL ANALYSES

In this section statistical analyses have been divided into three parts. In the first part the S/N ratio

is calculated for three responses. The S/N ratio for kerf width (KF) and surface roughness (Ra)

has been calculated for the smaller-the better type approach. In the same manner, the S/N ratio

for material removal rate (MRR) has been calculated keeping in mind the larger-the better type

approach. In second part of this section, ANOVA has been carried out for establishing the

significant process parameters for each of the machining responses. In the final part the

optimization of the machining responses has been carried out.

5.3.1 ANALYSIS OF SIGNAL TO NOISE RATIO

According to the Taguchi technique 126, the characteristic that a larger value presents the

better machining performance, such as material removal rate (MRR), is called larger-the-better

type problem. On the contrary, the characteristic that smaller value indicates better machining

performance, for example surface roughness (Ra) or kerf width (KF), is termed as smaller-the-

better type problem. Signal to noise ratio has been calculated for kerf width (KF), surface

92

roughness (Ra) and material removal rate MRR as η1, η2 and η3 respectively. The experimental

results are shown in table 5.4.

Table 5.4:- Calculated S/N ratio Exp. No. Kerf

(µm) Ra (µm) MRR(mm3/min)

Kf η1 Ra η2 Mrr η3

1. 376 1.82 8.535 -51.50 -5.20 18.62 2. 383 0.63 5.605 -51.66 4.01 14.97 3. 431 0.64 4.586 -52.69 3.88 13.23 4. 363 1.18 12.994 -51.20 -1.44 22.27 5. 352 1.24 9.172 -50.93 -1.87 19.25 6. 386 0.7 6.752 -51.73 3.10 16.59 7. 387 0.82 7.771 -51.75 1.72 17.81 8. 405 0.58 5.478 -52.15 4.73 14.77 9. 347 1.02 9.299 -50.81 -0.17 19.37 10. 371 1.68 18.726 -51.39 -4.51 25.45 11. 356 1.14 16.051 -51.03 -1.14 24.11 12. 393 1.4 7.261 -51.89 -2.92 17.22 13. 379 2.2 14.777 -51.57 -6.85 23.39 14. 380 1.5 10.191 -51.60 -3.52 20.16 15. 414 1.1 7.643 -52.34 -0.83 17.67 16. 403 0.4 5.732 -52.11 7.96 15.17 17. 393 1.52 9.809 -51.89 -3.64 19.83 18. 372 1.44 15.541 -51.41 -3.17 23.83 19. 363 1.85 8.535 -51.20 -5.34 18.62 20. 392 0.67 6.115 -51.87 3.48 15.73 21. 387 1.48 9.172 -51.75 -3.41 19.25 22. 357 0.92 7.389 -51.05 0.72 17.37 23. 355 1.58 16.943 -51.00 -3.97 24.58 24. 369 1.56 15.796 -51.34 -3.86 23.97 25. 344 1.14 11.720 -50.73 -1.14 21.38 26. 362 1.04 15.159 -51.17 -0.34 23.61 27. 377 1.36 10.955 -51.53 -2.67 20.79 28 385 0.52 5.350 -51.71 5.68 14.57 29 385 1.72 9.554 -51.71 -4.71 19.60 30 373 1.74 14.777 -51.43 -4.81 23.39 31 380 0.58 6.879 -51.60 4.73 16.75 32 374 0.96 8.408 -51.46 0.35 18.49

93

After this the mean S/N ratios were calculated. The mean S/N ratios for each level of the process

parameters are summarized for KF, Ra and MRR in tables 5.5, 5.6 and 5.7 respectively. These

tables are known as the mean S/N response tables. These tables provide a picture about

significance of process parameters on a particular machining response. That is done by

subtracting the lower mean S/N ratio value from the higher one pertaining to any level of that

process parameter. The highest will be the difference; the more will be the significance.

Table 5.5:- S/N Response Table for Kerf Width (KF) Control Factor Mean S/N ratio (dB)

Level 1 Level 2 Max-Min Hardness -51.698 -51.377 0.320

Open voltage -51.675 -51.400 0.275 On time -51.516 -51.559 0.043 Off time -51.595 -51.480 0.116

Servo voltage -51.214 -51.861 0.648 Overall Mean -51.537

Table 5.6:- S/N Response Table for Surface Roughness (Ra)

Control Factor Mean S/N ratio (dB) Level 1 Level 2 Max-Min

Hardness -1.525 -0.046 1.479 Open voltage -0.379 -1.192 0.813

On time 1.665 -3.236 4.902 Off time -1.435 -0.135 1.300

Servo voltage -2.177 0.606 2.783 Overall Mean -0.785

The S/N ratio graphs for KF, SR and MRR are presented as Figures 5.1, 5.2 and 5.3 respectively.

Likewise, these figures give an empirical picture of the affects of process parameters on the

94

Table 5.7:- S/N Response Table for Material Removal Rate (MRR) Control Factor Mean S/N ratio (dB)

Level 1 Level 2 Max-Min Hardness 19.502 19.363 0.139

Open voltage 18.579 20.285 1.706 On time 17.043 21.821 4.778 Off time 20.162 18.703 1.459

Servo voltage 21.290 17.574 3.716

Overall Mean 19.432

selected machining responses. For example, if figure 5.1 is closely observed that represents the

mean S/N ratios for KF, the slope for servo voltage (Factor E:SV) is the greatest, depicting that

Figure 5.1: Mean S/N ratio graph for KF

it is most significant. The slopes are greater for open voltage (Factor B:OV), pulse on time

(Factor C:ONT), pulse off time (Factor D:OFT), and the hardness (Factor A:HD) also.

This procedure can be adopted for the other two machining responses. After the S/N ratio has

Two levels of control factors

A. HARDNESS B. OPEN


VOLTAGE

95

Figure 5.2: Mean S/N ratio graph for Ra

been obtained, the next step of the data analysis is to identify the process parameters that have a

significant affect on each of these machining responses. This has been achieved by performing

ANOVA.

Figure 5.3: Mean S/N ratio graph for MRR

A. HARDNESS B. OPEN


VOLTAGE



A. HARDNESS B. OPEN


VOLTAGE

96

5.3.2 ANOVA FOR KF, Ra and MRR

The results from ANOVA have been shown in tables 5.8-5.10. Here, for KF, the workpiece

hardness (A), open voltage (B), pulse off time (D) and servo voltage (E) are significant whereas

pulse on time (C) is not significant. For Ra, hardness (A), pulse on time (C) and servo voltage (E)

are significant whereas open voltage (B) and pulse off time (D) are not significant. For MRR,

except material hardness (A) all of process parameters are significant.

Table 5.8:- Results of ANOVA- Kerf Width (KF) Source Sum of

Squares df Mean


A-H 1540.13 1 1540.13 30.01 < 0.0001 Significant

B-OV 1200.5 1 1200.5 23.39 0.0002 Significant

C-ONT 40.5 1 40.5 0.79 0.3875 Not significant


E-SV 6384.5 1 6384.5 124.4 < 0.0001 Significant

Error 821.13 16 51.32

Total 11627.88 21

Table 5.9:- Results of ANOVA- Surface Roughness (Ra)

Source Sum of Squares

df Mean Square

F-Value Prob > F Significance

A-H 0.43 1 0.43 5.21 0.0365 Significant

B-OV 0.031 1 0.031 0.38 0.5489 Not significant

C-ONT 2.62 1 2.62 32.04 < 0.0001 Significant

D-OFT 0.24 1 0.24 2.98 0.1036 Not significant

E-SV 0.84 1 0.84 10.23 0.0056 Significant

Error 1.31 16 0.082

Total 6.66 21

97

From these results of ANOVA and affects of the levels of the control factor’s mean S/N ratio on

the subject responses presented by graphs in Figures 6.1, 6.2 and 6.3, it becomes easier to get an

overview and better understanding of the process. Now in the light of these two analyses the

three machining responses will be discussed in the following section.

Table 5.10:- Results of ANOVA-Material Removal Rate (MRR) Source Sum of

Squares df Mean


A-H 0.62 1 0.62 0.54 0.4734 Not significant

B-OV 29.94 1 29.94 25.98 0.0001 Significant



E-SV 142.99 1 142.99 124.08 0.0001 Significant

Error 18.44 16 1.15

Total 488.23 21

5.4 DISCUSSION

This set of experiments has also served as confirmatory experiment to the previous set of

experiments. The results of ANOVA for this experiment are in consistence with the results of

ANOVA of first experiment overall which is being considered satisfactory. A very few process

parameters were taken in this experimentation as compared to previous set of experiments.

Results for MRR are in 100% agreement with that of the previous set of experiments. It is

pertinent to mention here that the trends of variations of machining responses with the variation

in process parameter are 100% in agreement with the first experiment and the available

literature. After carrying out ANOVA, from figures 5.1, 5.2, and 5.3, it can be observed that the

slope for significant factors is on the larger side as compared to non-significant factors. Now

98

affects of process parameters on the machining responses and their possible reasons will be

discussed one by one. The significant factors will be discussed in particular.


In case of KF, the results show that the process parameters such as workpiece hardness (A), open

voltage (B), pulse off time (D) and servo voltage (E) are significant whereas pulse on time (C) is

not significant. Here it is to be kept in mind that a minimum value of KF in terms of µm is

required. Larger S/N ratio will present the better achievement of aim, no matter if it is

maximization or minimization of a particular output. The graph at Figure 5.1 depicts that the kerf

width decreases with increase in workpiece hardness (A), open voltage (B) and pulse off time

(D) whereas it decreases with increase in servo voltage (E). These results are mostly consistent

with the results of first experiment and the available literature; however a probe is required in the

case of workpiece hardness.

The workpiece hardness has produced the same affect that was observed in the case of workpiece

thickness in the case of the kerf width. kerf width will depend upon the amount of material

removed. More the material removed; wider will is the kerf and vice versa. In this case the kerf

for harder workpiece material is lesser than that of the softer workpiece. The same reason can be

attributed to this case also that the same spark intensity was not enough for the harder material to

remove same amount of material that was done in the case of softer material. In order to remove

same amount of material the spark intensity will have to be increased in order to cope up with the

hardness.

99


In the case of Ra, according to table 5.9, hardness (Factor A:HD), pulse on time (Factor C:ONT)

and servo voltage (Factor E:SV) are significant whereas open voltage (Factor B:OV) and pulse

off time (Factor D:OFT) are not significant. From figure 5.2, it can be seen that the Ra decreases

with increase in hardness (Factor A:HD) and servo voltage (Factor E:SV) whereas it increases

with increase in pulse on time (Factor C:ONT). The trends of variation of the machining

responses with variation in the process parameters are in accordance with that of first

experiment. The reasons for these have already been discussed in chapter 4, however the

hardness factor is discussed.

The workpiece material hardness in this case has again shown its influence over the machining

process. In case of harder workpiece, the Ra is better than that of softer one. Much of the research

material is available on the fact that increases in spark energy affects negatively on the surface

roughness 126,135. So this result is in consistency with the available literature. This can mean

that while selecting any material for machining, the hardness factor may also be considered.


In this case the ANOVA results, from table 5.10, have shown that all the selected process

parameters are significant as expected except material hardness. From the figure 5.3, the trends

of variations of machining responses against each process parameters can be seen. It has been

observed that material removal rate (MRR) decreases with increase in hardness (A), pulse off

time (D) and servo voltage (E) whereas it increases with increase in open voltage (B) and pulse

on time (C).

100

The results from ANOVA have been very much consistent with the available literature and first

experiment. However, form the findings above, the increase in the workpiece hardness (A) has

not resulted in much reduction to material removal rate thereby indicating that the process is not

much affected by the hardness.

5.5 OPTIMIZATION OF MACHINING RESPONSES

The ANOVA for all the three responses provided the significant factors. The optimal level of a

significant control factor is a level with the greater S/N ratio. Thus the optimal levels of process

parameters for each response were calculated and will be discussed in this section.


For example, significant process parameters for optimizing KF are hardness (A) at level 2, open

voltage (B) at level 2, pulse off time (D) at level 2 and servo voltage (E) at level 1. For the

factors that are not significant any level can be selected that is pulse on time (C) at level 1. After

the optimal levels of the process parameters have been determined, the next step is to predict and

verify the improvement of S/N ratio. The S/N ratio ‘ηpre.’ can be predicted as follows [127,143]:

ηpre. = ηa + (ηA- ηa) + (ηB- ηa) + (ηC- ηa) + (ηD- ηa) + (ηE- ηa) (5.3)

where, ηa is the overall mean S/N ratio and ηA, ηB, ηC, ηD and ηE are the S/N ratios of the factors

A, B, C, D and E respectively at the optimal levels. The corresponding responses can be

calculated by using Eq. (5.2). Table 5.11 shows the predicted and the actual responses obtained

after executing optimal runs for kerf width (KF).

101

Table 5.11:- Results of conformation experiment for Kerf Width (KF)

Initial Control Factors Optimal Control Factors Prediction Experiment

Level A1 B1 C1 D1 E1 A2 B2 C1 D2 E1 A2 B2 C1 D2 E1 KF (microns) 376 348 347 S/N ratio(dB) -51.504 -50.837 -50.807


The increase in value of the S/N ratio from the initial cutting parameters levels to the optimal

cutting parameters levels is 0.70 dB for KF.


In the similar way, significant process parameters for optimizing Ra are hardness (A) at level 2,

pulse on time (C) at level 1, and servo voltage (E) at level 2. For the factors that are not

significant like open voltage (B) at level 1 and pulse off time (D) at level 2. Tables 5.12 shows

the comparison of the predicted and the actual responses obtained after executing optimal runs

for surface roughness (Ra). The increase in value of the S/N ratio from the initial cutting

parameters levels to the optimal cutting parameters levels is 9.21 dB for Ra.

Table 5.12:- Results of conformation experiment for Surface Roughness (Ra) Initial Control Factors Optimal Control Factors

Prediction Experiment Level A1 B1 C1 D1 E1 A2 B1 C1 D2 E2 A2 B1 C1 D2 E2

Ra Ra (microns) 1.82 0.57 0.63 S/N ratio(dB) -5.201 4.851 4.013



Likewise, significant process parameters for optimizing MRR are open voltage (B) at level 2,

pulse on time (C) at level 2, pulse off time (D) at level 1 and servo voltage (E) at level 1. Any of

the hardness levels in this case can be taken; here it has been taken at level 1.

102

Table 5.13:- Results of conformation experiment for Material Removal Rate (MRR) Initial Control Factors Optimal Control Factors

Prediction Experiment Level A1 B1 C1 D1 E1 A1 B2 C2 D1 E1 A1 B2 C2 D1 E1

MRR (mm3/Min) 8.758 18.476 18.726 S/N ratio(dB) 18.848 25.332 25.453


Tables 5.13 shows the comparison of the predicted and the actual responses obtained after

executing optimal runs for material removal rate (MRR). The increase in value of the S/N ratio

from the initial cutting parameters levels to the optimal cutting parameters levels is 6.61 dB for

MRR.

103

CHAPTER6CONCLUSIONANDRECOMMENDATIONS

In this work two sets of experiment were conducted to achieve the aim of the research topic.

There were 27 runs for the first experiment that were repeated three times, for evaluating

confidence level, which made it 81 runs. For the second experiment 32 runs were conducted and

they were also repeated three times making it 96. So, in this work a total of 177 runs were

conducted for obtaining the data and subsequent analysis to investigate the variables that affect

the machining outcomes in discussion. After completion of runs, conclusions were drawn on the

basis of data analysis and logic in collaboration with the held literature that is presented in the

forthcoming section. Some new ideas have been conceived that will help further exploration of

Wire-EDM process and have been made a part of recommendation section.

6.1 CONCLUSION

The current work provided the comprehensive analysis of the affect of workpiece thickness and

hardness along with various other process parameters on machining responses. The following

conclusions can be derived on the basis of the experimental results obtained in this study and are

consistent with the held literature.

Kerf width is influenced by variables like workpiece hardness, open voltage, on time, off

time, servo voltage, and the wire tension.

Kerf width increases significantly with increase in on time.

Kerf width increases significantly with increase in servo voltage.

104

Kerf width decreases significantly with increase in workpiece hardness.

Kerf width decreases significantly with increase in open voltage.

Kerf width decreases significantly with increase in off time.

Kerf width decreases significantly with increase in wire tension.

Surface roughness is influenced by the variables like workpiece thickness, workpiece

hardness, open voltage, on time, and the servo voltage.

Surface roughness increases significantly with increase in open voltage.

Surface roughness increases significantly with increase in on time.

Surface roughness decreases with increase in workpiece thickness.

Surface roughness decreases with increase in workpiece hardness.

Surface roughness decreases with increase in servo voltage.

Material removal rate is influenced by the variables like open voltage, on time, off time, and

the servo voltage.

Material removal rate increases significantly with increase in open voltage.

Material removal rate increases significantly with increase in on time.

Material removal rate decreases with increase in off time.

Material removal rate decreases significantly with increase in servo voltage.

Application of Taguchi Technique has sufficiently reduced the number of required runs

without affecting the results.

Increase in workpiece thickness has not much affected the material removal rate and kerf

width but has proven to be a significant factor in case of surface roughness.

Spark energy is uniformly distributed over the engaged length/area of the cutting wire.

105

Optimization of kerf width, surface roughness and material removal rate in case of tungsten

carbide and tool steel is readily available.

Workpiece hardness has been significant in kerf width and surface roughness but not in

material removal rate.

The kerf width, surface roughness for the harder material in case of same process parameters

setting is lesser as compared to softer material.

The experimental results have confirmed the suitability of using Taguchi design for analyses

and optimization of the control parameters for obtaining optimal responses.

6.2 RECOMMENDATIONS

Following are the recommendations for further research and industrial practice, which are based on this investigation and the drawn conclusions:

For thinner workpieces the spark energy will have to be further reduced in order to get

desired surface roughness and thereby compromising on material removal rate. A trade off is

inevitable depending upon requirement.

Optimization carried out till to date in case of surface roughness will stand good for that

particular thickness unless generalized through further work by including thickness.

Hardness factor will have to be considered for kerf width and surface roughness calculations.

Further experimentation can be carried out to further explore the reasons for the affects of

hardness.

Future investigations may include the comparison of hardness with the thermal and electrical

properties of material.

Future investigations may include the influence of grain structure on machining responses.

106

Future investigations may also include the microscopic analysis of the white layer and its

affects.

In addition further studies can be carried out by varying the cryogenic conditions such as

temperature of di-electric etc.

Using different wire diameters can also reveal some facts about the process.

Affect of varying conductivity of di-electric can also be studied.

107

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