A Project Report

on

“EXPERIMENTAL STUDY ON EFFECT OF PROCESS

PARAMETERS ON PERFORMANCE MEASURE OF EDM”

submitted to

Gujarat Technological University

for Partial Fulfillment Towards the

Subject : PROJECT-II (181901), Semester VIIIth

in the Field of

“MECHANICAL ENGINEERING”

Submitted by

PATEL PAVANKUMAR I. (080170119037)

PARMAR KAUSHIK C. (080170119027)

PATEL DARSHIL D. (090173119005)

MODI TARUN D. (090173119002)

Under the Guidance of

Prof. S.R. Pandya

Asst. Professor,

Department of Mechanical Engineering

Vishwakarma Government Engineering College , Chandkheda

Department of Mechanical Engineering

Vishwakarma Government Engineering College,

Chandkheda – 382424 APRIL/MAY 2012

Certificate

This is to certify that the project report entitled “EXPERIMENTAL STUDY ON

EFFECT OF PROCESS PARAMETERS ON PERFORMANCE MEASURE OF

EDM”

submitted by

PATEL PAVANKUMAR I. (080170119037)

PARMAR KAUSHIK C. (080170119027)

PATEL DARSHIL D. (090173119005)

MODI TARUN D. (090173119002)

towards the partial fulfillment of the requirement for the subject PROJECT-I (Subject

Code: 181901) (Semester VIIIth

) in the field of “MECHANICAL ENGINEERING”

of Gujarat Technological University is a record of the bona-fide work carried out by

him/her under my guidance and supervision. The work submitted, in my opinion, has

reached to a level required for being accepted for the examination.

.

Guide:

Prof. S.R.Pandya

Asst. Professor,

Department of Mechanical Engg.

Vishwakarma Government Engg.College ,

Chandkheda

Prof. Rupal. P Vyasa

Head of Department

Department of Mechanical Engg.

Vishwakarma Government Engg. College ,

Chandkheda

Certificate of Examiner

The Project Report entitled

“EXPERIMENTAL STUDY ON EFFECT OF PROCESS

PARAMETERS ON PERFORMANCE MEASURE OF EDM”

Submitted By

PATEL PAVANKUMAR I. (080170119037)

PARMAR KAUSHIK C. (080170119027)

PATEL DARSHIL D. (090173119005)

MODI TARUN D. (090173119002)

As a partial fulfillment of the requirement

for the

Subject : PROJECT-I (181901)

Semester-VIIIth

of Gujarat Technological University in the field of

“MECHANICAL ENGINEERING”

is hereby approved.

Internal Examiner External Examiner

Date :

Place :

ACKNOWLEDGEMENT

I express my cavernous sense of obligation and gratitude to my guide Prof. S R

PANDYA for her genuine guidance and constant encouragement throughout this project

work. I am highly obliged as my honourable guide have devoted her valuable time and

shared his expertise knowledge.

I extend my sincere thanks to HOD, Department of Mechanical Engineering and

Principal, Vishwakarma Government Engineering College, Chandkheda for providing me

such an opportunity to do my project work in my college.

I also wish to express my heartfelt appreciation to my friends, colleagues and

many who have rendered their support for the successful completion of the project, both

explicitly and implicitly.

PATEL PAVANKUMAR I. (080170119037)

PARMAR KAUSHIK C. (080170119027)

PATEL DARSHIL D. (090173119005)

MODI TARUN D. (090173119002)

8th

/Mechanical

Date:

Place:

ABSTRACT

Electron discharge machining is one of the earliest non-traditional machining

processes. EDM process is based on thermoelectric energy between the work piece and

an electrode. Material removal rate (MRR) is an important performance measure in EDM

process Low MRR is the disadvantage in EDM therefore no. Of ways are explored to

improve and optimize MRR.

This project works on mainly concentrated on improving the MRR by controlling

the various process parameters. For that a experiment is to be carried out on EDM

machine and result to be analyzed. The effect of various input parameters on output

responses have been analyzed using Analysis of Variance (ANOVA). Main effect plot

and S/N ratio have been used to determine the optimal design for each output response.

NOMENCLATURE

Vo Open Circuit Voltage

Vw The Working Voltage

Io The Maximum Current

W

t

Ton

Weight

Operation Time

The Pulse Time On

Toff

ρ

The Pulse Time Off

Density

LIST OF FIGURES

1.1 Relaxation Circuit 4

1.2 Variation Of Capacitor Voltage With Time 4

1.3

1.4

1.5

Pulse Wave Form Of Controlled Pulse Generator

Mechanism Of Material Removal

Schematic Diagram

5

6

7

1.6 Normal Polarity & Reverse Polarity 8

3.1

5.1

5.2

5.3

5.4

5.5

5.6

5.7

5.8

P- Diagram For Static Problem

Sparkonix S25 Series

EDM Work Table

Tool (EN31) After Machining

Tool (D2) After Machining

Tool (MS) After Machining

Tool (EN31) After Machining

Tool (D2) After Machining

Tool (MS) After Machining

21

34

35

39

40

40

41

41

42

LIST OF TABLES

4.1 L27 Result Table For Pilot Experiment

4.2 Response Table For SN Ratio

5.1 Factors & Their Levels

5.2 L27 Orthogonal Array

5.3 Constant Input Parameters

5.4 Response Characteristics

5.5 Work-piece Material Composition

5.6 Electrode Material Composition

6.1 Result Table For Finale Experiment

6.2 Response Table For SN Ratio

6.3 Analysis Of Variance

6.4 Confirmation Test Reading

7.1 Optimum Condition

26

28

31

32

34

36

38

39

43

45

46

48

49

LIST OF GRAPHS

4.1

6.1

6.2

Main Effect Plot For SN Ratio

Main Effect Plot For Means

Main Effect Plot For SN ratio

28

46

47

INDEX

Acknowledgment i

Abstract ii

Nomenclature iii

List of Tables iv

List of Figures v

List of Graphs vi

1. INTRODUCTION 1-13

1.1 Introduction to Non-Traditional Processes 1

1.2 Electric Discharge Machine 2

1.3 History Of EDM 2

1.4 Working Principle Of EDM 3

1.5 Mechanism Of Material Removal 5

1.6 Sinker EDM 7

1.7 EDM process Parameters 8

1.7.1 Polarity 8

1.7.2 Pulse On Time 9

1.7.3 Pulse Off Time 9

1.7.4 Peak Current 10

1.7.5 Discharge Current 10

1.7.6 Pulse Wave Form 10

1.7.7 Type Of Die-Electric Medium 11

1.7.8 Type Of Flushing 11

1.7.9 Electrode Gap 13

1.7.10 Electrode Material 13

2. LITRATURE REVIEW 14-16

2.1 Introduction 14

3. EXPERIMENTAL METHODOLOGY 17-25

3.1 Taguchi Method 17

3.2 Taguchi Philosophy 18

3.3 Experimental Design Strategy 19

3.4 Taguchi Method Categories 20

3.4.1 Static Problems 20

3.4.1.1 Signal to Noise Ratio 21

3.5 Taguchi Design Steps 23

3.6 Data Analysis 24

3.7 Advantages & Disadvantages Of Taguchi 24

4. PILOT EXPERIMENT 26-28

4.1 Pilot Experimentation 26

4.2 L27 Orthogonal Array Along With Results For EDM

Process During Pilot Experimentation 26

5. EXPERIMENTAL DESIGN 29-42

5.1 Introduction 29

5.2 Procedure Of Experimental Design 29

5.3 Establishment Of Objective Function 30

5.4 Degree Of Freedom 30

5.5 Selection Of Factors 30

5.6 Orthogonal Array 31

5.7 Experimental Set-up 33

5.8 Analysis Of Result 35

5.9 Material Composition For Tool & Tool 38

6. RESULT & ANALYSIS OF MRR 43-48

6.1 Introduction 43

6.2 Results For MRR 43

6.3 Results Of SN Ratio For MRR 44

6.4 Result Of ANOVA For MRR 47

7. RESULT, CONCLUSION & RECOMMENDATION 49-50

7.1 Optimal Design For MRR 49

7.2 Recommendation For Future Work 49

APPENDIX- A 51

APPENDIX- B 52

APPENDIX- C 53

REFEERENCESS 54-55

CHAPTER 1

INTRODUCTION

1.1 Introduction To Non-Traditional Processes

Technologically advanced industries like aeronautics, automobiles, nuclear

reactors, missiles, turbines etc. requires materials like high strength temperature resistant

alloys which have higher strength, corrosion resistance, toughness, and other diverse

properties.

With rapid development in the field of materials it has become essential to develop

cutting tool materials and processes which can safely and conveniently machine such new

materials for sustained productivity, high accuracy and versatility at automation.

Consequently, non-traditional techniques of machining are providing effective solutions

to the problem imposed by the increasing demand for high strength temperature resistant

alloys, the requirement of parts with intricate and compacted shapes and materials so hard

as to defy machining by conventional methods. The processes are non-conventional in

the sense that these don‟t employ a conventional tool for the material removal. Instead

these utilize energy in direct form to remove the materials from work-piece. The range of

applications of newly developed machining process is determined by work-piece

properties like electrical and thermal conductivity, melting temperature, electrochemical

equivalent etc. These techniques can be classified into three categories, i.e. mechanical,

electro-thermal, and electrochemical machining processes. The mechanical

nonconventional techniques (abrasive jet machining, ultrasonic machining, and water jet

machining) utilizes kinetic energy of either abrasive particles or a water jet to remove the

material. In electro-thermal method (plasma arc machining, laser beam machining, and

electron beam machining) the energy is supplied in form of heat, light, and electron

bombardment which results melting, or vaporization and melting both of work material.

In the chemical machining, etching process is being done. On the other hand, in

electrochemical machining an anodic dissolution process is going on in which high

material removal rate can be achieved. The selection of a process is depend upon various

factors like- process capabilities, physical parameters, shape to be machined, properties

of work-piece material to be cut, and economics of process.

1.2 Electric Discharge Machine

Electrical discharge machining (EDM) is one of the most extensively used

nonconventional material removal processes. In this process the material is removed by a

succession of electrical discharges, which occur between the electrode and the work-

piece. There is no direct contact between the electrode tool and the work-piece. These are

submersed in a dielectric liquid such as kerosene or deionized water. Its unique feature of

using thermal energy to machine electrically conductive parts regardless of hardness has

been its distinctive advantage. The electrical discharge machining process is widely used

in the aerospace, automobile, die manufacturing and moulds industries to machine hard

metals and its alloy.

1.3 History Of Electric Discharge Machining

In dates back to 1770, English chemist Joseph Priestly discovered the erosive

effect of electrical discharges on metal. After a long time, in 1943 at the Moscow

University where B.R. and N.I. Lazarenko decided to exploit the destructive effect of

electrical discharges for constructive use. They developed a controlled process of

machining to machine metals by vaporizing material from the surface of work-piece.

Since then, EDM technology has developed rapidly and become indispensable in

manufacturing applications such as die and mould making, micro-machining,

prototyping, etc. In 1950s

The RC (resistance–capacitance) relaxation circuit was introduced, in which provided the

first consistent dependable control of pulse times and also a simple servo control circuit

to automatically find and hold a given gap between the electrode (tool) and the work-

piece. In the 1980s, CNC EDM was introduced which improved the efficiency of the

machining operation.

1.4 Working Principle Of EDM

The basic principle in EDM is the conversion of electrical energy into thermal

energy through a series of discrete electrical discharges occurring between the electrode

and work piece immersed in the dielectric fluid. The insulating effect of the dielectric is

important in avoiding electrolysis of the electrodes during the EDM process. A spark is

produced is at the point of smallest inter-electrode gap by a high voltage, overcoming the

strength dielectric breakdown strength of the small gap between the cathode and anode at

a temperature in the range of 8000 to 12,000 °C. Erosion of metal from both electrodes

takes place there. Duration of each spark is very short. The entire cycle time is usually

few micro-seconds (μs).The frequency of pulsating direct current supply is about 20,000–

30,000 Hz is turned off. There is a sudden reduction in the temperature which allows the

circulating dielectric fluid to flush the molten material from the work-piece in the form of

microscopic debris. After each discharge, the capacitor is recharged from DC source

through a resistor, and the spark that follows is transferred to the next narrowest gap.

The cumulative effect of a succession of sparks spread over the entire Work-piece surface

leads to erosion, or machining to a shape, which is approximately complementary to that

of the tool.

A servo system, which compares the gap voltage with a reference value, is employed to

ensure that the electrode moves at a proper rate to maintain the right spark gap, and to

retract the electrode if short-circuiting occurs. The Lazarenko RC circuit does not give

good material removal rate (MRR), and higher MRR is possible only by sacrificing

surface finish. As indicated in Figure 1.1, the increase in voltage of capacitor should be

larger than the breakdown voltage and hence great enough to create a spark between

electrode and Work-piece, at region of least electrical resistance, which usually occurs at

the smallest inter electrode gap.

Figure 1.1: Relaxation circuit

A servo system, which compares the gap voltage with a reference value, is

employed to ensure that the electrode moves at a proper rate to maintain the right spark

gap, and to retract the electrode if short-circuiting occurs. The Lazarenko RC circuit does

not give good material removal rate (MRR), and higher MRR is possible only by

sacrificing surface finish. As indicated in Figure 1.2, the increase in voltage of capacitor

should be larger than the breakdown voltage and hence great enough to create a spark

between electrode and work-piece, at region of least electrical resistance, which usually

occurs at the smallest inter electrode gap.

Figure 1.2: Variation of capacitor voltage with time

This has been achieved with advent controlled pulse generator. It is typical wave

forms are shown in Figure 1.3. In this, as comparison to RC circuit there is increase in

pulse duration and less peak current value, shortened idle period.

Figure 1.3: Pulse waveform of controlled pulse generator

1.5 Mechanism Of Material Removal

The electro sparking method of metal working involves an electric erosion effect

in which the breakdown of electrode material is done by electric discharge. The discharge

is created by the ionization of dielectric which is spilled up of its molecules into ions and

electrons. This discharge is created between two electrodes through a gaseous or liquid

medium with the application of suitable voltage across the electrodes. The potential

intensity of electric field between them is built up at some predetermined value individual

electrons will break loose from the surface of the anode under the influence of the field

force. While moving in the inter-electrode space, the electrons collide with the neutral

molecules of the dielectric, detaching electrons from them and causing ionization.

At some time or other the ionization becomes such that a narrow channel of continuous

conductivity is formed. When this happens there is a continuous flow of electrons along

the channel to the electrode, resulting in the momentary current impulse or discharge.

The liberation of energy accompanying the discharge leads to generation of high

temperature.

This high temperature plasma causes fusion or particle vaporization of metal and the

dielectric fluid at the point of discharge. This leads to the formation of tiny crater at the

point of discharge in the work-piece. Comparatively less metal is eroded from the tool as

compared to the work-piece due to following reasons:

a) The momentum with which positive ions strike the cathode surface is much less

than the momentum with which the electron stream impinges on the anode

surface.

b) A compressive force is generated on the cathode surface by spark which helps

reduce tool wear.

The particles removed from the electrodes due to discharge fall in liquid, cool down and

contaminate the area around the electrodes by forming colloidal suspension of metal.

These suspensions, along with the products of decomposition of liquid dielectric are

drawn into the space between the electrodes during the initial part of discharge process

and are distributed along the electric lines of force, thus forming current carrying

„bridges‟. The discharge then occurs along one of these brides as result of ionization.

Figure 1.4: Mechanism of material removal

1.6 Sinker EDM

Sinker EDM sometimes is also referred to as cavity type EDM or volume EDM. It

consists of an electrode and work-piece that are submerged in an insulating liquid such as

oil or dielectric fluid. In it, hydrocarbon dielectrics are normally used because surface

roughness is better and tool electrode wear is lower compared to de-ionized water.

The electrode and work-piece are connected to a suitable power supply. The power

supply generates an electrical potential between the two parts. As the electrode

approaches the work-piece, dielectric breakdown occurs in the fluid forming an

ionization channel, and a small spark is generated. The resulting heat and cavitations

vaporize the work material, and to some extent, the electrode. These sparks strike one at a

time in huge numbers at seemingly random locations between the electrode and the work-

piece. As the base metal is eroded, the spark gap increases. Thus electrode is lowered

automatically by the machine so that the process can continue uninterrupted. Several

hundred thousand sparks occur per second in this process, with the actual duty cycle

being carefully controlled by the setup parameters. These controlling cycles are

sometimes known as "on time" and "off time"

Figure 1.5: Schematic diagram of the Sinker EDM

The on time setting determines the length or duration of the spark. Hence, a longer on

time produces a deeper cavity for that spark and all subsequent sparks for that cycle

creating a rougher finish on the work-piece. The reverse is true for a shorter on time. Off

time is the period of time that one spark is replaced by another. A longer off time, allows

the flushing of dielectric fluid through a nozzle to clean out the eroded debris, thereby

avoiding a short circuit. These settings are maintained in micro seconds.

The work-piece can be formed, either by replication of a shaped tool electrode. The

numerical control monitors the gap conditions (voltage and current) and synchronously

controls the different axes and the pulse generator. The dielectric liquid is filtrated to

remove debris particles and decomposition products.

1.7 EDM Process Parameters

1.7.1 Polarity

The Polarity normally used is normal polarity in which the tool is negative and

work-piece is positive. Sometimes positive polarity can be used depending upon the

requirement, where tool is positive and work-piece is negative. The negative polarity of

the work-piece has an inferior surface roughness than that under positive polarity in

EDM.

Figure1.6: Normal Polarity and Reverse Polarity

As the electron processes has smaller mass than anions show quicker reaction, the

anode material is worn out predominantly. This effect causes minimum wear to the tool

electrodes and becomes of importance under finishing operations with shorter on-times.

However, while running longer discharges, the early electron process predominance

changes to positron process (proportion of ion flow increases with pulse duration),

resulting in high tool wear.

In general, polarity is determined by experiments and is a matter of tool material, work

material, current density and pulse length combinations

.

1.7.2 Pulse on time

Pulse on-time is the time period during which machining takes place. MRR is

directly proportional to amount of energy applied during pulse on-time. The energy of

spark is controlled by the peak amperage and the length of the on-time. The longer the

on-time pulse is sustained, the more work-piece material will be eroded. The resulting

crater will be broader and deeper than a crater produced by a shorter on-time. These large

craters will create a rougher surface finish. Extended on times gives more heat to work-

piece, which means the recast layer will be larger and the heat affected zone will be

deeper. Hence, excessive on-times can be counter-productive. When the optimum on-

time for each electrode-work material combination is exceeded, material removal rate

starts to decrease.

.

1.7.3 Pulse off time

Pulse off-time is the time during which re-ionization of dielectric takes place. The

discharge between the electrodes leads to ionization of the spark gap. Before another

spark can take place, the medium must de-ionize and regain its dielectric strength. This

takes some finite time and power must be switched off during this time. Too low values

of pulse off time may lead to short-circuits and arcing. A large value on other hand

increases the overall machining time since no machining can take place during the off-

time. Each cycle has an on-time and off-time that is expressed in units of microseconds.

1.7.4 Peak current

This is the amount of power used in discharge machining, measured in units of

amperage, and is the most important machining parameter in EDM. In each on-time

pulse, the current increases until it reaches a preset level, which is expressed as the peak

current. Higher value of peak current leads to rough surface finish operations and wider

craters on work materials. Its higher value improves MRR, but at the cost of surface

finish and tool wear.

Hence it is more important in EDM because the machined cavity is a replica of tool

electrode and excessive wear will hamper the accuracy of machining [8].

1.7.5 Discharge current

The discharge current (Id) is a measure of the power supplied to the discharge

gap. A higher current leads to a higher pulse energy and formation of deeper discharge

craters. This increases the material removal rate (MRR) and the surface roughness (Ra)

value. Similar effect on MRR and Ra is produced when the gap voltage (Vg) is increased.

Once the current starts to flow, voltage drops and stabilizes at the working gap level. The

preset voltage determines the width of the spark gap between the leading edge of the

electrode and work-piece. Higher voltage settings increase the gap, which improves the

flushing conditions and helps to stabilize the cut.

1.7.6 Pulse wave form

For higher surface finish, higher peak current values and short spark duration is

required, a controlled pulse generator is used in EDM to generate proper pulse wave

form. The pulses of high energy and low frequency are used in rough machining. The

pulse shape is normally rectangular, but generators with other pulse shapes have also

been developed.

Using a generator which can produce trapezoidal pulses succeeded in reducing

relative tool wear to very low values.

1.7 .7 Type of Dielectric medium

The fluids used as dielectric are generally hydrocarbon oils. The kerosene oil,

paraffin oil, lubricating oil can be used. The deionized water gives high MRR and TWR .

However, the use of deionized water may result in higher levels of material removal rate

in some special situations such as when a brass electrode at negative polarity is used ;

pulse durations smaller than 500 μs are employed and machining of Ti–6A1–4V with a

copper electrode . It has seen that machining a steel work-piece with a negative brass

electrode in deionized water and with pulse time ranging from 400 to 1500 μs resulted in

improved performance (higher material removal rate and lower electrode wear) when

compared to performing the same operation in a hydrocarbon oil. For a pulse time of 800

μs, material removal rate was approximately 60% higher and electrode wear 25% lower.

A good dielectric fluid should have following properties:

a) It should have dielectric strength (i.e. behave as insulator until the required

Breakdown voltage between the electrodes is attained).

b) It should take minimum possible time to breakdown, once the break down

voltage is attained.

c) It should able to deionized the gap immediately after the spark has occurred.

d) It should serve as an effective cooling medium.

e) It should have high degree of fluidity.

1.7.8 Type of flushing

It is basic requirement of dielectric that it should maintain its dielectric strength

(insulating properties) during its whole operation. There is no problem at the start of

EDM, but after discharge the debris are produced in the gap reduce the dielectric

strength, which cause unwanted discharges which can damage to both tool and work-

piece. Hence effective flushing is required to remove unwanted debris from the gap [3].

TWR and MRR are affected by the type of dielectric and the method of its flushing. In

EDM, flushing can be achieved by following methods:

1.7.8.1 Suction flushing

In this, dielectric may be sucked through either the work-piece or the electrode.

This technique is employed to avoid any tapering effect due to sparking between

machining debris and the side walls of the electrodes. Suction flushing through the tool

rather than through the work-piece is more effective.

1.7.8.2 Injection flushing

In this technique, dielectric is fed through either the work-piece or the tool which

are predrilled to accommodate the flow. With the injection method, tapering of

components arises due to the lateral discharge action occurring as a result of particles

being flushed up the sides of electrodes.

1.7.8.3 Side flushing

When the flushing holes cannot be drilled either in the work-piece or the tool, side

flushing is employed. If there is need of flushing of entire working area, special

precautions have to taken for the pumping of dielectric.

1.7.8.4 Flushing by dielectric pumping

This method has been found particularly suitable in deep hole drilling. Flushing is

obtained by using the electrode pulsation movement. When the electrode is raised, clean

dielectric is sucked into mix with contaminated fluid, and as the electrode is lowered the

particles are flushed out.

1.7.9 Electrode gap

The servo feed system is used to control the working gap at a proper width.

Mostly electro-mechanical (DC or stepper motors) and electro-hydraulic systems are

used, and are normally designed to respond to average gap voltage [8]. Larger gap widths

cause longer ignition delays, resulting in a higher average gap voltage. If the measured

average gap voltage is higher than the servo reference voltage preset by the operator, the

feed speed increases. On the contrary, the feed speed decreases or the electrode is

retracted when the average gap voltage is lower than the servo reference voltage, which is

the case for smaller gap widths resulting in a smaller ignition delay. Therefore short-

circuits caused by debris particles and humps of discharge craters can be avoided. Also

quick changes in the working surface area, when tool electrode shapes are complicated,

does not result in hazardous machining. In some cases, the average ignition delay time is

used in place of the average gap voltage to monitor the gap width.

1.7.10 Electrode material

The shape of electrode will be basically same as that of the product is desired. The

electrode materials are classified as metallic material (copper, brass, tungsten,

aluminium), non-metallic material (graphite), combined metallic and non-metallic

(copper-graphite), and metallic coating as insulators (copper on moulded plastic, copper

on ceramic) etc. Materials having high melting-point, good electrically conductivity, low

wear rate and easily machinability are usually chosen as tool materials for EDM. They

should be cheap and readily shaped by conventional methods. High density graphite is

used in pulsed EDM equipment, although the material does not perform satisfactorily in

RC EDM work. It gives low wear due to its high melting temperature. Copper has the

qualities for high stock metal removal. It is a stable material under sparking conditions.

Brass as a tool material has high wear. Copper-boron and silver tungsten both exhibit

extremely low wear. Sometimes copper tungsten is employed as the cathode metal. Its

use yields high machining rates and very low wear. Due to its high cost and not so readily

shaped, its applications are limited.

CHAPTER 2

LITRATURE REVIEW.

2.1 INTRODUCTION

A large work has been done on different aspects of EDM. This chapter

covers theVLiterature on EDM machine settings and other process parameters.

A study by Bernd M. Schumacher[3], The ignition of electrical discharges in a

dirty, liquide filkled gap, when applying EDM, is mostly interpted as iron action

identical as found by physical research of dischrges in air (Lichtenberg figures) or

in vaccum (radio tubes) as well as with investigations on the breakthrough

strength of insulating hydrocarbon liquides. The state of the servo-controlled gap

in real EDM, however, diffuser very much from such condition. The author

stipulates ignition of electrical discharges by the evaporation of particle, removal

from the electrodes, as well as gas bubbles from earlier discharges. The material

removal reaction is grouped in an evaporation phase at start of ignition and later

in the ejection of fused material by instantaneous boiling at the discharge spots.

The gap width derives from the gap contamination avg., depending from process

setting.

A study By Kuldip Oza, R.K. Garg [5], Electrical discharge machining (EDM) is

one of the earliest non-traditional machining processes. EDM process is based on

thermoelectric energy between the workpiece and an electrode. Material removal

rate (MRR) is an important performance measure in EDM process. Since long,

EDM researchers have explored a number of ways to improve and optimize the

MRR including some unique experimental concepts that depart from the

traditional EDM sparking phenomenon. Despite a range of different approaches,

all the research work in this area shares the same objectives of achieving more

efficient material removal coupled with a reduction in tool wear and improved

surface quality.

A study on dry EDM by M.kunieda, B. lauwers,k.p. rajurker[6], with copper as

tool electrode & steel as workshop reveal that in case of EDM in Air, the tool

electrode wear ratio was much lower & MRR much higher when tool electrode

was negative. In the case of EDM in a liquid there was more tool of electrode

wear & lower MRR when the polarity of tool is negative. Hence negative polarity

was found to be desirable for material transfer from the tool electrode.

A study by A Thilaivanan , P aspkan , K.N. Srinivasan, R. saravasan[7] , A

suitable selection of machining parameters for the electrical discharge machining

process relies heavily on the operators‟ technologies and experience because of

their numerous and diverse range. Machining parameters tables provided by the

machine tool builder cannot meet the operators‟ requirements, since for an

arbitrary desired machining time for a particular job, they do not provide the

optimal machining conditions. An approach to determine parameters setting is

proposed. Based on the Taguchi parameter design method and the analysis of

variance, the significant factors affecting the machining performance such as total

machining time, oversize and taper for a hole machined by EDM process, are

determined.

A study By Mohd Amri Lajs, H.C.D. Mohd Radzi[9] , With the increasing

demand for new, hard, high strength, hardness, toughness, and temperature

resistant material in engineering, the development and application of EDM has

become increasingly important . EDM has been used effectively in machining

hard, high strength, and temperature resistance materials. Material is removed by

means of rapid and repetitive spark discharges across the gap between electrode

and workpiece. Therefore, the merits of the EDM technique become mos apparent

when machining metal alloy Tungsten Carbide which has the highest hardness in

Reinforcement. In addition, mechanical and physical properties of tungsten

carbide such as hardness toughness, high wear resistance has made it an important

material for engineering components particularly in making moulds and dies.

Since the EDM process does not involve mechanical energy, the removal rate is

not affected by either hardness, strength or toughness of the workpiece material.

Therefore, a comprehensive study of the effects of EDM parameters (peak

current, machining voltage, pulse duration and interval time) on the machining

characteristics such as electrode wear rate, material removal rate, surface

roughness and etc., is of great significance and could be of necessity Although

study of these parameters has been performed by many researchers, most of the

studies do not much consider both engineering philosophy (DOE) and

mathematical formulation (ANOVA), particularly in machining very hard

materials such as Tungsten Carbide. Therefore, the Taguchi method, which is a

powerful tool for parametric design of performance characteristics, is used to

determine the optimal machining parameters for minimum electrode wear ratio,

maximum material removal rate and minimum surface roughness in the EDM

operations. The experimental details when using the Taguchi method are

described.

A study by J. L. Lin, K. S. Wang, B. H. Yan, Y. S. Tarng[10], In this paper the

application of the Taguchi Method with fuzzy logic for optimizing for EDM

process with multiple process with multiple performance characteristics. The

machining parameters are optimized with consideration of the multiple

performance characteristics.

CHAPTER 3

EXPERIMENTAL METHODOLOGY

3.1 Taguchi Method

A scientific approach to plan the experiments is a necessity for efficient conduct of

experiments. By the statistical design of experiments the process of planning the

experiment is carried out, so that appropriate data will be collected and analyzed by

statistical methods resulting in valid and objective conclusions. When the problem

involves data that are subjected to experimental error, statistical methodology is the only

objective approach to analysis. Thus, there are two aspects of an experimental problem:

the design of the experiments and the statistical analysis of the data. These two points are

closely related since the method of analysis depends directly on the design of

experiments employed. The advantages of design of experiments are as follows:

Numbers of trials is significantly reduced.

Important decision variables which control and improve the performance of the

product or the process can be identified.

Optimal setting of the parameters can be found out.

Qualitative estimation of parameters can be made.

Experimental error can be estimated.

Inference regarding the effect of parameters on the characteristics of the process can

be made.

In the present work, the Taguchi‟s method, have been used to plan the experiments

and subsequent analysis of the data collected.

3.2 Taguchi’s Philosophy

Design of experiment (doe) is a powerful statistical technique for improving

product/process designs and solving production problems. A standardized version of the

doe, as forwarded by Dr. Genichi Taguchi, allows one to easily learn and apply the

technique product design optimization and production problem investigation. There are a

number of statistical techniques available for engineering and scientific

Design of experiment (doe) is a powerful statistical technique for improving

product/process designs and solving production problems. A standardized version of the

doe, as forwarded by Dr. Genichi Taguchi, allows one to easily learn and apply the

technique product design optimization and production problem investigation. There are a

number of statistical techniques available for engineering and scientific studies. Taguchi

has prescribed a standardized way to utilize the Design of Experiments (DOE) technique to

enhance the quality of products and processes.

Taguchi‟s comprehensive system of quality engineering is one of the greatest

engineering achievements of the 20th century. His methods focus on the effective

application of engineering strategies rather than advanced statistical techniques. The

Taguchi method was developed by Dr. Genichi Taguchi of Japan. It includes both upstream

and shop-floor quality engineering. Upstream methods efficiently use small-scale

experiments to reduce variability and remain cost-effective, and robust designs for large-

scale production and market place. Shop-floor techniques provide cost-based, real time

methods for monitoring and maintaining quality in production. The farther upstream a

quality method is applied, the greater leverages it produces on the improvement, and the

more it reduces the cost and time. Taguchi‟s philosophy is founded on the following three

very simple and fundamental concepts:

Quality should be designed into the product and not inspected into it.

Quality is best achieved by minimizing the deviations from the target. The product

or process should be so designed that it is immune to uncontrollable environmental

variables.

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

and the losses should be measured system-wide.

Taguchi proposes an “off-line” strategy for quality improvement as an alternative to

an attempt to inspect quality into a product on the production line. He observes that

poor quality cannot be improved by the process of inspection, screening and

salvaging. No amount of inspection can put quality back into the product. Taguchi

recommends a three-stage process: system design, parameter design and tolerance

design In the present work Taguchi‟s parameter design approach is used to study

the effect of process parameters on the various responses of the turning process.

3.3 Experimental Design Strategy

Taguchi recommends orthogonal array (OA) for lying out of experiments. These OA‟s

are generalized Graeco-Latin squares. To design an experiment is to select the most

suitable OA and to assign the parameters and interactions of interest to the appropriate

columns. The use of linear graphs and triangular tables suggested by Taguchi makes the

assignment of parameters simple. The array forces all experimenters to design almost

identical experiments.

In the Taguchi method the results of the experiments are analysed to achieve one or

more of the following objectives:

To establish the best or the optimum condition for a product or process

To estimate the contribution of individual parameters and interactions

To estimate the response under the optimum condition

The optimum condition is identified by studying the main effects of each of the

parameters. The main effects indicate the general trends of influence of each parameter.

The knowledge of contribution of individual parameters is a key in deciding the nature of

control to be established on a production process. The analysis of variance (ANOVA) is the

statistical treatment most commonly applied to the results of the experiments in

determining the percent contribution of each parameter against a stated level of confidence.

Study of ANOVA table for a given analysis helps to determine which of the parameters

need control.

Taguchi suggests two different routes to carry out the complete analysis. First, the

standard approach, where the results of a single run or the average of repetitive runs are

processed through main effect and ANOVA analysis. The second approach which Taguchi

strongly recommends for multiple runs is to use signal- to- noise ratio (S/N) for the same

steps in the analysis. The S/N ratio is a concurrent quality metric linked to the loss function.

By maximizing the S/N ratio, the loss associated can be minimized. The S/N ratio

determines the most robust set of operating conditions from variation within the results.

The S/N ratio is treated as a response of the experiment. Taguchi recommends the use of

outer OA to force the noise variation into the experiment i.e. the noise is intentionally

introduced into experiment. However, processes are often times subject to many noise

factors that in combination, strongly influence the variation of the response. For extremely

„noisy‟ systems, it is not generally necessary to identify specific noise factors and to

deliberately control them during experimentation. It is sufficient to generate repetitions at

each experimental condition of the controllable parameters and analyse them using an

appropriate S/N ratio.

In the present investigation, S/N data analysis has been performed. The effects of

the selected turning process parameters on the selected quality characteristics have been

investigated through the plots of the main effects. The optimum condition for each of the

quality characteristics has been established through S/N data analysis.

3.4 Taguchi method categories

3.4.1 Static Problems

Generally, a process to be optimized has several control factors which directly

decide the target or desired value of the output. The optimization then involves determining

the best control factor levels so that the output is at the target value. Such a problem is

called as a "STATIC PROBLEM".

This is best explained using a P-Diagram which is shown in figure 4.5 below ("P" stands

for Process or Product). Noise is shown to be present in the process but should have no

effect on the output! This is the primary aim of the Taguchi experiments - to minimize

variations in output even though noise is present in the process. The process is then said to

have become ROBUST.

3.4.1.1. Signal to noise Ratio

Once the experimental design has been determined and the trials have been carried

out, the measured performance characteristic from each trial can be used to analyse the

relative effect of the different parameters. The product/process/system design phase

involves deciding the best values/levels for the control factors. The signal to noise (S/N)

ratio is an ideal metric for that purpose.

Figure 3.1 : P-diagram for static problem

The loss-function discussed above is an effective figure of merit for making engineering

design decisions. However, to establish an appropriate loss-function with its k value to use

as a figure of merit is not always cost-effective and easy. Recognizing the Dilemma,

Taguchi created a transform function for the loss-function which is named as signal -to-

noise (S/N) ratio.

The S/N ratio, as stated earlier, is a concurrent statistic. A concurrent statistic is able to look

at two characteristics of a distribution and roll these characteristics into a single number or

figure of merit. The S/N ratio combines both the parameters (the mean level of the quality

characteristic and variance around this mean) into a single metric.

A high value of S/N implies that signal is much higher than the random effects of noise

factors. Process operation consistent with highest S/N always yields optimum quality with

minimum variation.

The S/N ratio consolidates several repetitions into one value. The equation for calculating

S/N ratios for “smaller is better” (LB),”larger is better” (HB) and “nominal is best” (NB)

types of characteristics are as follows:

1. Larger is better:

(S/N)HB= -10 log (MSDHB)

Where,

MSDHB= n

1(

yyyyn

22

3

2

2

2

1

1....

111 ),

n = no. of repetitions,

y= observed value.

This is usually chosen S/N Ratio for most desirable characteristics like “MRR” etc.

2. Smaller is better:

(S/N) LB= -10 log (MSDLB)

Where,

MSDLB= )...(1 22

3

2

2

2

1yyyy

nn

This is usually chosen S/N ratio for all undesirable characteristics like “TWR”, ROC” etc.

3. Nominal is best

(S/N)NB=10log [Variance

anSquareofme]

This case arises when a specified value is MOST desired, meaning that neither a

smaller nor a larger value is desirable. Examples are:

(I) Most parts in mechanical fittings have dimensions which are nominal-the-best type.

(ii) Ratios of chemicals or mixtures are nominally the best type.

(iii) Thickness should be uniform in deposition /growth /plating /etching.

The expressions for MSD are different for different quality characteristics. For the

„nominal is best‟ characteristic, the standard definition of MSD is used. For the other two

characteristics the definition is slightly modified. For „smaller is better‟, the unstarted target

value is zero. For „larger is better‟, the inverse of each large value becomes a small value

and again, the unstarted target value is zero. Thus for all three expressions, the smallest

magnitude of MSD is being sought.

3.5 Taguchi Design Step

STEP-1: Selection of process parameters and identification of responses,

STEP-2: Assignment of levels to the process parameters,

STEP-3: Selection of proper O.A and assignment of process parameters to the O.A,

STEP-4: Experiment is to be conducted based on orthogonal array,

STEP-5: Calculation of loss function and the S/N ratio

STEP-6: Calculation of mean S/N ratio and analyze the results,

STEP-7: Analysis of variance (ANOVA),

STEP-8: Selection of optimal combination of process parameters,

STEP-9: Verification test of optimal process parameter

3.6 Data Analysis A number of methods have been suggested by Taguchi for analyzing the data:

observation method, ranking method, column effect method, ANOVA, S/N ANOVA, plot

of average response curves, interaction graphs etc. However, in the present investigation

the following methods have been used:

ANOVA for S/N data

S/N response graphs

Interaction graphs

Residual graphs

The plot of average responses at each level of a parameter indicates the trend. It is a

pictorial representation of the effect of parameter on the response. The change in the

response characteristic with the change in levels of a parameter can easily be visualized

from these curves. Typically, ANOVA for OA‟s are conducted in the same manner as other

structured experiments

The S/N ratio is treated as a response of the experiment, which is a measure of the

variation within a trial when noise factors are present. A standard ANOVA can be

conducted on S/N ratio which will identify the significant parameters (mean and variation).

Interaction graphs are used to select the best combination of interactive parameters.

Residual plots are used to check the accuracy.

3.7 Advantages and disadvantages of Taguchi

An advantage of the Taguchi method is that it emphasizes a mean performance

characteristic value close to the target value rather than a value within certain specification

limits, thus improving the product quality. Additionally, Taguchi's method for experimental

design is straightforward and easy to apply to many engineering situations, making it a

powerful yet simple tool. It can be used to quickly narrow down the scope of a research

project or to identify problems in a manufacturing process from data already in existence.

Also, the Taguchi method allows for the analysis of many different parameters without a

prohibitively high amount of experimentation. In this way, it allows for the identification of

key parameters that have the most effect on the performance characteristic value so that

further experimentation on these parameters can be performed and the parameters that have

little effect can be ignored.

The main disadvantage of the Taguchi method is that the results obtained are only

relative and do not exactly indicate what parameter has the highest effect on the

performance characteristic value. The Taguchi method has been criticized in the literature

for difficulty in accounting for interactions between parameters.

CHAPTER 4

PILOT EXPERIMENT

4.1 PILOT EXPERIMENTATION

The effect of various input parameters i.e. pulse on, pulse off, current, electrode,

and work-piece were investigated through the pilot experimentation. One response was

selected for pilot experimentation namely material removal rate (MRR). The assignment

of factors was carried out using statistical software MINITAB. All the factors were varied

at three levels. The degrees of freedom required for the experiment was calculated, thus

the orthogonal array that can be used should have degrees of freedom (dof) greater than

11. L27 which can accommodate 3-level factors was used for conduct of experiments to

measures one response values namely, MRR. After the conduct of the 27 trials the mean

values for MRR is tabulated in Table. For the analysis of the result, Analysis by S/N ratio

performed and main effect plot for S/N ratio was obtained.

4.2 L27 Orthogonal Array along with results for EDM process during

pilot experimentation

Table 4.1 : L27 Result table for pilot experiment

Trial

No.

Current

(Amp)

Ton

(µs)

Toff

(µs)

Tool WP MRR

(mm3/min)

1 3 2 8 Brass MS 0.0765

2 3 2 8 Brass D2 0.3636

3 3 2 8 Brass E31 0.0750

4 3 5 5 Copper MS 0.4591

5 3 5 5 Copper D2 0.4675

6 3 5 5 Copper E31 0.3250

7 3 8 2 W MS 0.5102

8 3 8 2 W D2 0.0779

9 3 8 2 W E31 0.0250

10 12 2 5 W MS 0.3316

11 12 2 5 W D2 0.7272

12 12 2 5 W E31 0.4750

13 12 5 2 Brass MS 0.5103

14 12 5 2 Brass D2 2.9090

15 12 5 2 Brass E31 0.3000

16 12 8 8 Copper MS 3.2398

17 12 8 8 Copper D2 2.4415

18 12 8 8 Copper E31 0.2500

19 20 2 2 Copper MS 0.6887

20 20 2 2 Copper D2 0.5714

21 20 2 2 Copper E31 2.3750

22 20 5 8 W MS 2.5765

23 20 5 8 W D2 2.5714

24 20 5 8 W E31 0.5500

25 20 8 5 Brass MS 1.6326

26 20 8 5 Brass D2 8.0519

27 20 8 5 Brass E31 4.5000

The relationship of MRR with current, pulse on time and pulse off time during the

machining using copper ,brass and graphite electrode in dielectric is shown in the Figure

4.1. It was observed that at low current, MRR is low but increases sharply with increased

current. The current was observed to be most significant factor affecting MRR. The

MRR increased with increase in the pulse on time and initially increased & then after

decreased with increase in pulse off time. The work-piece material had significant effect

on MRR. and the electrode material had insignificant effect on MRR.

Graph 4.1: Main effect plot for SN ratio

Table 4.2: Response Table For SN ratio

20123

5

0

-5

-10

-15

852 852

WCuBrass

5

0

-5

-10

-15

En31D2MS

current

Me

an

of

SN

ra

tio

sTon Toff

Tool WP

Main Effects Plot for SN ratiosData Means

Signal-to-noise: Larger is better

Level Current Ton Toff Tool WP

1 -15.1997 -8.1135 -7.5188 -2.7999 -3.4351

2 -2.2139 -2.1026 -0.7233 -1.9771 -0.2024

3 5.1204 -2.0771 -4.0511 -7.5161 -8.6536

Rank 1 4 3 5 2

CHAPTER 5

EXPERIMENTAL DESIGN

5.1 Introduction

The full factorial design is referred as the technique of defining and investigating

all possible conditions in an experiment involving multiple factors while the fractional

factorial design investigates only a fraction of all the combinations. Although these

approaches are widely used, they have certain limitations: they are inefficient in time and

cost when the number of the variables is large; they require strict mathematical treatment

in the design of the experiment and in the analysis of results; the same experiment may

have different designs thus produce different results; further, determination of

contribution of each factors is normally not permitted in this kind of design. The Taguchi

method has been proposed to overcome these limitations by simplifying and

standardizing the fractional factorial design. The methodology involves identification of

controllable and uncontrollable parameters and the establishment of a series of

experiments to find out the optimum combination of the parameters which has greatest

influence on the performance and the least variation from the target of the design. The

effect of various parameters (work-piece material, electrode, dielectric, pulse on time,

pulse off time current and powder) and some of the effects of interactions between the

main factors were also be studied using parameterization approach developed by

Taguchi.

5.2 Procedure of experimental design

The whole procedure of Taguchi method is as under.

1. Establishment of objective functions.

2. Selection of factors and/or interactions to be evaluated.

3. Identifications of uncontrollable factors and test conditions.

4. Selection of number of levels for the controllable and uncontrollable factors.

5. Calculation total degree of freedom needed

6. Select the appropriate Orthogonal Array (OA).

7. Assignment of factors and/or interactions to columns.

8. Execution of experiments according to trial conditions in the array.

9. Analyze results.

10. Confirmation experiments

5.3 Establishment of objective function

The objective of the study is to evaluate the main effects of work-piece material,

electrode, pulse off, pulse on time, current and on the MRR. o studied.

5.4 Degree of freedom (dof)

The number of factors and their interactions and level for factors determine the

total degree of freedom required for the entire experiment. The degree of freedom for

each factor is given by the number of levels minus one.

dof for each factor : k-1=3-1=2

where k is the number of level for each factor

Total Dof of experiment = No of trials – 1 = 27 – 1 = 26

5.5 Selection of Factors

The determination of which factors to investigate depends on the responses of

interest. The factors affects the responses were identified using cause and effect analysis,

brainstorming and pilot experimentation. The lists of factors studied with their levels are

shown in the Table 5.1.

Table 5.1 : Factors and their Levels

FACTORS LEVELS

LEVEL 1 LEVEL 2 LEVEL 3

Current (amp) 3 12 20

Pulse On Time 3 5 8

Pulse Off Time 9 5 3

Tool Material Copper Brass Graphite

WP Material M.S. EN31 D2

5.6 Orthogonal array

OA plays a critical part in achieving the high efficiency of the Taguchi method.

OA is derived from factorial design of experiment by a series of very sophisticated

mathematical algorithms including combinatorics, finite fields, geometry and error

correcting codes. The algorithms ensure that the OA to be constructed in a statistically

independent manner that each level has an equal number of occurrences within each

column; and for each level within one column, each level within any other column will

occur an equal number of times as well. Then, the columns are called orthogonal to each

other. OA‟s are available with a variety of factors and levels in the Taguchi method.

Since each column is orthogonal to the others, if the results associated with one level of a

specific factor are much different at another level, it is because changing that factor from

one level to the next has strong impact on the quality characteristic being measured.

Since the levels of the other factors are occurring an equal number of times for each

level of the strong factor, any effect by these other factors will be ruled out. The Taguchi

method apparently has the following strengths:

1. Consistency in experimental design and analysis.

2. Reduction of time and cost of experiments.

3. Robustness of performance without removing the noise factors.

The selection of orthogonal array depends on:

1. The number of factors and interactions of interest

2. The number of levels for the factors of interest

Taguchi‟s orthogonal arrays are experimental designs that usually require only a fraction

of the full factorial combinations. The arrays are designed to handle as many factors as

possible in a certain number of runs compared to those dictated by full factorial design.

The columns of the arrays are balanced and orthogonal. This means that in each pair of

columns, all factor combinations occur same number of times. Orthogonal designs allow

estimating the effect of each factor on the response independently of all other factors.

Once the degrees of freedom are known, the next step, selecting the orthogonal array

(OA) is easy. The number of treatment conditions is equal to the number of rows in the

orthogonal array and it must be equal to or greater than the degrees of freedom. The

interactions to be evaluated will require an even larger orthogonal array. Once the

appropriate orthogonal array has been selected, the factors and interactions can be

assigned to the various columns.

Table 5.2 : L27 Orthogonal Array

Trial

No.

Current

(amp)

Ton

(µs)

Toff

(µs)

Tool

Mtr.

WP

Mtr.

1 3 3 9 Copper MS

2 3 3 9 Copper D2

3 3 3 9 Copper EN31

4 3 5 5 Brass MS

5 3 5 5 Brass D2

6 3 5 5 Brass EN31

7 3 8 3 Gr. MS

8 3 8 3 Gr. D2

9 3 8 3 Gr. EN31

10 12 3 5 Gr. MS

11 12 3 5 Gr. D2

12 12 3 5 Gr. EN31

13 12 5 3 Copper MS

14 12 5 3 Copper D2

15 12 5 3 Copper EN31

16 12 8 9 Brass MS

17 12 8 9 Brass D2

18 12 8 9 Brass EN31

19 20 3 3 Brass MS

20 20 3 3 Brass D2

21 20 3 3 Brass EN31

22 20 5 9 Gr. MS

23 20 5 9 Gr. D2

24 20 5 9 Gr. EN31

25 20 8 5 Copper MS

26 20 8 5 Copper D2

27 20 8 5 Copper EN31

5.7 Experimental set up

The experiments have been conducted on the Electrical Discharge Machine S25

Of Sparkonix Ltd. available at Vishwakarma Government engineering college., in

Machine Tool lab. A large number of input parameters which can be varied in the EDM

process, i.e. pulse on, pulse off, polarity, peak current, electrode gap and type of flushing,

each having its own effect on the output parameters such as tool wear rate, material

removal rate, surface finish and hardness of machined surface. Current, pulse on and

pulse off are the parameters which were varied on the machine for experimentation. The

ranges of these parameters for the experimental work have been selected on the basis of

results of pilot experiments. The input parameters have been fixed for during the whole

experimentation, as given in the Table 5.3.

Table 5.3 : Constant Input Parameter

Sr No. Parameter Value

1 Machining Time 20 min.

2 Die-electric Fluid HC

3 Polarity Straight

Figure 5.1 SPARKONIX S25 SERIES Courtesy “EDM Lab”

Figure 5.2 EDM Work table Courtesy “EDM Lab”

5.8 Analyses of results

Signal-to-noise ratio

The parameters that influence the output can be categorized into two classes,

namely controllable (or design) factors and uncontrollable (or noise) factors. Controllable

factors are those factors whose values can be set and easily adjusted by the designer.

Uncontrollable factors are the sources of variation often associated with

operational environment. The best settings of control factors as they influence the output

parameters are determined through experiments. From the analysis point of view, there

are three possible categories of the response characteristics explained below.

𝑦2

𝑟

𝑖=1

𝑖 = 𝑠𝑢𝑚𝑎𝑡𝑖𝑜𝑛 𝑜𝑓𝑎𝑙𝑙 𝑟𝑒𝑠𝑝𝑜𝑛𝑐𝑒 𝑣𝑎𝑙𝑢𝑒 𝑜𝑓𝑒𝑎𝑐𝑕 𝑡𝑟𝑖𝑎𝑙

MSD = Mean square deviation

yi= Observed value of the response characteristic

y0= nominal or target value of the results

Signal to noise ratio for response characteristics

The parameters that influence the output can be categorized in two categories,

controllable factors and uncontrollable factors. The control factors that may contribute to

reduced variation can be quickly identified by looking at the amount of variation present

in response. The uncontrollable factors are the sources of variation often associated with

operational environment. For this experimental work, response characteristics have given

in the Table 5.4.

Table 5.4 : Response Characteristics

Response Name Response Type Unit

Material Removal Rare Higher is Better mm3 / min

Tool Wear Rate Lower is Better mm3 / min

Micro Hardness Higher is Better HVN

Surface Roughness Lower is Better Microns

Measurement of F-value of Fisher’s F ratio

The principle of the F test is that the larger the F value for a particular parameter,

the greater the effect on the performance characteristic due to the change in that process

parameter. F value is defined as:

F = 𝑀𝑆 𝑓𝑜𝑟 𝑎 𝑡𝑒𝑟𝑚

𝑀𝑆 𝑓𝑜𝑟 𝑡𝑕𝑒 𝑒𝑟𝑟𝑜𝑟 𝑡𝑒𝑟𝑚

Computation of average performance:

Average performance of a factor at certain level is the influence of the factor at

this level on the mean response of the experiments.

Analysis of variance

The knowledge of the contribution of individual factors is critically important for

the control of the final response. The analysis of variance (ANOVA) is a common

statistical technique to determine the percent contribution of each factor for results of the

experiment. It calculates parameters known as sum of squares (SS), pure SS, degree of

freedom (DOF), variance, F-ratio and percentage of each factor. Since the procedure of

ANOVA is a very complicated and employs a considerable of statistical formulae, only a

brief description of is given as following.

The Sum of Squares (SS) is a measure of the deviation of the experimental data

from the mean value of the data.

Let „A‟ be a factor under investigation

𝑆𝑆𝑇 = (𝑦𝑖 − 𝑇)2

𝑁

𝑖=1

Where N = Number of response observations, T is the mean of all observations i y is

the i response

Factor Sum of Squares ( A SS ) - Squared deviations of factor (A) averages

𝑆𝑆𝑇 = (𝑦𝑖 − 𝑇)2

𝑁

𝑖=1

−𝑇2

𝑁

Where

Average of all observations under Ai level = Ai / nAi

T = sum of all observations

T =Average of all observations = T / N

Number of observations nAi = under Ai level

Error Sum of Squares ( e SS ) - Squared deviations of observations from factor (A)

Averages

𝑆𝑆𝑒 = (𝑦𝑖−𝐴𝑗 )2

𝑛𝐴𝑖

𝑖=1

𝐾𝑎

𝑗=1

5.9 Material Composition for work-piece & electrode material

Three work-piece materials Mild Steel, D2 and EN31 and three electrode

materials Graphite, Copper and Brass were used. Before the start of experimentation, The

percentage composition of the work-piece and electrode material is provided in Table 5.5.

Table 5.5 Work-Piece Material Composition

Work

piece % Composition

Fe C Si Mn P S Cr Mo Ni Co Cu V T W

MS 96.00 2.0 0.6 1.65 - - - - - - 0.6 - - -

D2 83.5 1.70 0.30 0.30 0.03 0.03 12.3 0.60 - - 0.05 0.10 - 0.50

EN31 92.3 0.3 1.0 0.4 0.04 - 5.0 - - - - - 1.0 -

Table 5.6 Electrode Material Composition

Figure 5.3 Work Piece (EN31) After machining

Electrode % Composition

W Cu Ni Z Ti Lead

Copper - 99.78 0.121 0.047 0.014 0.026

Brass - 67.00 - 33.00 - -

Graphite High Carbon Content (95-99%)

Figure 5.4 Work Piece (D2) After machining

Figure 5.5 Work Piece (MS) After machining

Figure 5.6 Tool (Copper)

Figure 5.7 Tool (Graphite)

Figure 5.8 Tool (Brass)

CHAPTER 6

RESULT AND ANALYSIS OF MRR

6.1 Introduction

The effects of parameters i.e. work-piece, dielectric, electrode, pulse on time,

pulse off time, current, were evaluated using ANOVA & S/N ratio, General linear Model

and S/N ratio. A confidence interval of 95% has been used for the analysis. Two runs for

each of 27 trails were completed to measure the Signal to Noise ratio(S/N Ratio).

6.2 Results For MRR

The results for MRR for each of the 27 treatment conditions with repetition are

given in Table. MRR of each sample is calculated from weight difference of work-piece

before and after the performance trial, which is given by:

𝑀𝑅𝑅 = 𝑤𝑒𝑖𝑔𝑕𝑡 𝑑𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑐𝑒

𝜌 × 𝑡𝑖𝑚𝑒 × 1000 𝑚𝑚3/𝑚𝑖𝑛

Table 6.1 Result Table For Final Experiment

SR

No

Current Ton Toff Tool Mtr. WP Mtr. MRR Mean

MRR

S/N

Ratio I II

1 3 3 9 Copper MS 0.0682 0.0695 0.068878 -23.238446

2 3 3 9 Copper D2 0.0649 0.0675 0.066234 -23.578411

3 3 3 9 Copper EN31 0.0164 0.0079 0.012171 -38.293437

4 3 5 5 Brass MS 0.3444 0.3176 0.330995 -9.603574

5 3 5 5 Brass D2 0.2006 0.1273 0.163961 -15.705187

6 3 5 5 Brass EN31 0.1862 0.1816 0.183882 -14.709236

7 3 8 3 Gr. MS 0.4994 0.8584 0.67889 -3.3640079

8 3 8 3 Gr. D2 0.4643 0.4571 0.460714 -6.7313664

9 3 8 3 Gr. EN31 0.3737 0.2493 0.311513 -10.130472

10 12 3 5 Gr. MS 1.0925 0.9152 1.003827 0.03317339

11 12 3 5 Gr. D2 1.9422 2.0539 1.998052 6.01213551

12 12 3 5 Gr. EN31 2.2658 1.8072 2.036513 6.17774441

13 12 5 3 Copper MS 6.8265 6.6282 6.72736 16.556893

14 12 5 3 Copper D2 4.8097 4.2760 4.542857 13.1465816

15 12 5 3 Copper EN31 6.9243 8.5908 7.757566 17.7945093

16 12 8 9 Brass MS 1.7978 2.7596 2.278699 7.15373916

17 12 8 9 Brass D2 2.5558 2.1864 2.371104 7.49901168

18 12 8 9 Brass EN31 2.3461 3.0414 2.69375 8.60714575

19 20 3 3 Brass MS 0.8846 0.2526 0.568559 -4.9044942

20 20 3 3 Brass D2 0.5110 0.8169 0.663961 -3.5571481

21 20 3 3 Brass EN31 0.5559 0.7993 0.677632 -3.3801273

22 20 5 9 Gr. MS 2.5593 1.9707 2.264987 7.10131521

23 20 5 9 Gr. D2 5.7961 5.1506 5.473377 14.7651067

24 20 5 9 Gr. EN31 3.7441 2.2882 3.016118 9.58896778

25 20 8 5 Copper MS 20.4069 21.7806 21.09375 26.4830759

26 20 8 5 Copper D2 15.9851 14.4773 15.23117 23.6546646

27 20 8 5 Copper EN31 12.3783 11.2211 11.79967 21.437398

6.3 Result OF S/N ratio Of MRR

The S/N ratio consolidates several repetitions into one value and is an indication

of the amount of variation present. The S/N ratios have been calculated to identify the

major contributing factors that cause variation in the MRR. S/N ratio has been calculated

for each reading using MINITAB. MRR is “Higher is better” type response which is

given by:

(S/N)HB = -10 log (MSDHB)

Where MSDHB = 1

𝑟

1

𝑦 𝑗2

𝑟𝑗 =1

MSDHB = Mean Square Deviation for higher-the-better response.

Table 6.2 : Response Table for SN ratio

Level Current Ton Toff Tool WP

1 -16.1505 -9.4143 1.7145 3.7736 1.8020

2 9.2201 -4.3262 4.8645 3.1778 1.7228

3 10.1321 8.2899 -3.3772 2.6058 -0.3231

Rank 1 2 3 4 5

Graph 6.1 : Main Effects Plot for Means

Table 6.3 : Analysis of variance

Source DF Seq SS Adj SS Adj MS F P

Current 2 4005.81 4005.81 2002.90 129.23 0.000

Ton 2 1556.86 1553.86 776.93 50.13 0.000

Toff 2 311.32 311.32 155.66 10.04 0.001

Tool Material 2 249.41 249.41 124.70 8.05 0.004

WP 2 26.12 26.12 13.06 0.84 0.449

Error 16 247.97 247.97 15.50

Total 26 6394.49

20123

8

6

4

2

0

853 953

GraphiteBrassCopper

8

6

4

2

0

EN31D2MS

Current

Me

an

of

Me

an

s

Ton Toff

Tool Material WP

Main Effects Plot for MeansData Means

Graph 6.2 Main Effects Plot for SN ratios

Table 6.2 shows Response table for S/N ratio of MRR at 95% confidence interval.

Current was observed to be the most significant factor affecting the MRR, followed by

pulse on time, pulse off time, tool material and work piece material. Graph 6.1 shows

Main Effects for Means for MRR and Graph 6.2 shows Main Effects Plot for SN ratios

for MRR. Both the graphs gives same results for optimum conditions.

6.4 Confirmation Test

Once the optimum level of process parameter has been selected final step is to predict

and verify the improvement of the performance characteristics using the optimal level of

process parameters. As a general rule optimum performance can be calculated by using

following expression.

20123

0

-6

-12

-18

-24

853 953

GraphiteBrassCopper

0

-6

-12

-18

-24

EN31D2MS

Current

Me

an

of

SN

ra

tio

s

Ton Toff

Tool Material WP

Main Effects Plot for SN ratiosData Means

Signal-to-noise: Larger is better

T = Grand total of all results

N = Total No of results

Yopt = Performance at optimum conditions.

Table 6.4 Confirmation Test Reading

Current Pulse On time Pulse off Time Machining Time MRR

20 8 5 20min 20.4196

𝑌 𝑜𝑝𝑡 = 𝑇

𝑁+ 𝐴 3 − 𝑇

𝑁 + 𝐵 3 − 𝑇

𝑁 + 𝐶 2 − 𝑇

𝑁 + 𝐷1 − 𝑇

𝑁 + 𝐸 1 − 𝑇

𝑁

= 3.49912 + (6.75432-3.49912) + (6.324362-3.49912) + (5.982424-3.49912)

+ (7.47773-3.49912) + (3.89066 – 3.49912)

Yopt = 16.4334

% error = 𝑌𝑎𝑐𝑡 .− 𝑌 𝑡 𝑕 .

𝑌 𝑡 𝑕

= 20.4186−16.4334

16.4334 x 100

= 24.25 %

CHAPTER 7

RESULT CONCLUSION AND RECOMMNDATION

7.1 Optimal Design For MRR

In this experimental analysis, the main effect plot in Figure used to estimate the

mean MRR. In S/N ratio highest MRR was observed when work-piece material MS was

machined with copper tool at pulse on time 8μs, pulse off time 5 μs & current 20Amp. It

is observed that current at 20Amp has optimal value for higher MRR because it decreases

variation .The results of ANOVA for S/N ratios of MRR ( table no 7.1) indicates that for

α = 0.5 value current, Ton ,Toff & are most significant machining parameters while

work piece material is insignificant parameter affecting MRR .

Table : 7.1 Optimum Condition

Factors Value

Current 20 Amp.

Pulse On Time 8 µs

Pulse Off Time 5 µs

Tool Mtr. Copper

Work-Piece Mtr. M.S.

7.2 Recommendations for future work

In this experiment effect of process parameters on performance of MRR was

analyzed. Further the effect of process parameters on performance measure of other

performance parameter for example TWR and surface roughness can be carried out .

Only three work-piece materials, namely D2, MS and EN31 had been used. Other

materials such as titanium, H11, OHNS die steel and tungsten hot work die steel can be

machined.

flexible modeling tools like Artificial Neural Network, Genetic Algorithms, and Fuzzy

logic which are highly efficient in mapping between input variables and output variables.

By using this models and the data obtained from the experiment a process model to

obtain optimum condition .

APPENDIX-A TECHNICAL SPECIFICATION OF EDM MACHINE

The experiment been conducted on Electric discharge machine model S-25 ,

Sparkonix machines Ltd. pune. Technical Data for machines as under :

1. Electrical Data

Supply Voltage 415V 3Phase 50Hz.

Max Machine Current 25Amp.

Current Range 3 range of 6amp each

Single range of 3 amp

2 range of 2amp each

2. Machine specification

Work Tank 600 x 400 x 275 mm

Work Table 400x 300 mm

X- Travel 200 mm

Y- Travel 150 mm

Z- Travel 200 mm

APPENDIX- B

SPECIFICATION OF MEASURING INSTRUMENT

1. Weighing Machine

Company : SHIMADZU CORPORATION (Japan)

Type : AX 200

M/C No. : D432612833

Capacity : 200 gm.

Readability : 0.1 mg.

APPENDIX- C

TECHNICAL SPECIFICATION OF DIE-ELECTRISC MEDIUM

Die-electric Fluid – Hydro-Carbon Oil

Specific Gravity (at 15 C) 0.797

Kinematic Viscosity (at 40 C) 1.8

Flash Point ( C) 75

Boiling Point ( C) 200-250

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