Parametric Optimization of Electro Discharge Machining ...Die sinking EDM and Wire EDM are two...

IJSRD - International Journal for Scientific Research & Development| Vol. 3, Issue 03, 2015 | ISSN (online): 2321-0613

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Parametric Optimization of Electro Discharge Machining Process for

MRR, Micro Hardness and Surface Roughness

Patel Umesh K1 Prof. Vidhya Nair

2

1,2Department of Mechanical Engineering

1,2LDRP – ITR, Gandhinagar

Abstract— The Electro Discharge Machining is a widely

accepted non-convection machining process. There are so

many process parameters which are statistically affects the

machining. Best combination of these parameters gives

excellent result. For current research pulse on time, pulse off

time, peak current and spark gap set voltage is selected to

optimize material removal rate. Surface roughness and

micro hardness is also optimizing for better result. The

analysis is based on Response surface Methodology.

Keywords: E.D.M., Taguchi Technique, ANOVA, Surface

Finish, MRR

I. INTRODUCTION

Electrical Discharge Machining is a one type of Non-

convectional machining Process. Non-convectional

processes are design to machine hard material and to

produce complex shapes which are not economical to

produce by convectional method. These methods become

more popular with the employment of CNC facilities.

Electrical Discharge Machining is most popular to make

very fine hole. Die sinking EDM and Wire EDM are two

widely used method of Electrical Discharge Machining.

Generally Wire Electrical Discharge Machining is used to

cut complex shape. These methods have found successful

applications in several important industries for machining

of components having complicated shapes made of hard

materials like tungsten carbides, super-alloys, ceramics,

refractory materials as well as common material.

Electrical discharge machining (EDM) is one of the

most extensively used non conventional material removal

processes. Its unique feature of using thermal energy to

machine electrically conductive parts regardless of hardness

has been its distinctive advantage in the manufacture of

mould, die, automotive, aerospace and surgical components.

In addition, EDM does not make direct contact between the

electrode and the workpiece eliminating mechanical

stresses, chatter and vibration problems during machining.

Today, an electrode as small as 0.1 mm can be used to „drill‟

holes into curved surfaces at steep angles without drill

„wander‟.

II. LITERATURE REVIEW

So many research papers and articles are survey on EDM

those are related to know the effect of process parameter on

performance of process. The materials investigated on EDM

are most of HSS, other Tool material, Hot Die material,

Cold Die material, Nickel alloys and Titanium alloys which

are hard compare to other material. These materials are AISI

M2, AISI D2, AISI D3, AISI D5, AISI H11, AISI 4140,

SKD 11, En 16, En 19, En 31, En 32, 1040, 2379, 2738,

Inconel, Ti alloys, Al alloys, 7131 cemented, Tungsten

Carbide (WC) etc. Different author use different

combination of process parameter. They analyze the

experimental data by plotting Interaction graphs, Residual

plots for accuracy and Response curves. Some other

methods used by different author for analysis of Taguchi‟s

DOE data regarding to EDM and EDM are Regression

analysis, Response Surface Methodology, Central

Composite Design (CCD), Feasible-Direction Algorithm,

SA algorithm, Pareto, Artificial Bee Colony (ABC), Grey

Relational Analysis, Genetic Algorithm, Fuzzy clustering,

Artificial Neural Network, Tabu-Search Algorithm,

Principle component method etc. Most of the author used

L27 Orthogonal Array. Generally the effect of Pulse ON

time, Pulse OFF time, Spark gap set Voltage, Peak current,

Flushing Pressure, Work piece height, wire tension and wire

feed on the material removal rate, surface roughness, kerf

and gap current is investigated.

I found in literature survey that nobody did a

process parameter optimization for SS410with Copper

Tungsten electrode. SS410is a low alloy high strength alloy

steel and Copper Tungsten has low wear rate so for present

work this material are selected for analysis and optimization.

III. EXPERIMENTAL PROCEDURE

The Conventional machining processes were developed to

machine the different kind of reinforcements. But they lost

their competitive edge to non-conventional machining

because of poor surface finish, high tool wear rate and high

tooling cost. To Increasing the weight ratio of Copper in has

high tensile strength, electrical conductivity and low

resistance. Powder metallurgy has been an effective method

of producing copper composite material. Tool can be made

up of copper and tungsten material. So increase production

rate, reduced machining cost, tool wear rate is decreased and

improved surface finish of the composite materials. So

Copper-tungsten compsite material is selected for current

project work.

IV. DESIGN OF EXPERIMENTS

A. Selection of orthogonal array

Three level of variable process parameter is chosen for

convenience for experiment and analysis. Based on the

literature survey the levels are decided. Table 4 show the

process parameter called factor, their symbol, units and

range as a machine units and actual units.

The following parameters are remaining constant

during process:-

1) Tool Material : Copper-Tungsten Composite

2) Work piece Material : SS410

3) Dielectric Fluid : Deionized Water

4) Flushing Pressure : 12 kgf/cm

Parametric Optimization of Electro Discharge Machining Process for MRR, Micro Hardness and Surface Roughness

(IJSRD/Vol. 3/Issue 03/2015/797)


Level 1 Level 2 Level 3

Pulse ON time 70 90 100

Pulse OFF time 50 60 90

Peak current 10 15 20

Servo Voltage 35 40 45

Pulse ON time 70 90 100

Table-1&2 Levels of various control factors (A) (B) (C) (D)

1 70 50 10 35

2 70 50 10 35

3 70 50 10 35

4 70 60 15 40

5 70 60 15 40

6 70 60 15 40

7 70 90 20 45

8 70 90 20 45

9 70 90 20 45

10 90 50 15 45

11 90 50 15 45

12 90 50 15 45

13 90 60 20 35

14 90 60 20 35

15 90 60 20 35

16 90 90 15 40

17 90 90 15 40

18 90 90 15 40

19 100 50 20 40

20 100 50 20 40

21 100 50 20 40

22 100 60 15 45

23 100 60 15 45

24 100 60 15 45

25 100 90 15 30

26 100 90 15 30

27 100 90 15 30

Table- 2 T a g u c h i L 2 7 OA

B. Selection of material

An SS410is a high strength, low alloy steel that finds its best

application where there is need for more strength per unit of

weight. Less of this material is needed to fulfill given

strength requirements than is necessary with regular carbon

steels. Grade 50 is used in general plate applications when

the plate will be riveted, bolted, or welded. Grade 50 is a

Columbium-Vanadium steel that offers a minimum yield of

50,000 PSI. In addition, SS410 Grade 50 is noted for its

increased resistance to atmospheric corrosion. Grade 50

contains more alloying elements than plain carbon steel and

thus is somewhat more difficult to form. Grade 50 is more

difficult to cold work, but can be successfully bent or shaped

but requires more force than plain carbon steel.

Machinability is rated at 66%, Average cutting speed 110

ft/min. Easily welded by all commercial methods.

Constitute element % contribution

Carbon (C) 0.23

Manganese (Mn) 1.35

Silicon (Si) 0.15-0.4

Phosphorus (P) 0.04

Sulfur (S) 0.05

Vanadium (V) 0.01-0.05

Niobium (Nb) 0.005-0.05

Table-3: Chemical Composition of AISI A2 Tool Steel

C. Experimental work

The experiments willperformed on F-50 SPARK EROSION

MACHINE. the Following steps will followed in the cutting

operation. First A tool will mounted on machine, The work

piece is mounted on the work table, The program is made

for drill to work piece, All constant parameter are set first

and then the variable parameters are set for first reading as

per Taguchi‟s T27 array.After drilling first piece the

variable parameters are set for second drill.The required

measurements are continuously carried out during and after

drilling operation.

Fig. 1: Wire cut EDM Machine

SR NO MRR SR MH

1 0.9878 2.23 171.6

2 0.9500 2.21 172.3

3 0.9697 2.15 170.5

4 0.9073 1.00 180.1

5 0.8859 1.81 181.2

6 0.8974 1.80 182.0

7 0.6821 1.79 185.3

8 0.6985 1.78 185.7

9 0.8021 1.75 186.4

10 1.1982 2.10 201.0

11 1.1966 2.08 201.6

12 1.1916 2.09 202.0

13 1.1767 2.11 167.0

14 1.1752 2.06 168.4

15 1.1703 2.07 164.0

16 0.5346 1.70 192.0

17 0.5401 1.65 191.5

18 0.5214 1.68 189.4

19 1.4003 2.02 196.0

20 1.3823 1.90 195.8

21 1.3511 2.04 197.0

22 1.0684 1.95 204.0

23 1.4190 1.96 207.6

24 1.0947 1.94 204.8




25 1.0059 1.90 188.0

26 1.0026 1.89 190.0

27 1.0075 1.88 187.0

V. ANALYSIS BY RESPONSE SURFACE METHODOLOGY (RSM)

1009070

1.2

1.1

1.0

0.9

0.8

906050

201510

1.2

1.1

1.0

0.9

0.8

454035

TON

Me

an

TOFF

I V

Main Effects Plot for MRRData Means

45

0.7 40

0.8

0.9

10

1.0

15 3520

MRR

V

I

TON 85

TOFF 70

Hold Values

Surface Plot of MRR vs V, I

45

1.50 40

1.65

1.80

10

1.95

15 3520

SR

V

I

TON 85

TOFF 70

Hold Values

Surface Plot of SR vs V, I

20

17515

180

185

190

601075

90

MH

I

T OFF

TON 85

V 40

Hold Values

Surface Plot of MH vs I, TOFF

VI. OPTIMIZATION

The method makes use of an objective function, D(X),

called the desirability function and transforms an estimated

response into a scale free value (de) called desirability. The

desirable ranges are from zero to one. The factor settings

with maximum total desirability are considered to be the

optimal parameter conditions. So, higher value of

Desirability is desired for optimal value. The simultaneous

objective function is a geometric mean of all transformed

responses, given as

De = (d1 x d2 x d3 x ..... dn)1/n

CurHigh

Low0.99164D

Optimal

d = 0.99164

Maximum

MRR

y = 1.4918

0.99164

Desirability

Composite

35.0

45.0

10.0

20.0

50.0

90.0

70.0

100.0TOFF I VTON

[100.0] [50.0] [20.0] [35.0]




CurHigh

Low1.0000D

Optimal

d = 1.0000

Minimum

SR

y = 1.3742

1.0000

Desirability

Composite

35.0

45.0

10.0

20.0

50.0

90.0

70.0

100.0TOFF I VTON

[70.0] [77.4747] [14.9495] [40.5556]

CurHigh

Low0.56410D

Optimal

d = 0.56410

Maximum

MH

y = 212.5127

0.56410

Desirability

Composite

35.0

45.0

10.0

20.0

50.0

90.0

70.0

100.0TOFF I VTON

[100.0] [50.0] [13.1313] [45.0]

VII. CONCLUSION

After completion of thesis work successfully the following

conclusion is carried out about the effect of process

parameter and the optimization of Material removal rate,

Surface roughness and Micro-hardness.

A. The effect of Process Parameter

1) For MRR,

TON : Significant, increasing MH

TOFF : Significant, decreasing MH

V: Not much Significant as TON and TOFF,

increasing MH

I: Not much Significant as TON and TOFF,

decreasing up to 40 than increasing

2) For SR,

TON : Not much Significant as TOFF, increasing

SR

TOFF : Significant, decreasing SR

V : Significant, dcreasing upto 15 than increasing

I : Not much Significant as V and TOFF,

decrasing up to 40 than increasing

3) For MH

TON : More Significant, increasing MH

TOFF : Significant, decrasing MH up to the value

of 60 than increasing

V : Significant, decreasing MH

I : Significant, increasing MH

B. Optimization of Response

1) For MRR,

The optimum value of MRR is 1.49 mm3/min calculated by

software which is confirmed by doing experimental. During

this experiment the optimum value is found 1.48 mm3/min.

For SR,

The optimum value of SR is 1.37 Ra calculated by software

which is confirmed by doing experimental. During this

experiment the optimum value is found 1.4 Ra.

2) For MH

The optimum value of SR is 212 BHN calculated by

software which is confirmed by doing experimental. During

this experiment the optimum value is found 209 BHN.

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