parametric optimization during wedm machining of aisi d3 ... · parametric optimization during wedm...

IJSRD - International Journal for Scientific Research & Development| Vol. 2, Issue 03, 2014 | ISSN (online): 2321-0613

All rights reserved by www.ijsrd.com 1805

Parametric Optimization during WEDM Machining of AISI D3 using

Response Surface Methodology

Nilesh K.Patel1 Sandip Patel

2 Yuvraj raol

3

1 P.G. Student

2, 3 Assistant Professor

1, 2 MEC Basna

3 LCIT Bhandu

Abstract— Wire electrical discharge machining (WEDM) is

a specialised thermal machining process capable of

accurately machining parts with varying hardness or

complex shapes, which have sharp edges that are very

difficult to be machined by the main stream machining

processes. This paper studied the effect of various process

parameters like: Current, Pulse on time, Pulse off time and

Wire tension on responses like MRR and SR. Central

composite design (CCD) is used for experimentation and

Response surface methodology is applied for developed

second ordered mathematical model. The adequacy of the

above the proposed models have been tested through the

analysis of variance (ANOVA).

Keywords: ANOVA, CCD, MRR, SR, WEDM, RSM.

I. INTRODUCTION

Wire electrical discharge machining (WEDM) is a

specialized thermal machining process capable of accurately

machining parts with varying hardness or complex shapes,

which have sharp edges that are very difficult to be

machined by the main stream machining processes. This

practical technology of the WEDM process is based on the

conventional EDM sparking phenomenon utilizing the

widely accepted non-contact technique of material removal.

Since the introduction of the process, WEDM has evolved

from a simple means of making tools and dies to the best

alternative of producing micro-scale parts with the highest

degree of dimensional accuracy and surface finish quality

[1]. R. E. Williams (1991) et. al. [2] studied the surface

morphology in Wire cut EDM. Surface roughness profiles

were studied with stochastic modeling and analysis

methodology to better understand the process mechanism.

Scanning Electron Microscope (SEM) highlighted the

important feature to WEDMed surface. Y. S. Tarang, S. C

.Ma, L. A. Chung [3] studied the optimal cutting process

parameters by applying Feed forward neural network within

the selected range of process parameters to study. A

simulated annealing algorithm is used to identify the optimal

cutting parameters.

They concluded that Neural Network can clearly

clarify complicated relationship between the cutting

parameters and cutting performance. Mohammad Jafar

Haddadet. Al. [4] has been carried out roundness and

material removal rate (MRR) study on the cylindrical wire

electrical discharge turning (CWEDT). The material chosen

in this case was AISI D3 tool steel due to its growing range

of applications in the field of manufacturing tools, dies and

molds as punch, tapping, reaming and so on in cylindrical

forms. This study was made only for the finishing stages and

has been carried out on the influence of four design factors:

power, voltage, and pulse off time and spindle rotational

speed, over the three previous mentioned response variables.

II. EXPERIMENTAL SET-UP

A number of experiments were conducted to study the

effects of various machining parameters on WEDM process.

These studies were undertaken to investigate the effects of

various machining parameters on Material removal rate and

Surface roughness. The selected workpiece material for the

research work is AISI D3 steel was selected due to its

emergent range of applications in the field of mould

industries.

The material MRR is expressed as the ratio of the

difference of weight of the workpiece before and after

machining to the machining time and density of the

material.

tD

WWMRR tatb

Where, Wtb weight before machining of w/p (gm),

Wta weight after machining of w/p (gm), D density of work-

piece material (gm/mm3) & t time consumed for machining

(min).

The Ra value, also known as center line average

(CLA) and arithmetic average (AA) is obtained by

averaging the height of the surface above and below the

centre line. The Ra will be measured using a surface

roughness tester from Mitutoyo, Model: SJ 201P.

In this investigation, experimental design was

established on the basis of 2k factorial, where k is the

number of variables, with central composite-second-order

rotatable design to improve the reliability of results and to

reduce the size of experimentation without loss of accuracy.

Thus, the minimum possible number of experiments (N) can

be determined from the following equations:

.......nnnN ac

k

cn 2

kna 2

In this case k = 4 and thus nc = 2k = 16 corner

points at ±1 level,na = 2 X k = 8 axial points at γ = ±2, and a

center point at zero level repeated 7 times (no). This

involves a total of 31 experimental observations. The Level

and factors are depicted in table 1.

+2 +1 0 -1 -2

Current (amp) 1 2 3 4 5

Pulse on time (μs) 6 8 10 12 14

Pulse off time (μs) 2 4 6 8 10

Parametric Optimization during WEDM Machining of AISI D3 using Response Surface Methodology

(IJSRD/Vol. 2/Issue 03/2014/461)


Wire Tension (N) 0.8 1.1 1.4 1.7 2.0

Table. 1: Process Parameters and their Levels for CCD.

III. RESPONSE SURFACE METHODOLOGY

RSM is a collection of mathematical and statistical

techniques that are useful for modeling and analysis of

problems in which the response of interest is influenced by

several variables and objective is to optimize this response

[5]. In order to study the effects of the EDM parameters on

the above mentioned machining criteria, second order

polynomial response surface mathematical models can be

developed. In the general case, the response surface is

described by an equation of the form:

k

i

k

i

r

ji

jiijiiiii xxxxY1 1

2

2

2

0

Where Y is the corresponding response, iX is the

input variables, 2

iX and ji XX are the squares and

interaction terms, respectively, of these input variables. The

unknown regression coefficients are iji ,0 , andii . Using

CCD various 31 number of experiments to be conducted as

shown in Table: 2.

Sr. No. MRR Ra

1. 4.8199 4.194

2. 6.2356 6.235

3. 4.2732 2.843

4. 4.5236 5.806

5. 6.2314 4.66

6. 5.2127 5.258

7. 6.6987 6.532

8. 4.7611 3.088

9. 2.9587 4.235

10. 3.8005 5.386

11. 6.2314 4.125

12. 4.8199 4.194

13. 4.8199 4.194

14. 2.0568 3.965

15. 5.0255 5.004

16. 4.8199 4.194

17. 4.8199 4.194

18. 4.9599 4.235

19. 3.1166 4.472

20. 8.2093 5.698

21. 4.8547 5.236

22. 6.0047 5.368

23. 8.0750 6.895

24. 5.2759 5.698

25. 4.8199 4.194

26. 2.3564 5.084

27. 4.8199 4.194

28. 4.7546 4.659

29. 5.6985 4.165

30. 6.5689 3.877

31. 4.2356 7.622

Table. 2: Observed Values for Performance Characteristics

Term Coef SE Coef T P

Constant -3.98950 5.78486 -0.690 0.500

Ip 4.41279 1.05122 4.198 0.001

Ton 0.95082 0.58968 1.612 0.126

Toff -1.18458 0.52561 -2.254 0.039

WT -2.07008 3.84899 -0.538 0.598

Ip*Ip 0.06434 0.08985 0.716 0.484

Ton*Ton 0.08666 0.02246 3.858 0.001

Toff*Toff -0.01650 0.02246 -0.734 0.473

WT*WT -0.85117 0.99835 -0.853 0.406

Ip*Ton -0.40368 0.06006 -6.721 0.000

Ip*Toff 0.13207 0.06006 2.199 0.043

Ip*WT -0.87444 0.40040 -2.184 0.044

Ton*Toff -0.12109 0.03003 -4.032 0.001

Ton*WT -0.23593 0.20020 -1.178 0.256

Toff*WT 1.55468 0.20020 7.766 0.000

R-Sq = 93.61% R-Sq(pred) = 63.17% R-Sq(adj) = 88.01%

Table. 3: Estimated Regression Coefficients for MRR

The regression equation for MRR is described

below.

WTTWTTTTWTI

TITIWTTT

IWTTTIMRR

offonoffonp

offponpoffon

poffonp

5546.12359.0121.08744.0

132.04036.08511.00165.00866.0

0643.007.21845.19508.04127.49895.3

222

2

The Coefficient of determination R2 as 93.61% for

MRR, which signifies that how much variation in the

response is explained by the model. The higher of R2,

indicates the better fitting of the model with the data.

Source D

f

Seq SS Adj SS Adj

MS

F P

Regressio

n

1

4

54.074

0

54.074

0

3.862

4

16.7

3

0.000

0 Linear 4 19.312

5

6.7740 1.693

5

7.34 0.001

Square 4 4.1189 4.1189 1.029

7

4.46 0.013

Interactio

n

6 30.642

6

30.642

6

5.107

1

22.1

2

0.000

Residual

Error

1

6

3.6938 3.6938 0.230

9

Lack of

Fit

1

0

3.6938 3.6938 0.369

4

Pure

Error

6 0.0000 0.0000 0.000

0

Total 3

0

57.767

7

Table. 4: Analysis of variance for MRR

It is important to check the adequacy of the fitted

model, because an incorrect or under-specified model can

lead to misleading conclusions. By checking the fit of the

model one can check whether the model is under specified.

The model adequacy checking includes the test for

significance of the regression model, model coefficients, and

lack of fit, which is carried out subsequently using ANOVA




on the curtailed model (Table-IV). The P value indicates the

significance of regression analysis.

The Coefficient of determination R2 as 95.62% for

SR, which signifies that how much variation in the response

is explained by the model.

Term Coef SE Coef T P

Constant 10.5172 3.65929 2.874 0.011

Ip 0.9589 0.66496 1.442 0.169

Ton -1.8288 0.37301 -4.903 0.000

Toff -1.1874 0.33248 -3.571 0.003

WT 4.9741 2.43473 2.043 0.058

Ip*Ip -0.1763 0.05684 -3.101 0.007

Ton*Ton 0.1178 0.01421 8.289 0.000

Toff*Toff 0.1054 0.01421 7.420 0.000

WT*WT 1.0610 0.63152 1.680 0.112

Ip*Ton 0.0447 0.03799 1.177 0.256

Ip*Toff 0.1913 0.03799 5.035 0.000

Ip*WT -0.8665 0.25328 -3.421 0.004

Ton*Toff 0.0087 0.01900 0.457 0.654

Ton*WT -0.2693 0.12664 -2.126 0.049

Toff*WT -0.5611 0.12664 -4.431 0.000

R-Sq = 95.62% R-Sq(pred) = 74.76% R-Sq(adj) = 91.78%

Table. 5: Estimated Regression Coefficients for SR

WTTWTTTTWTI

TITIWTTT

IWTTTISR

offonoffonp

offponpoffon

poffonp

5611.02693.0087.08665.0

1913.00447.0061.11054.01178.0

1763.09741.41874.18288.19589.05172.10

222

2

Source D

f

Seq SS Adj SS Adj

MS

F P

Regressi

on

1

4

32.255

7

32.255

7

2.3039

8

24.9

4

0.000

Linear 4 14.076

3

4.9148 1.2287

0

13.3

0

0.000

Square 4 12.378

1

12.378

1

3.0945

2

33.5

0

0.000

0 Interactio

n

6 5.8014 5.8014 0.9668

9

10.4

7

0.000

0 Residual

Error

1

6

1.4780 1.4780 0.0923

8

Lack of

Fit

1

0

1.4780 1.4780 0.1478

0

Pure

Error

6 0.0000 0.0000 0.0000

Total 3

0

33.733

7

Table. 6: Analysis of variance for SR (CCD)

The P value satisfies the regression model within

95% of significance level.

IV. RESULT AND DISCUSSIONS

12.5

0 10.0

4

8

1.0 7.5

12

2.54.0 5.0

5.5

MRR

Ton

Ip

Toff 6

WT 1.4

Hold Values

Surface Plot of MRR vs Ton, Ip

Fig. 1: Effect of Ip and Ton on MRR

From Figure 1 the MRR is found to have an

increasing trend with the increase of current and pulse on

time. MRR is increasing nonlinearly with the current. This is

obvious, as the Ip increases, the pulse energy increases, and

thus more heat is produced in the tool work piece interface

that leads to increase the melting and evaporation of the

electrode. One can interpret that Ip has a significant direct

impact on MRR.

From figure 2 it is clear that with increase in pulse

off time the MRR tends to increase for any value of Current.

That’s why it is important to check the combined effect of

Ton and Toff.

9

3 6

4

5

1.0

6

32.54.0

5.5

MRR

Toff

Ip

Ton 10

WT 1.4

Hold Values

Surface Plot of MRR vs Toff, Ip

Fig. 2: Effect of Ip and Toff on MRR

93

6

6

5.0

9

7.5 310.0

12.5

MRR

Toff

Ton

Ip 3

WT 1.4

Hold Values

Surface Plot of MRR vs Toff, Ton

Fig. 3: Effect of Ton and Toff on MRR

From Figure 3 the MRR is found to have an

increasing trend with the increase of pulse on and pulse off

time. This establishes the fact that MRR is also proportional

to the total machining time with rate of energy supplied. It is

observed that the MRR values are high when Ton is low with

higher Toff or Toff is low with higher Ton. From the analysis it

is said that the interaction of Ton and Toff is significant.

Although the influence of this two parameter is very less

when compared with the effect of Ip on MRR.

2.0

1.53

4

5

6

1.0 1.02.5

4.05.5

MRR

WT

Ip

Ton 10

Toff 6

Hold Values

Surface Plot of MRR vs WT, Ip

Fig. 4: Effect of Ip and WT on MRR

Fig. 4 shows the estimated response surface for

MRR in relation to the process parameters of Ip and WT

while Toff and Ton remain constant at their middle value. It

can be seen from the figure, the MRR tends to increase

significantly with the increase in Ip for any value of WT. At




low current and high WT the MRR value is high. In other

case for low WT and high Ip the value of MRR is high.

Means for Higher MRR the combination of Current and

Wire Tension is necessary.

V. EFFECT OF VARIOUS PROCESS PARAMETERS ON SR

12.5

10.0

4

6

1.0 7.5

8

2.54.0 5.0

5.5

SR

Ton

Ip

Toff 6

WT 1.4

Hold Values

Surface Plot of SR vs Ton, Ip

Fig. 5: Effect of Ip and Ton on MRR


Surface Roughness in relation to the process parameters of

Ip and Ton while Toff and V remain constant at their middle

value. It can be seen from the figure, the SR tends to

increase significantly with the increase in Ip for any value of

Ton. However, the SR tends to increase with increase in

Ton, especially at higher Ip. Hence, minimum SR is

obtained at low peak current and low pulse on time. This is

due to their dominant control over the input energy, i.e. with

the increase in Ip and Ton generates strong spark for longer

time, which create the higher temperature and crater, hence

rough surface in the workpiece and low Ip creates small

crater and therefore smooth surface.

9

2 6

4

6

1.0 32.54.0

5.5

SR

Toff

Ip

Ton 10

WT 1.4

Hold Values

Surface Plot of SR vs Toff, Ip

Fig. 6: Effect of Ip and Toff on SR



Ip and Toff while Ton and V remain constant at their middle

value. It can be seen from the figure, the SR tends to

increase significantly with the increase in Ip with certain

value, and the explanation is same, as stated earlier.

However, with the increase in Toff at low current, SR

decreases. It is because it takes time before next spark and

reduces the crater effect due to higher temperature.

2.0

1.53

4

5

1.0

6

1.02.5

4.05.5

SR

WT

Ip

Ton 10

Toff 6

Hold Values

Surface Plot of SR vs WT, Ip

Fig. 7: Effect of Ip and WT on SR



Ip and WT while Ton and Toff remain constant at their

middle value. It can be seen from the figure, the SR tends to

increase significantly with the increase in Ip for any value of

WT. At high current low high WT the SR value is high.

9

4 6

6

8

5.07.5 3

10.012.5

SR

Toff

Ton

Ip 3

WT 1.4

Hold Values

Surface Plot of SR vs Toff, Ton

Fig. 8: Effect of Ton and Toff on SR

Fig. 8 represents SR as a function of Ton and Toff,

whereas the Ip and WT remains constant at its middle level.

It is observed that the SR values are low when Ton and Toff

at its middle zone.

VI. CONCLUSION

The MRR is found to have an increasing trend with

the increase of current and pulse on time. MRR is

increasing nonlinearly with the current. This is

obvious, as the Ip increases, the pulse energy

increases.

The MRR is found to have an increasing trend with

the increase of pulse on and pulse off time. This

establishes the fact that MRR is also proportional

to the total machining time with rate of energy

supplied. It is observed that the MRR values are

high when Ton is low with higher Toff or Toff is low

with higher Ton.

The MRR tends to increase significantly with the

increase in Ip for any value of WT. At low current

and high WT the MRR value is high. In other case

for low WT and high Ip the value of MRR is high.

Means for Higher MRR the combination of Current

and Wire Tension is necessary.

The SR tends to increase significantly with the

increase in Ip for any value of Ton. However, the

SR tends to increase with increase in Ton,

especially at higher Ip. Hence, minimum SR is

obtained at low peak current and low pulse on time.

The SR tends to increase significantly with the

increase in Ip for any value of WT. At high current

low high WT the SR value is high.

REFERANCE

[1] K.H. Ho, S.T. Newman (2003) ‘State of the art in wire

electrical discharge machining (WEDM)’

International Journal of Machine Tools &

Manufacture 44 (2004) 1247–1259.

[2] R. E. Williams et. Al.(1991) ‘study of wire electrical

discharge machined surface characteristics’ Journal of




Materials Processing Technology, 28 ( 1991 ) 127-

138.

[3] Y. S. Tarang, S. C .Ma, L. A. Chung (1994)

‘Determination of optimal cutying parameters inWire

electrical discharge machining’

[4] Mohammad Jafar Haddad, AlirezaFadaeiTehrani

(2008)‘Investigation of cylindrical wire electrical

discharge turning(CWEDT) of AISI D3 tool steel

based on statistical analysis’ journal of materials

processing technology 1 9 8 (2008) 77–85

[5] C.H. Che Heron , B. Md. Deros , A. Ginting and M.

Fauziah “Investigation on the influence of machining

parameters when machining tool steel using EDM”

Journal of Materials Processing Technology Vol 116:

pp 84-87, 2001.

parametric optimization during wedm machining of aisi d3 ... · parametric optimization during wedm...

Documents