Experimental Investigation and optimization of EDM process parameters on Aluminum metal matrix composite

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ScienceDirect Materials Today: Proceedings 5 (2018) 24731–24740

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IConAMMA_2017

Experimental Investigation and optimization of EDM process parameters on Aluminum metal matrix composite Anil Kumar Bodukuria*, Chandramouli Sb, Eswaraiah Kc, Laxman Jd a,b,d

c

Scholar,Department of Mechanical Engineering, Kakatiya University, Warangal,Telangana-506009, India. Departmente of Mechanical Engineering, Kakatiya Institute of Technology & Science, Warangal, Telangana-506009, India.

Abstract The present work is to investigate the optimal EDM process parameters on Al-7075 based SiCp reinforced Metal Matrix Composite with copper metal as a tool electrode. The effect of various process parameters on machining performance was investigated. The input parameters considered are current, pulse on time, pulse off time and tool lift and their effect on Material Removal Rate, Tool Wear Rate and Surface Roughness had been analysed. The Taguchi method and traditional desirability function analysis was used to formulate the multi response experimental layout, Optimal machining conditions were identified for the given tool & work piece combination and also it was found that the results were in line with the literature data. © 2018 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of International Conference on Advances in Materials and Manufacturing Applications [IConAMMA 2017]. Keywords:MMC; EDM; Taghuchi; MRR; TWR; SR;

1. Introduction The new era of materials such as MMC’s are extensively attracted the aerospace, automobile and military applications as they have high strength to weight ratio, superior stiffness and resistance to high temperature. To serve such high strength MMC’s in machining EDM is playing an important role. As EDM is non conventional machining processes in which there is no physical contact between tool electrode and work piece. EDM is a high precision

* Corresponding author. Tel.: +919700381439; +918008840830. E-mail address:[email protected]. 2214-7853© 2018 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of International Conference on Advances in Materials and Manufacturing Applications [IConAMMA 2017].

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metal removal process which uses thermal energy by generating series of successive sparks to erode the work material. By erosive effect the material is removed from both tool and work piece. Dielectric fluid will ionize due to short discharges in a liquid dielectric gap between tool and work piece. Nomenclature MMC MRR TWR SR SiCp S/N EDM Current Pulse on time Pulse off time Tool Lift

Metal Matrix Composite Material removal rate Tool wear rate Surface roughness Silicon Carbide particulate Signal to Noise ratio Electric Discharge Machining I P On P Off T lift

2. Literature survey Hung et al. [1] has investigated on the Aluminium MMC reinforced with SiCp which is used to shield and protect the Aluminium matrix from being vaporized to reduce the Metal Removal Rate (MRR) on EDM process. Un-melted SiC particles will dropout due to surrounding molten Al while machining by EDM. Even though the removed material is flushed away by flushing of dielectric fluid, some droplets Al droplets are trapping the loosened SiC particles it re-solidifies on to the surface to form a Recast Layer. Below the Recast layer it is known to be softened Heat Affected Zone (HAZ) and no crack would found in the recast layer. The MRR and the recast layer depth are controlled by input power but the current alone dominates the surface finish of an Electric Discharge Machined surface. Hocheng et al. [2] has defined the correlation between major machining parameters, current and Pulse ontime and crater size produced by conducting experiments on Al-MMC reinforced SiC. Their results shows the predicted proportionality based on heat conduction model with comparison of common steels in material removal rate. Chicosz and Karokzak [3] conducted feasibility study of EDM machinability of Al/Si/Mg-Al2O3 metal matrix composites. They studied the effect of current on the recast layer formation of machined surfaces. They found that the optimized pattern of current density and frequency of sparks will improve the machining performance. Hwa et al. [4] has investigated the Rotary EDM with ball burnishing for Al2O3/Al6061 composite to find out machinability of composite using Taguchi method. MRR, surface roughness and improvement of SR are considered for optimizing machining technique. The analysis reveals the supports in practical technique for applying Rotary EDM with ball burnishing in machining the composite. Narendara singh et al [5] has studied the electrical discharge machining of Al-10%SiC metal matrix composite. They selected current, pulse on time, flushing pressure as machining parameters. The response studied for this study was metal removal rate, tool wear rate, radial over cut and surface roughness. The study reveals the effect of each machining parameter on the responses. Chandramouli S, Shrinivas Balraj U and Eswaraiah K [6] investigated about the process parameters on EDM of RENE80 nickel super alloy material with aluminum as a tool electrode. The Taguchi method is used to formulate the experimental layout, ANOVA method is used to analysis the effect of input process parameters. The results reveals the importance proper selection of process parameters in Electric Discharge Machining. Wang and Yan [7] experimented on Al6061/Al2O3 composite using rotary EDM by using Taguchi methodology to optimize the blind hole drilling of composite. The experimental result reveals that the revised copper electrode with an eccentric hole has the optimum performance for machining in various aspects. MRR, EWR and SR verify this optimization of the machining technique. M.M. Rahman et al. [8] investigated the effect of the peak current and pulse duration on the performance characteristics of the EDM. The results reveal that current and pulse on time significantly affected the MRR, TWR and SR, the MRR increases almost proportionally with the increasing current, the SR increases as increase in current

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for different pulse on time, TWR increased with increasing peak current and decreased while increase in pulse on time. Nanimina et al [9] investigated the effects of EDM on Al6061-30%Al2O3 metal matrix composites. They selected peak current, pulse on time and pulse off time as machining parameter and material removal rate and tool wear rate as responses. They found that MRR increases with the High current and pulse on time. High TWR is observed at low peak current and pulse on time. Manna and Bhattacharya [10] investigated the parameter setting during the machining of Aluminium reinforced silicon carbide AMMC and the Taguchi method was used to optimize the CNC wire cut EDM parameters and L18 orthogonal array was used. From experimental results and through ANOVA and F-test values, the significant factors are determined for each machining performance criteria, such as the MRR, SR, gap current and spark gap (gap width). Mathematical models relating to the machining performance are established using the Gauss elimination method for the effective machining of Al/SiC MMC 3. Experimental Procedure 3.1. Fabrication of metal matrix composite Aluminium 7075 alloy is chosen as testing material due to its greater strength and excellent corrosion resistance among all available aluminium alloys. Al 7075 is widely used for construction of aircraft structures, such as wings and fuselages. Its strength and light weight are also desirable in other fields. Rock climbing equipment, bicycle components, and hang gliders are commonly made from 7075 aluminium alloys Al 7075 is widely used for construction of aircraft structures, such as wings and fuselages. Its strength and light weight are also desirable in other fields. Chemical compositions of 7075 are tabulated in table 1. The liquid metallurgy technique i.e., stir casting technique is employed for preparing the composite specimens. This is most cost-effective to fabricate composites with discontinuous fibers or particulates. Matrix alloy (Al-7075) was firstly superheated over its melting temperature. Then temperature was lowered gradually below the liquidus temperature to keep the matrix alloy in the semi-solid state. At this temperature, the preheated (800°C) SiC particles of about 10 % of weight of Al-7075 were introduced into the slurry and mixed with stainless steel stirrer. The composite slurry temperature was increased to obtain fully liquid state. Stirring was continued about 30 minutes. The melt was then superheated above liquidus temperature (760°C) and the slag which is formed on the slurry is removed. The pouring temperature was maintained at 700°C. The melt was then allowed to solidify. The time taken for solidification is about 2 hrs. Properties of Al 7075 & SiC are tabulated in table 2. The EDM shown in figure 1 is used for machining of prepared MMC. Table 1 Chemical composition of 7075 Composition

Aluminium

Silicon

Ferrous

Titanium

Manganese

Magnesium

Zinc

Chromium

Zirconium

Percentage

87.2-91.4%

0-0.4%

0-0.5%

0-0.2%

0-0.3%

2.1-2.9%

5.1-6.1%

0.18-0.28%

0-0.25%

Table 2 Properties of Al 7075 & SiC Property Elastic Modulus (GPa)

Density (g/cc)

Poison’s Ratio

Hardness (HB500)

Al 7075

70-80

2.81

0.33

60

220(T)

SiC

410

3.1

0.14

2800

3900(C)

Tensile/Compressive

3.2. Machining of MMC by EDM Prepared MMC is used as work piece for machining by EDM. The specifications of EDM are tabulated in table 3 and the input parameters that are considered for study are current, pulse on time and pulse off time. These are varied to study MRR, TWR and Surface Roughness. Polarity of the experimental set-up is kept constant throughout the experimentation. Polarity has been fixed as straight polarity (Tool electrode negative) for all the experiments because it is desirable setting for material transfer to occur. With straight polarity, the energy available per discharge

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at work surface is higher as compared to the tool electrode and consequently material removal rate is also higher. In this work the effects of four process parameters such as I, T On , T Off and Tlift have been considered for investigation of three responses i.e. MRR, TWR and SR. The experimental design with Taguchi’s method, L18 orthogonal array has been employed. The machined MMC specimen is shown in figure 2. MINITAB 16 Software is used for determining ANOVA & S/N ratio plots of MRR, TWR and SR are tabulated in table 4. The S/N ratio plots of MRR & TWR are shown in figure 3 and S/N ratio plots of SR is shown in figure 4. Table 3 Specifications of EDM Specifications of EDM Spark gap

0.025mm

Table Dimension

350 x 250 mm

Maximum Table Loading

200 Kg

Normal Current

25 Amp

Work Tank Dimension

600 x 390 x 275 mm

Dielectric Fluid

EDM 30 grade oil

Tool Material

Copper

Materials that can be machined

All conducting metals and alloys

Limitations

High specific energy consumption non conducting materials can’t be machined

Figure 1 V3525 -EDM

Figure 2 MMC Specimens

Figure 3 S/N ratios of MRR and TWR.

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Table 4 Design layout & Experimental results and S/N Ratios of responses Factor A

Factor B

Factor C

Factor D

Response 1

Response 2

Response 3

I

T On

T Off

T Lift

Average SR

Average TWR

Average MRR

S/N MRR

S/N TWR

S/N SR

6

20

10

5

5.05

51.2

73

37.2665

-34.1854

-14.0658

6

50

20

10

5.9

41

65.8

36.3645

-32.2557

-15.4170

6

100

50

20

5.78

20.1

33

30.3703

-26.0639

-15.2386

9

20

10

10

6.8

60.2

103.4

40.2904

-35.5919

-16.6502

9

50

20

20

7.04

41.4

80.2

38.0835

-32.3400

-16.9581

9

100

50

5

6.7

48.3

102.4

40.2060

-33.6789

-16.5215

12

20

20

5

7.32

64.2

140

42.9226

-36.1507

-17.2902

12

50

50

10

7.23

42.5

94

39.4626

-32.5678

-17.1828

12

100

10

20

8.2

49.5

106

40.5061

-33.8921

-18.2763

6

20

50

20

5.65

24.7

38

31.5957

-27.8539

-15.0410

6

50

10

5

5.2

50.05

76

37.6163

-33.9881

-14.3201

6

100

20

10

5.75

33.4

64.8

36.2315

-30.4749

-15.1934

9

20

20

20

6.3

39.3

82.6

38.3396

-31.8879

-15.9868

9

50

50

5

6.4

48.7

93.6

39.4255

-33.7506

-16.1236

9

100

10

10

6.9

49.37

104.8

40.4072

-33.8693

-16.7770

12

20

50

10

6.8

41.04

95

39.5545

-32.2641

-16.6502

12

50

10

20

8.5

51.25

101

40.0864

-34.1939

-18.5884

12

100

20

5

7.35

55.46

139.8

42.9101

-34.8796

-17.3257

Main Effects Plot for Means Composie Desirability Data Means

I

0.50

TON

T OFF

TOOLIFT

Meanof Means

0.45

0.40

0.35

0.30

6

Figure 4 S/N ratios of SR 4. Results and Discussions

9

12

20

50

100

10

20

50

5

10

20

Figure 5 Main effects for Composite desirability

The input values are studied through experimentation and desirability function analysis for influence of machining parameters on response characteristics. The following sections explain about various parameters and their effect on responses.

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4.1. Effect of process parameters on MRR, TWR AND SR Single objective response optimization was carried out to investigate the effects of machining parameters on MRR, TWR & SR. According to the Taguchi method, S/N ratios were calculated for each experiment and S/N ratio plots for MRR, TWR and SR are drawn. The objective of experimentation is to maximize the MRR, Minimize the TWR and SR. The response table for S/N ratios of MRR, TWR and SR were calculated considering the factor that MRR is larger-the-better performance characteristic and the following equation 1 is generated. The response table for S/N ratios for TWR and SR were calculated assuming lesser-the-better performance characteristic and equation 2 is generated. The response table for MRR, TWR and SR was presented in table 5 and table 7. . The ANOVA for MRR, TWR and SR was performed with help of Minitab software. Table 6 summarizes the effect fitted models of individual process parameters on MRR, TWR and SR through ANOVA. The equations expressed as:

----------------------(1) ----------------------(2) Where yij = observed response value, i=1, 2... ....n & j = 1, 2...k and n = number of replications Table 5 Response table for MRR RESPONSE TABLE FOR MRR (Means) Level

I

T ON

T OFF

TOOL LIFT

1

58.43

88.67

94.03

104.13

2

94.50

85.10

95.53

87.97

3

112.63

91.80

76.00

73.47

Delta

54.20

6.70

19.53

30.67

Rank

1

4

3

2

Current: An ANOVA and S/N ratio graph reveals that the MRR increases with increase in Current. Because the increase in current leads to increase of spark energy which causes melting and vaporization of work piece. As MRR increases the SR also increases due to increase in current rate. Pulse on time: MRR increases first with increase in pulse on time and then it decreases, this is due to the amount of energy generated at high pulse on time will first remove the material but due to improper flushing, the removed material will form debris and it is welded on the work surface. In case of TWR first decreases and then it increases. This is due to the high amount of energy will cause the TWR. SR of work piece is decreased due to the high pulse on energy is used for TWR instead of MRR. Pulse off time: MRR decreases with increase in pulse off time because the time taken for flushing increases. TWR and SR increases with increase in pulse off time the eroded particles from gap between tool and work piece will stick on work piece, which results in increase of SR. Lift time: MRR decreases with increase in lift time, due to decrease in actual time of machining the work piece. The surface roughness decreases with increase of tool lift which permits high flushing time.

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Table 6 ANOVA TABLE FOR FITTED MODELS RESPOSE

Source

Sum Squares

MRR

I

9134.5

TWR

SR

Of

DOF

Mean Square

F Value

Model summary

2

4567.26

185.13

S

4.96693 98.38%

T ON

134.9

2

67.43

2.73

R-sq

T OFF

1418.0

2

709.00

28.74

R-sq(adj)

96.95%

57.24

R-sq(pred)

93.53%

31.23

S

3.22793 95.59%

TOOL LIFT

2824.1

2

1412.06

Error

222.0

9

24.67

Total

222.0

17

I

650.85

2

T ON

54.78

2

27.39

2.63

R-sq

325.43

T OFF

624.05

2

312.03

29.95

R-sq(adj)

91.67%

TOOL LIFT

702.45

2

351.23

33.71

R-sq(pred)

82.36%

Error

93.78

9

10.42

Total

2125.91

17

I

12.2081

2

S

0.330448

6.1040

55.90

R-sq

T ON

0.7405

2

0.3703

3.39

93.58%

T OFF

0.3644

2

0.1822

1.67

R-sq(adj)

87.87%

TOOL LIFT

1.0100

2

0.5050

4.62

R-sq(pred)

74.32%

Error

0.9828

9

0.1092

Total

15.3057

17

Table 7 RESPONSE TABLE FOR TWR & SR Level

I

T ON

T OFF

TOOL LIFT

1

36.74

46.77

51.93

52.98

2

47.88

45.82

45.79

44.59

3

50.66

42.69

37.56

37.71

Delta

13.92

4.09

14.37

15.28

Rank

3

4

2

1

RESPONSE TABLE FOR SR Level

I

T ON

T OFF

TOOL LIFT

1

5.55

6.320

6.775

6.337

2

6.691

6.713

6.611

6.563

3

7.567

6.780

6.427

6.913

Delta

2.012

0.460

0.348

0.576

Rank

1

3

4

2

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The experimental results were used to obtain the mathematical relationship between process parameters and machining outputs. The coefficients of mathematical models were computed using method of multiple regression analysis by MINITAB 16 (Software Package for Statistical Solutions) was used for the regression analysis. Mathematical models were developed at 95 % confidence level by using regression analysis to determine the responses. Regression equations MRR TWR SR

= 40.57 + 9.033 I + 0.0488 T On - 0.497 T Off - 1.960 T lift = 47.57 + 2.319 I - 0.0522 T On - 0.3397 T Off - 0.971 T lift = 3.061 + 0.3353 I + 0.00530 T On - 0.00812 T Off + 0.0379 T lift

------------(3) ------------(4) -------------(5)

4.2. Multi-objective optimization of response parameters by desirability approach The main aim of the present study is to find the optimal machining conditions of EDM process. The Taguchi optimization based on desirability analysis is an ideal technique for finding the optimal machining condition of WEDM process. Here the goal was to maximize the material removal rate and minimize the Tool Wear rate and surface roughness. Desirability approach helps us to map between the predicted response ‘y’ and desirability function‘d’. The desirability value varies from 0 to 1. If the desirability value is zero, it indicates that predicted value was completely undesirable and the desirability value of one was idle. The desirability of corresponding response increases as the value of d increases. The Two-sided transformation desirability function of maximization for MRR as shown in Eq. (6), minimization of tool wear and surface roughness as shown in Eq. (7).

------------------------(6)

-------------------------(7) Where, d was a desirability function of y, ymin and ymax are lower and upper limits of response value of ‘y’, respectively, wt was weight, which can be varied from 0.1 to 10 to adjust the shape of desirability function. An overall desirability function D (0 _ D _ 1) was defined as the geometric mean of individual desirability functions. The multi objective function was a geometric mean of all transformed responses of single objective problem shown in Eq. (8). The higher the D value, better was the desirability of the combined response levels.

------------------(8) Multi-response optimization was carried out using desirability function in conjunction with Taguchi method. The ranges of input parameters viz pulse on time, pulse off time, current and Tool lift. The weight values are assigned for MRR, TWR and SR as one and equal importance given to each response. A set of 18 optimal solutions were derived for the specified design space constraints (Table 8) for material removal rate, Tool wear rate and surface roughness using Minitab statistical software. The set of conditions possessing highest desirability value was selected as optimum condition for the desired responses. Table 8 shows the optimal set of condition with higher desirability function required for obtaining desired response characteristics under specified constraints. Figure 5 shows the main effects plotted for the composite desirability at different levels of the processing parameters. Basically, the larger the composite desirability, the better is the multiple performance characteristics. However, the relative importance among the parameters for the multiple performance characteristics will still need to be known so that the optimal combinations of the process parameter levels can be determined more accurately. From the table 9, it is clear that the optimal set of process parameters from desirability analysis was I-3, TON 3, TOFF 2, Tool Lift 1.

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Table 8 Set of Optimal Solutions for EDM process. RUN 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18.

SR

DESIR SR

MRR

5.05 5.9 5.78 6.8 7.04535 6.7 7.32 7.23 8.2 5.65 5.2 5.75 6.3 6.4 6.9 6.8 8.5 7.35

0.964027 0.832109 0.764734 0.569082 0.40164 0.602415 0.428502 0.302415 0.080676 0.898068 0.850242 0.81256 0.515425 0.621964 0.435749 0.4162 0.100225 0.295169

73 65.8 33 103.4 80.2 102.4 140 94 106 38 76 64.8 82.6 93.6 104.8 95 101 139.8

DESIR MRR 0.436449 0.266044 0.010592 0.62243 0.467601 0.634268 0.957009 0.590031 0.68567 0 0.403115 0.32866 0.500935 0.571651 0.651713 0.623364 0.623053 0.986293

TWR

DESIR TWR

51.2 41 20.1 60.2 41.4 48.3 64.2 42.5 49.5 24.7 50.05 33.4 39.3 48.7 49.37 41.04 51.25 55.46

0.25056689 0.60185185 1 0.18851096 0.50525321 0.41655329 0.07411187 0.47305367 0.37403628 0.9228647 0.27226002 0.67278912 0.48356009 0.34561602 0.28114135 0.45136054 0.30309902 0.16674225

Composite desirability 0.472406 0.510749 0.200830 0.405696 0.456115 0.541934 0.312070 0.438662 0.274531 0.000000 0.453578 0.564279 0.499803 0.497161 0.430599 0.489241 0.266498 0.364788

Rank 7 3 17 12 8 2 14 10 15 18 9 1 4 5 11 6 16 13

Table 9 Response Table of Composite Desirability Response Table of Composite Desirability Process parameters

LEVEL 1

LEVEL 2

LEVEL 3

DELTA

Rank

I

0.3670

0.4719

0.3576

0.1143

2

T ON

0.3632

0.4371

0.3962

0.0739

4

T OFF

0.3839

0.4513

0.3613

0.0900

3

TOOL LIFT

0.4403

0.4732

0.2830

0.1902

1

4.3. Analysis of Variance for Composite Desirability: The results obtained from the experiments were analyzed using Analysis of Variance to find the significance of each input factor on the measures of process performances, Material Removal Rate, Tool Wear rate and surface roughness. Using the composite desirability value, ANOVA was formulated for identifying the significant factors. The results of ANOVA are presented in the Table 10. Table 80 Analysis Of Variance For Composite Desirability Analysis Of Variance For Composite Desirability Process parameters

Source

DOF

Sum Of Squares

Mean Square

F Value

contribution

I

I

2

0.04829

0.024147

1.70

23.6 %

T ON

T ON

2

0.01646

0.008229

0.58

57.9 %

T OFF

T OFF

2

0.02631

0.013154

0.93

43.0 %

TOOL LIFT

TOOL LIFT

2

0.12407

0.062035

4.38

4.7 %

Error

9

0.12754

0.014171

Total

17

0.34267

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Table 11 Comparison of Initial Settings with Optimized Experimental Results Responses

Initial machining parameters

Optimal machining parameters

% change in responses

Setting level

A1B1C1D1

A3B3C2D1

Surface Roughness (SR)

5.05

5.47

8.31% increase

Metal Removal rate (MRR)

73

84.3333

15.5 5% increase

Tool wear rate (TWR)

51.2

42.93

16.15 % decrease

Composite Desirability

0.472406

0.587926(Predicted)

5. Conclusion The ANOVA and F-Ratio revealed two effective parameters in view of tool and work piece. With respect to TWR, both ANOVA and F-Ratio shown that tool lift is the dominant parameter. But in case of SR and MRR of work piece the current is dominant parameter followed by other three parameters. The results reveals clearly that Pulse ON time was the major influencing factor contributing 57.9 % to performance measures, followed by Pulse Off Time contributing 43.0 %, peak current contributing 23.6%, and Tool Lift contributing 4.7%. From desirability analysis table, I-3 T On 3- T Off 2 TLift-1 was found as the set of optimal process parameters. These settings were used for confirmation experiments. Comparisons of Initial Settings with Optimized Experimental Results are tabulated in table 11. There was an increment of 8.311 % in surface roughness of the job when compared to the base experiment. There was a decrement of 16.15 % in Tool Wear Rate was been observed. Also, an increase of 15.55 % was also obtained in MRR after the confirmation experiment. There is also an increase in the composite desirability of the setting. The results are definitely satisfactory and show an improved response value References [1] Hung, N.P.; Yang L.J.; Leong K.W, “Electrical discharge machining of cast metal matrix composites”, Journal of Materials Processing Technology. 44 (1994) 229-236. [2] Hocheng, H.; Lei, W.T.; Hsu H.S., “Preliminary study of material removal in electrical-discharge machining of SiC/Al.”, Journal of Materials Processing Technology. 63: (1997). 813-818. [3] P.Chicosz and P.Karokzak., “Sinking EDM of aluminum matrix composites”, Material science-poland,vol 26, (2008)No.3, 547-554. [4] Biing Hwa Yan, Che Chung Wang, Han Ming Chow and Yan Cherng Lin. “Feasibility study of rotary electrical discharge machining with ball burnishing for Al2O3/6061Al composite”, International Journal of Machine Tools and Manufacture,Vol 40, (August 2000) Pages 1403–1421. [5] P.Narender Singh, K. Raghukandan and B.C. Pai,“Optimization by Grey relational analysis of EDM parameters on machining Al 10%SiCP composites”, Journal of Materials ProcessingTechnology,Vol 155–156, (2004) p.1658–1661. [6] Chandramouli S, Shrinivas Balraj U and Eswaraiah K, “Optimization of Electrical Discharge Machining Process Parameters Using Taguchi Method”, International Journal of Advanced Mechanical Engineering. ISSN 2250-3234 Volume 4, Number 4 (2014), pp. 425-434. [7] Wang, C.C.; Yan, B.H., “Blind-hole drilling of Al2O3/6061Al composite using rotary electro-discharge machining”, Journal of Materials Processing Technology. (2000),102: 90-102. [8] .A.Mouangue nanimina, A.M.Abdul Ran, F.Ahamd, A.Zainuddin and S.H. Jason Lo, “Effect of EDM on aluminum metal matrix composites”, Journal of applied science, DOI 10-3923/Jan . (2010). 2011, Pg.1-5. [9] Ajeet Bergaley, “Optimization of Electrical and Non Electrical Factors in EDM for Machining Die Steel Using Copper Electrode by Adopting Taguchi Technique”, International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-3, Issue-3, August 2013. [10] Manna, A.; Bhattacharyya, B, “Taguchi and Gauss elimination method: A dual response approach for parametric optimization of CNC wire cut EDM of PRAlSiC-MMC”, Int J Adv Manuf Technol (2006),. 28: 67–75.