Multi-objective optimization of machining characteristics during wire

Journal of Engg. Research

Vol. 1 - (3) December 2013 pp. 145-160, 2013

Multi-objective optimization of machining characteristics during wire electrical discharge machining of Al/ZrO2particulate reinforced metal matrix composite SANJEEV KR. GARG*, ALAKESH MANNA**, AJAI JAIN* *

National Institute of Technology, Kurukshetra, Haryana, India. PEC University of Technology, Chandigarh, India. * Corresponding author E-mail: [email protected] **

ABSTRACT This paper presents an experimental investigation of the machining characteristics and the e€ect of wire EDM process parameters during machining of newly developed Al/ ZrO2 particulate reinforced metal matrix composite (PRMMC) material. Central composite design (CCD) of response surface methodology (RSM) considering full factorial approach has been used to design the experiment. Experiments have been carried out in order to optimise the input parameters such as pulse width, time between pulses, servo control mean reference voltage, short pulse time, wire feed rate and wire mechanical tension for performance measures such as cutting velocity and surface roughness. The multi-optimization results obtained by initial parameters setting, response surface methodology and grey relational techniques have been compared and validated by con®rmation experiments. The comparison of performance characteristics using di€erent wire electrodes has also been presented in this paper. The experimental results of performance characteristics have proved that newly developed MMC can be machined e€ectively by wire EDM.

Keywords: Cutting velocity; particulate reinforced metal matrix composite; surface roughness; wire electrical discharge machining.

Abbreviations: Symbol

PW SCMRV WFR SG MRR Wire EDM PRMMC

Abbreviation

Symbol

Pulse Width TBP Servo control mean reference voltage SPT Wire feed rate WMT Spark gap SR Material removal rate CV Wire electrical discharge machine RSM Particulate reinforced metal matrix t composite Gray relational grade b

Abbreviation

Time between pulses Short pulse time Wire mechanical tension Surface roughness Cutting velocity Response surface methodology Thickness of work piece Width of cut

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Sanjeev Kr.Garg, Alakesh Manna, Ajai Jain

INTRODUCTION A composite can simply be de®ned as a combination of two or more dissimilar materials having a distinct interface between them such that the properties of the resulting material are superior to the individual constituting components (Bhargava, 2009). Metal Matrix Composites (MMC) have been so intensely researched over the past years that many new high strength to weight ratio materials have been prepared (Smith, 2004). Most of these materials have been developed for the aerospace industries, storage battery plates, satellite structures, antenna structure and high temperature structures (Kalpakjian & Stevan, 2000). In particulate reinforced metal matrix composites (PRMMC), discrete, uniformly dispersed particles of a hard brittle material are surrounded by a softer and more ductile matrix. Small particles of uniform size with proper orientation exhibit more strengthening e€ects. MMC can be manufactured by powder metallurgy route, di€usion bonding, co-continuous deformation, stir casting, deposition techniques, in-situ processing methods (Murthy, et al., 2005). Many researchers have tried modern machining methods to machine the MMC out of which wire electrical discharge machining (WEDM) emerged as an e€ective machining method. In the present work, parametric optimization of WEDM of Al/ZrO2-PRMMC is done using response surface methodology. WEDM is a complex machining process controlled by a large number of process parameters (Benedict, 1987; Boothroyd & Winston, 1989). The setting of various process parameters required in the WEDM process, plays a crucial role in producing an optimal machining performance. The parameter settings given by the manufacturers are only valid for the common steel grades so for advanced materials, parameters settings have to be optimized experimentally. Rozenek, et al. (2001) experimentally investigated the e€ect of machining parameters on the machining feed rate and surface roughness during WEDM of metal matrix composites such as AlSi7Mg/SiC and AlSi7Mg/Al2O3 using brass wire. Yan, et al. (2005) examined the WEDM of Al2O3p/6061Al composite. The authors found that material removal rate, surface roughness and width of slit of cutting test material signi®cantly depend on volume fraction of reinforcement (Al2O3 particles). Hewidy, et al. (2005) modeled the WEDM parameters for Inconel 601 using response surface methodology for the performance characteristics such as Metal Removal rate, Wear Ratio, Surface Roughness using brass wire as wire electrode. Ramakrishnan & Karunamoorthy (2006) used zinc coated brass wire for WEDM of heat treated tool steel workpiece and obtained multi response optimization using Taguchis robust design approach. Mahapatra & Patnaik (2006) used a 0.25 mm diameter zinc coated copper wire for a block of D2 tool steel workpiece in their experimental work and concluded that minimum wire tension gives maximum MRR but maximum wire tension gives maximum surface ®nish and minimum kerf. Chiang & Chang (2006) presented an e€ective approach for the optimization of the WEDM

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147

process of Al2O3 particle reinforced in Al 6061 alloy with multiple performance characteristics using pure copper as wire electrode. Authors used the Grey relational analysis to investigate the surface removal rate and surface roughness. Manna & Bhattacharyya (2006) used Taguchi and Gauss elimination methods for the parametric optimization of aluminium reinforced silicon carbide metal matrix composite and used brass wire in WEDM. The authors also studied the e€ect of machining parameters on machining performance characteristics such as metal removal rate, surface roughness, gap current and spark gap. Saha, et al. (2008) analyzed the wire electrical discharge machining of tungsten carbide cobalt composite using uncoated brass wire. Patil & Brahmankar (2010) experimentally investigated the e€ect of electrical as well as non-electrical machining parameters on performance in WEDM of metal matrix composite (Al/ Al2O3p). The authors revealed that process parameters such as reinforcement percentage, current and pulse on-time were found to have signi®cant e€ect on cutting rate, surface ®nish and kerf width separately. Garg, et al. (2010) conducted a review of research work in sinking EDM and WEDM on metal matrix composite materials. The authors concluded that most of the published work belongs to SiC reinforced metal matrix composites and very less work was reported on Al2O3 reinforced and other types of metal matrix composites. Patil & Brahmankar (2010) determined the material removal rate in wire electro-discharge machining of silicon carbide particulate reinforced aluminium matrix composites using dimensional analysis. The experimental results found by the authors showed thatLiterature survey on the WEDM of metal matrix composites reveals that no work has been reported on Al/ZrO2-PRMMC so far. In the present work Al/ZrO2-PRMMC has been prepared using Stir Casting technique because of simplicity and good results and 5% of ZrO2 particulates by weight has been added to prepare Al/ZrO2-PRMMC. Literature survey also reveals that di€used wire is not used for the WEDM of composites. So in present work, the di€used wire has been used as wire electrode in experimentation. Parametric optimization of WEDM of Al/ ZrO2-PRMMC has been done using response surface methodology.

EXPERIMENTAL SET-UP Robo®l-290 CNC wire EDM and di€used wire electrode having 250?m diameter has been used for experimentation. The di€used wire manufactured by Nikunj Group, India (NiCutTM-COMBII) has been used which has high conductive brass core and di€used zinc. Performance of WEDM has been evaluated on the basis of cutting velocity (CV) and surface roughness (SR). The mean cutting velocity is calculated using equation 1. Mean cutting velocity= length of travel/ machining time. ------(1) Machining time is obtained from the computer attached to the machine tool. Figure 1 shows the photograph of the Wire EDM of the workpiece. A Surfcom (Make: ZEISS; Model:130A) surface measuring instrument is used to measure

148

Sanjeev Kr.Garg, Alakesh Manna, Ajai Jain

the surface roughness (Ra) and is shown in ®gure 2. Table 1 shows the parameters, their range, actual and coded values for the experiment. Central composite design (CCD) of response surface methodology (RSM) has been used to design the experiment with the help of design of experiment (8.0 version) software. The coded values have been generated by CCD of RSM design considering six factors as input parameters.

Table 1. Parameters, their range, coded values and real values for the experiment Levels of Process Parameters S.No Parameters Units -1.57 -1 0 +1 +1.57 1 2 3 4 5 6

Pulse width (PW) s Time between pulses (TBP) s Short pulse time (SPT) s Servo control reference volts mean voltage (SCMRV) Wire feed rate (WFR) m/min Wire mechanical tension daN (WMT)

Constant parameters 7 8 9 10

Work-Piece height Machining voltage Ignition pulse current Maximum feed rate

0.36 4.61 0.12 11.52

0.5 0.8 0.2 20

0.75 14 0.35 35

1 20 0.5 50

1.14 23.39 0.6 58.48

4.87 0.43

6 0.6

8 0.9

10 1.2

11.13 1.37

10.1 mm 80 volts 8 units (1/2 A) 10 units (73.2 micron/min.)

Fig. 1. WEDM of workpiece

Multi-objective optimization of machining characteristics during wire electrical discharge machining...

149

Fig. 2. Measurement of surface roughness

RESULTS AND DISCUSSIONS Process performance characteristics such as cutting velocity and surface roughness have been used to analyse the e€ect of process parameters. Table 2 shows the parameter setting of total 86 runs as per central composite design of response surface methodology.

E€ect of process parameters on performance characteristics: Figure 3 presents the combined e€ect of pulse width and time between pulses on cutting velocity (mm/min.). It revealed that the cutting velocity increases with the increase in pulse width and it also increases with the increase in time between pulses upto some extent and then decreases with the increase in time between pulses. Figure 4 presents the combined e€ect of servo control mean referance voltage and short pulse time on cutting velocity. It delienated that the cutting velocity increases with the increase of short pulse time and it also increases with the increase in servo control mean referance voltage upto some extent and then decreases further with the increase of servo control mean referance voltage. This is due to the fact that spark energy increases with the increase in pulse width and short pulse time whereas it decreases with the increase in time between pulses and servo control mean referance voltage. Figure 5 presents the combined e€ect of servo control mean referance voltage and wire feed rate on cutting velocity from which it is clear that wire feed rate has no signi®cant e€ect on cutting

Sanjeev Kr.Garg, Alakesh Manna, Ajai Jain

150

PBT

WP

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 5. 1 0 0 0 0 0 75.1- 0 0 0 0 0 0 7 5. 1 0 0 0 0 0 75.1- 0 0 0 0 0 0 7 5. 1 0 0 0 0 0 75.1- 0 0 0 0 0 0 75.1 0 0 0 0 0 75.1- 0 0 0 0 0 0 75.1 0 0 0 0 0 75.1- 0 0 0 0 0 0 75.1 0 0 0 0 0 75.11 1 1 1 1 1 1 1 1 1 1 11 1 1 1 11 1 1 1 1 111 1 1 11 1 1 1 1 11 1-

TMW RFW VRMCS TPS

68 58 48 38 28 18 08 97 87 77 67 57 47 37 27 17 07 96 86 76 66 56 46 36 26 16 06 95

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 111-

1 1 1 1 1 1 1 1 1 1 11111111111111111 1 1

1 1 111111111 1 1 1 1 1 1 1 111111111 1 1

111 1 1 1 11111 1 1 1 11111 1 1 1 11111 1 1

NUR TMW RFW VRMCS TPS

111 1 111 1 111 1 111 1 111 1 111 1 111 1 1-

PBT

1 11 11 11 11 11 11 11 11 11 11 11 11 11 11

WP tnemirepxe eht rof gnittes sretemarap tupnI

85 75 65 55 45 35 25 15 05 94 84 74 64 54 44 34 24 14 04 93 83 73 63 53 43 33 23 13 03

11111111111111111111111111111-

1 1 1 1 1 1 1 1 1 1 1 1 1 1111111111111111-

1 1 1 1 1 111111111 1 1 1 1 1 1 1 11111111-

1 11111 1 1 1 11111 1 1 1 11111 1 1 1 1111-

NUR TMW RFW VRMCS TPS

.2 elbaT

11 1 111 1 111 1 111 1 111 1 111 1 111 1 11-

PBT

11 11 11 11 11 11 11 11 11 11 11 11 11 11 1-

WP

92 82 72 62 52 42 32 22 12 02 91 81 71 61 51 41 31 21 11 01 9 8 7 6 5 4 3 2 1

NUR

Multi-objective optimization of machining characteristics during wire electrical discharge machining...

151

velocity. Figure 6 presents the combined e€ect of pulse width and time between pulses on surface roughness. It revealed that surface roughness increases with the increase of pulse width. Surface roughness also increases with the increase in time between pulses upto some extent but then it decreases with the further increase in time between pulses.The above said e€ects on surface roughness are due to the fact that spark energy produced during spark between wire electrode and workpiece increases with the increase in pulse width whereas it decreases with the increase in time between pulses. Surface roughness of machined surfaces increases with the increase in spark energy produced during machining and it decreases with the decrease in spark energy. Figure 7 presents the combined e€ect of time between pulses and servo control mean referance voltage on surface roughness from which it is clear that surface roughness increases with the increase in servo control mean referance voltage. This is due to the fact that cutting velocity decreases with the increase in servo control mean referance voltage hence spark energy produced during spark concentrates on a particular area for larger extent and causes more surface roughness. Figure 8 presents the combined e€ect of wire feed rate and wire mechanical tension on surface roughness from which it is clear that wire mechanical tension and wire feed rate have no such signi®cant e€ect on surface roughness. This is due to the fact that the change in the surface roughness with the change in wire mechanical tension as well as wire feed rate is not signi®cant. However in order to prevent wire from from breakage and to provide proper accuracy in cutting, certain minimum wire mechanical tension and wire feed rate is selected for the machining.

Fig. 3. E€ect of Pulse Width (PW) and Time between Pulses on Cutting Velocity

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Sanjeev Kr.Garg, Alakesh Manna, Ajai Jain

Fig. 4. E€ect of Short Pulse Time and Servo Control Mean Reference Voltage on Cutting Velocity

Fig. 5. E€ect of Servo Control Mean Reference Voltage and Wire Feed Rate on Cutting Velocity

Multi-objective optimization of machining characteristics during wire electrical discharge machining...

Fig. 6. E€ect of Pulse Width and Time between Pulses on Surface Roughness

Fig. 7. E€ect of Time between Pulses and Servo Control Mean Reference Voltage on Surface Roughness

153

154

Sanjeev Kr.Garg, Alakesh Manna, Ajai Jain

Fig. 8. E€ect of Wire Feed Rate and Wire Mechanical Tension on Surface Roughness

Grey relational analysis: Multi-performance characteristic theory using the Grey relational analysis has been used to determine the optimum parametric combination when only partial information is known. According to grey theory, all known information is known as white information, all the unknown information is known as black information and partial information is known as Grey information (Deng, 1982; Deng 1989). In the present investigation, Grey relational analysis has been used to ®nd the multi-objective optimization of performance characteristics for the machining of Al/ZrO2 PRMMC. The grey relational model expresses the relationship between two elements in a system and if the relationship between two system factors is higher, the grey relational grade is large. This grey relational grade is then further used to determine the optimum parametric combination for the multi-objective optimization.

Preprocessing data: The experimental results have been normalized and this data

pre-processing step is known as the generation of grey relational data (Dang, 1982; Dang, 1989). The elements of each option have been normalized using three di€erent approaches such as larger-the-better, smaller-the-better and nominal-the-better. For the present investigation, cutting velocity has been considered as larger-the-better characteristic whereas surface roughness has been considered as smaller-the-better characteristics. Preprocessed data for cutting velocity and surface roughness has been calculated using Equations1 and 2 respectively and has been shown in table 3.

Multi-objective optimization of machining characteristics during wire electrical discharge machining...

X; …k† ˆ

min yi…k† min yi…k†

yi …k† ÿ yi …k† ÿ

max max yi…k† ÿ yi…k† Xi …k† ˆ max yi…k† ÿ min yi…k†

155

:::::::::::

…1†

::::::::::::

…2†

Where; Xi (k) is the value after grey relational generation, Min yi (k) is smallest value of yi (k) for the kth response, Max yi (k) is largest value of yi (k) for the kth response.

Calculation of grey relational coecient: The grey relational coecient has been

calculated from preprocessed data using `Equation 4'. Table 3 shows the grey relational coecient for cutting velocity and surface roughness. …X0i ; Xij † ˆ

1min ‡ 1max 1ij ‡ 1max

::::::::::

…4†

Where;

1ij ˆ jXoj ÿ Xijj;1min ˆ Min {1ij; i ˆ 1; 2; ::::::; m; j ˆ 1; 2; :::::::; n; 1max ˆ Max 1ij; i ˆ 1; :::::::2m; j ˆ 1; 2::::::n; the distinguishing coecient,,

has been used to compensate for the e€ect of the data series and is de®ned in the range 0-1. The value of  has been taken equal to 0.5.

Calculation of grey relational grade: The grey relational grade represents the

levels of relationship between the reference sequence and the comparability sequence. The grey relational grades for cutting velocity and surface roughness have been obtained using the `Equation 5' and are shown in Table 3.

…X0j ; Xij † ˆ

1

X n

n kˆ1

…X0j ; Xij †

::::::::::::

…5†

Response table for grey relational grade: The response table provides the basis to

identify the optimum parametric combination for the performance characteristics. Based on the maximum value of the parameter shown in response Table 4 for di€erent levels, the optimal level of machining parameters is A5B1C5D4 E5F1 for performance characteristics cutting velocity (CV) and surface roughness (SR) considering together for multi- objective optimization. So, the optimal machining conditions for multi-objective optimization corresponding to above said levels are: pulse width= 1.14 s; time between pulses= 4.61 s; short pulse time= 0.7 s; servo control mean reference voltage =50 volts; wire feed rate =11.1 m/min and wire mechanical tension = 0.43 daN (From table 1).

156

Sanjeev Kr.Garg, Alakesh Manna, Ajai Jain

Parameter Pulse width Time between pulses Short pulse time Servo control reference mean voltage Wire feed rate Wire mechanical tension

Table 4. Response Table Level 1 Level 2 Level 3 (-1.57) (-1) (0) 0.5612

Level 4 (1)

0.68645

0.55152

0.60018 0.58155 0.59588

0.56345 0.56202 0.56477

0.52053

0.5162

0.56748

0.64388

0.59345

0.59428 0.58746

0.56168 0.56741

0.56579 0.57261

0.65414

0.5599 0.57853 0.5642

Level 5 (+1.57) 0.62148 0.52483

0.60469 0.5815

0.62468

0.44937

Legend: Bold letters shows selected optimal value for each parameter

COMPARISON OF PERFORMANCE MEASURE AND CONFIRMATION EXPERIMENT Table 5 shows the comparison of the results obtained using initial parameter setting, Response Surface methodology and Grey Relational analysis. The percentage change in the cutting velocity and surface roughness is 5.85% and 13.37% respectively when optimum parametric combination obtained by response surface methodology is compared to the initial parameters setting. Moreover, the percentage change in the cutting velocity and surface roughness is 16.71% and 8.98% respectively when optimum parametric combination obtained by grey relational analysis is compared to the initial parameters setting. Table 6 shows the results of comparison in performance characteristics using brass and di€used wire electrode from which it is clear that there is 3.35% increase in cutting velocity and 2.17% decrease in surface roughness when performance of di€used wire electrode is compared to brass wire electrode. Moreover, no. of wire breakages are also reduced while using di€used wire electrode as compared to brass wire electrode.

Multi-objective optimization of machining characteristics during wire electrical discharge machining...

157

Table 5. Results of con®rmation experiment and comparison between initial parameter setting, response surface methodology and grey relational analysis.

Results Initial (Experimental) % Change parameter Method utilized Optimal Cutting Surface Cutting Surface S. No. setting for optimisation setting velocity roughness velocity roughness 1.

A2B2C2D2E2F2

2.

-----

3.

-----

-----

----A=1.00; B=20; Response C=0.47; Surface D=20; Methodology E=7.08; F=0.60 A5=1.14; B1=4.61; Grey Relational C5=0.7; D4=50; Analysis E5=11.1; F1=0.43

7.055

1.981

-----

-----

7.468

1.716

05.85

13.37

8.234

1.803

16.71

8.98

Legend: A:Pulse Width; B: Time between pulses; C: Short pulse time; D: Servo control reference mean voltage; E: Wire feed rate; F: Wire mechanical tension.

Table 6. Comparison of performance measures using brass and di€used wire electrode Optimum Value of Responses % Change Brass Wire Electrode Di€used Wire Electrode CVmax SRmin CVmax SRmin CVmax SRmin 1.843 8.234 1.803 3.35 2.17 7.967

CONCLUSIONS

Based on the experimental results during WEDM of Al/ZrO2 particulate reinforced metal matrix composite (PRMMC), the following points are concluded and listed below: . Cutting velocity increases with the increase in pulse width and short pulse

time. It also increases with the increase in time between pulses and servo control mean referance voltage initially but then decreases further with the increase in time between pulses and servo control mean referance voltage. Wire feed rate has no such signi®cant e€ect on cutting velocity.

. Surface roughness increases with the increase in pulse width and servo

control mean referance voltage. Surface roughness also increases with the

158

Sanjeev Kr.Garg, Alakesh Manna, Ajai Jain

increase in time between pulses initially but then decreases with the further increase in time between pulses. Wire mechanical tension and wire feed rate have no such signi®cant e€ect on surface roughness. . The optimal machining conditions for multi- objective optimization are pulse width: 1.14 s; time between pulses: 4.61s; short pulse time: 0.7s; servo

control mean reference voltage: 50 volts; wire feed rate: 11.1 m/min and wire mechanical tension: 0.43 daN.

. Di€used wire electrode provides better results in its performance related to

cutting velocity, surface roughness and breakage as compared to brass wire electrode.

. The value of performance characteristics i.e. cutting velocity= 8.234 mm/ min and surface roughness= 1.803(Ra, m) indicate that the Al/ZrO2

particulate reinforced metal matrix composite can be machined e€ectively by WEDM.

REFERENCES:

Benedict, G. F. 1987. Non-traditional Manufacturing Processes Marcel Dekker, New York. Pp. 231-232. Bhargava, A. K. 2009. Engineering Materials: Polymers, Ceramics and Composites. Eastern Economy Edition, India. Boothroyd, G., & Winston, A. K. 1989. Fundamentals of Machining. Marcel Dekker, New York: 491-510. Chiang, Ko-Ta., & Chang, F. P. 2006. Optimization of the WEDM process of particle-reinforced

material with multiple performance characteristics using grey relational analysis. Journal of Materials Processing Technology 180: 96-101. Control problem of Grey System. System control letter 5: 288-294. Deng, J., 1989. Introduction to Grey system theory. Journal of Grey system. 1: 1-24. Review of research work in sinking EDM and WEDM on metal matrix composite materials. International Journal of Advance Manufacturing Technology 50: 611-624. Modeling the machining parameters of wire electrical discharge machining of Inconel 601 using RSM. Journal of Material Processing Technology 169: 328-336. Digraph and matrix method to evaluate the machinability of tungsten carbide composite with wire EDM. International Journal of Advance Manufacturing Technology 56: 959-974. Manufacturing Engineering and Technology. Pearson Education. India. Optimization of process variables a€ecting the quality of Al-11%Si alloy castings produced by V-process. India. Ph.D. Thesis, University of Roorkee, Roorkee. Parametric Optimization of wire electrical discharge machining WEDM process using Taguchi method. Journal of the Brazil Society of Mechanical Science & Engineering 28: 422-429.

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Jangra, K., Grover, S., Chan, F. T. S., & Aggarwal, A. 2011. Kalpakjian, Serope & Schmid, & Steven, R. 2000. Kumar, P. 1993. Mahapatra, S. S., Patnaik, & Amar 2006.

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Manna, A., & Bhattacharya, B. 2006. Taguchi and Gauss elimination method: a dual response approach for parametric optimization of CNC wire cut EDM of PR-AISiC-MMC. International Journal of Advance manufacturing Technology 28: 67-75. Murthy, V. S. R.; Jena, A. K.; Gupta, K. P.; & Murty, G. S. 2005. Structure and properties of engineering materials, Tata McGraw-Hill, India. Patil, N. G. & Brahmankar, P. K. 2010. Some studies into wire electro-discharge machining of

alumina particulate-reinforced aluminum matrix composites. International Journal of Advance Manufacturing Technology 48: 537-555. Determination of material removal rate in wire electrodischarge machining of metal matrix composites using dimensional analysis. International Journal of Advance Manufacturing Technology 51: 599-610. Multi response optimization of wire EDM operations using robust design of experiments. International Journal of Advance Manufacturing Technology 29: 105-112. Electric discharge machining characteristics of metal matrix composites. Journal of Materials Processing Technology 109: 367-370. Soft computing model based prediction of cutting speed and surface roughness in wire electro-discharge machining of tungsten carbide cobalt composite. International Journal of Advance Manufacturing Technology 39: 74-84. Surface roughness, Kerf and Surface Roughness of Tungsten Carbide machined with wire electrical discharge machining. Journal of Materials Engineering and Performance 20 (1): 71-76. Foundations of Materials Science and Engineering, McGraw-Hill, India. Yan, B. W., Tsai, H. C., Huang, F. Y. & Lee, L. C. 2005. Examination of wire electrical discharge machining of Al2O3p/ 6061 Al composites. International Journal of Machine Tools and Manufacture 45: 251-259.

Patil, N. G. and Brahmankar, P. K. 2010.

Ramakrishnan, R. & Karunamoorthy, L. 2006.

Rozenek, M., Kozak, J., Dabrowski, L. & Lubowski, K. 2001. Saha, P., Singha, A., Pal, S. K., & Saha, P. 2008.

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Smith, W. F. 2004.

Submitted : 3/ May/ 2012 Revised : 7/ Oct /2012 Accepted : 20/ Nov / 2012

160

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