Optimization of machining parameters for EDM operations based on central composite design and...

Journal of Mechanical Science and Technology 28 (3) (2014) 1045~1053

www.springerlink.com/content/1738-494x DOI 10.1007/s12206-013-1180-x

Optimization of machining parameters for EDM operations based on

central composite design and desirability approach† Subramanian Gopalakannan1,* and Thiagarajan Senthilvelan2

1Department of Mechanical Engineering, Adhiparasakthi Engineering College, Melmaruvathur, Tamilnadu-603 319, India 2Department of Mechanical Engineering, Pondicherry Engineering College, Puducherry-605 014, India

(Manuscript Received May 27, 2012; Revised August 4, 2013; Accepted October 19, 2013)

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Abstract A novel aluminium metal matrix composite reinforced with SiC particles were prepared by liquid metallurgy route. Recent develop-

ments in composites are not only focused on the improvement of mechanical properties, but also on machinability for difficult-to-machine shapes. Electrical discharge machining (EDM) was employed to machine MMC with copper electrode. using EDM. Experi-ments were conducted using pulse current, gap voltage, pulse on time and pulse off time as typical process parameters. The experiment plan adopts face centered central composite design of response surface methodology. Analysis of variance was applied to investigate the influence of process parameters and their interactions viz., pulse current, gap voltage, pulse on time and pulse off time on material re-moval rate (MRR), electrode wear ratio (EWR) and surface roughness (SR). The objective was to identify the significant process parame-ters that affect the output characteristics. Further a mathematical model has been formulated by applying response surface method in order to estimate the machining characteristics such as MRR, EWR and SR.

Keywords: EDM; Metal matrix composite; Response surface method; Central composite design; Desirability; Optimization ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 1. Introduction

Metal matrix composites (MMCs) are one of the recent ad-vanced materials having the properties of light weight, high specific strength, good wear resistance and a low thermal ex-pansion coefficient. These composite materials are extensively used in structural, aerospace and automotive industries. The applications of existing aluminium silicon carbide MMCs are limited because of their poor machinability which results in poor surface finish and excessive tool wear. MMCs are com-posed of metallic base material called matrix, which is rein-forced with a hard ceramic reinforcement [1-3]. Due to pos-session of higher hardness and reinforcement strength, com-posite materials are difficult to be machined by traditional techniques. Hence electrical discharge machining (EDM) process becomes viable method to these kinds of composite materials. Since the EDM process does not involve mechani-cal energy, the material removal rate is not influenced by the material properties like hardness, strength, toughness etc. with poor machinability such as cemented tungsten carbide and composites can also be processed without much difficulty by the EDM process [4, 5].

Several investigations into the machining aspects of EDM on MMCs with only single particulate reinforcement have been carried out and reported. George et al. investigated the carbon- carbon composites considering three parameters at two levels and reported that pulse current and pulse on time are significant for EWR and MRR [6]. The effect of percent-age volume of SiC and other machining characteristics were studied while machining Al-SiC, and concluded that increase in SiC decreases the MRR, where as increases EWR and SR [7, 8]. The effect of rotation of electrode on EDM of Al-SiC and Al- Al2O3 composites yielded positive effect on MRR, EWR and SR [9, 10]. Harmesh Kumar and Paulo Davim have carried out an experimental study on the machining parame-ters in powder mixed electric discharge machining of Al-10%SiC MMC. They mixed silicon powder into the dielectric fluid and reported that the addition of silicon powder into the dielectric fluid of EDM increases MRR and decreases SR [11].

The present work is envisaged to develop a mathematical model and analyze the effects of EDM parameters on the per-formance characteristics of MMC using response surface me-thodology (RSM). Accordingly, the quantitative mathematical models have been carried out to study influence of pulse cur-rent (Ip), voltage (Vg), pulse on time (Ton) and pulse off time (Toff) on the material removal rate (MRR), electrode wear rate (EWR) and surface roughness (SR) by using RSM [12].

*Corresponding author. Tel.: +91 9944949026, Fax.: +91 44 27529094 E-mail address: [email protected]

† Recommended by Associate Editor Sung Hoon Ahn © KSME & Springer 2014

1046 S. Gopalakannan and T. Senthilvelan / Journal of Mechanical Science and Technology 28 (3) (2014) 1045~1053

2. Experimental details

2.1 Work material and ceramic reinforcement

The material used in the present investigation consists of Aluminium 7075 (Al-Zn-Mg-Cu alloy) is used as the base matrix alloy. Its chemical composition (%) is Si = 0.2, Fe = 0.22, Cu = 2.0 max, Mn = 0.1, Mg = 2.1-2.9, Zn = 5.1-6.1, Ti = 0.1 max, Cr = 0.2, and balance as Al. It is a very high strength material used for highly stressed structural parts. The applications of Al 7075 are Aircraft fittings, gears and shafts, fuse parts, meter shafts and gears, missile parts, regulating valve parts, worm gears, keys, aircraft, aerospace and defense applications; bike frames, all terrain vehicle (ATV). Alumin-ium-zinc-magnesium alloys have a greater response to heat treatment than the binary aluminium-zinc alloys resulting in higher possible strengths. It possesses high heat dissipation capacity due to its high thermal conductivity and is suitable for high strength and high temperature applications. Silicon carbide (SiC) has excellent high-temperature strength, a very high oxidation ability and good chemical resistance. Its ther-mal conductivity is four times that of steel and it has low thermal expansion co efficient, hence it is preferred for high temperature heat exchangers.

2.2 Preparation of MMC by stir casting method and its me-

chanical properties

The aluminium matrix was reinforced with 10wt% of SiC with an average particle size of 25 microns. The composites were cast using stir casting technique as it ensures uniform distribution of the reinforcements [13]. The schematic view of stir casting setup with electrical furnace and stirring assembly is shown in Fig. 1. The SEM micrograph of MMC shows the uniform dispersion of the SiC particles is given in Fig. 2. From the cast MMC the standard tensile specimens were pre-pared by machining as per dimensions of ASTM E8. To ob-tain mechanical properties, specimens with overall length 100 mm, thickness of 6 mm and a gauge length of 25 mm were tested in UNITEK - 94100 universal testing machine which gives an ultimate tensile strength of 181.694 MPa and yield strength of 154.372 MPa. The hardness of the samples was measured using a UHL Vickers micro hardness measuring

machine by applying a load of 0.5 kg and this load was ap-plied for 20 seconds yielded 120.5 HV. In order to eliminate the possibility of error a minimum of five hardness readings were taken for each sample.

2.3 Design of experiments

Response surface methodology (RSM) is an interaction of mathematical and statistical techniques for modeling and op-timizing the response variables which incorporates quantita-tive independent variables. The behavior of the system is ex-plained by the following second order polynomial regression model also called a quadratic model. The coefficients of re-gression model can be estimated from the experimental results by ‘Design Expert 8.0.6’ software.

2 .D i i i iY C C X d X z= + > + > ± (1)

In the present study the experiments were designed on the

basis of the central composite design (CCD) technique. The factorial portion of CCD is a full factorial design with all combination of the factors at two levels (high, +1, and low, -1) and composed of eight star points, and six central points (coded level 0), which is the midpoint between the high and low levels, corresponds to an α value of 1. The “face-centered CCD” involves 30 experimental observations at four inde-pendent input variables. The Table 1 shows both the coded and actual values of the four machining parameters and their possible ranges [14]. The experimental layout that was

Fig. 1. Schematic view of setup for fabrication of composite.

Table 1. Process parameters and their levels.

Level S.No. Parameter

-1 0 +1

1 Voltage [V] 40 50 60

2 Current [A] 6 10 14

3 Pulse on time [Ton] µs 4 6 8

4 Pulse off time [Toff] µs 5 7 9

Fig. 2. SEM micrograph showing the SiC particle distribution.

S. Gopalakannan and T. Senthilvelan / Journal of Mechanical Science and Technology 28 (3) (2014) 1045~1053 1047

adopted in this study in the actual form is shown in Table 2.

2.4 Experimental procedure

A series of experiments were performed on a die-sinking EDM of type Grace D-6030S based on Table 2. The sche-matic diagram of the EDM process is shown in Fig. 3. The work materials of size diameter 20 mm and thickness 30 mm, and electrolytic copper electrode of 10 mm diameter was used. The circular electrode is preferred over the other shapes of

electrodes, provides higher MRR and lower EWR [15]. Commercial grade kerosene was employed as the dielectric fluid and impulse jet flushing system was used to flush away the eroded materials from the sparking zone. The machining is done for 20 minutes for all experiments. The material removal rate and electrode wear values have been calculated by weight difference of the workpiece and electrode material before and after the machining using a digital weighing scale of 0.001 gram precision.

3. Results and discussion

The machining performance criteria selected for this study were based on performance characteristics such as material removal rate (MRR), electrode wear rate (EWR) and surface roughness (SR) [16].

MRR = (wjb-wja) / t (2)

where wjb and wja are weights of the work piece before and after machining, and the machining time. Electrode wear (EW) is expressed as the ratio of difference of weight of the tool electrode before and after machining to the machining time.

Table 2. Design layout and experimental results.

Exp. No. A: Voltage B: Current C:Ton D:Toff MRR [g/min] EWR [g/min] SR [µm] 1 40 6 4 9 0.29 0.001 6.245 2 60 6 8 9 1.047 0.004 14.322 3 50 10 4 7 0.795 0.003 7.545 4 50 10 6 7 0.329 0.008 14.717 5 50 6 6 7 1.046 0.003 9.149 6 60 14 4 5 0.1622 0.013 9.577 7 40 14 8 9 0.484 0.011 16.758 8 40 6 8 9 1.178 0.003 10.389 9 50 10 6 9 0.8175 0.006 12.196 10 40 6 4 5 0.0854 0.005 6.301 11 60 10 6 7 0.3682 0.009 18.214 12 60 6 8 9 0.738 0.014 21.324 13 50 10 6 7 0.3521 0.008 10.325 14 50 10 8 7 0.3621 0.008 13.608 15 40 14 4 9 0.0866 0.004 6.753 16 50 10 6 7 0.342 0.008 12.485 17 50 10 6 7 0.372 0.008 14.867 18 60 6 4 9 0.292 0.001 9.04 19 60 14 8 5 0.598 0.012 14.514 20 60 6 4 5 0.0918 0.004 7.647 21 40 14 8 5 0.361 0.012 15.845 22 60 14 4 9 0.1165 0.003 10.168 23 50 14 6 7 0.478 0.010 11.728 24 50 10 6 7 0.354 0.008 16.243 25 60 6 8 5 0.157 0.004 11.558 26 50 10 6 7 0.369 0.008 15.851 27 40 14 4 5 0.165 0.007 10.008 28 40 6 8 5 0.142 0.005 13.289 29 50 10 6 5 0.376 0.007 12.512 30 40 10 6 7 0.3192 0.006 12.629

Fig. 3. Schematic diagram of the EDM process.


EW=(web-wea) / t (3)

where web and wea are weights of the tool electrode before and after machining, and the machining time.

The material removal rate and electrode wear values have been calculated by weight difference of the work material and the electrode before and after machining using a digital weigh-ing scale and recorded. The average surface roughness value Ra

(µm) was chosen to assess the surface finish quality. The sur-face of material generated using EDM is composed of many microscopic craters associated with random spark discharge between the electrodes. The size of craters produced mainly on the work piece surface depends mainly upon the energy of the discharge. As more energetic pulses usually lead to a higher material removal, so a deeper cavity was formed. As the cavity depth increases the roughness value also increases [4]. The surface roughness measurements for the machined surface are performed with a Kosaka Surfcoder SE 1200.

3.1 Mathematical model for MRR, EWR and SR

The fit summary recommended that the quadratic model is statistically significant for analysis of MRR and SR and linear model for EWR. The results quadratic and linear models are given in ANOVA Table 3. When the R2 ap approaches unity, the better the response model fits the actual data. It exists the less the difference between the predicted and actual data. Fur-ther the value of adequate precision (AP) in this model, which compares the range of the predicted value at the design point to the average prediction error, is well above 4. The values obtained are as follows: R2 = 0.9765and AP= 42.262 for MRR; R2 = 0.9365 and AP= 36.436 for EWR; R2 = 0.8865 and AP= 22.093 for SR. The backward elimination process eliminates the insignificant terms to adjust the fitted quadratic models. These insignificant model terms can be removed and the test of lack of fit displays not significant as it is desired. The final response equations for MRR, EWR and SR are:

Table 3. The ANOVA table for the fitted models.

a)For MRR Sourse SS df MS F-value Prob>F Model 1.39 8 0.13 125.97 < 0.0001 Significant Residual 1.20 21 1.027E-003 Lack of Fit 0.91 16 1.327E-003 152.49 0.32485 Not significant Pure Error 0.28 5 8.700E-006 Cor Total 2.58 29 Std. Dev. 0.24 R-Squared 0.9765 Mean 0.42 Adj R-Squared 0.9399 C.V. % 56.51 Pred R-Squared 0.8534 Predicted residual error of sum of squares (PRESS) = 2.44 AdeqPrecision 42.262 b)For EWR Sourse SS df MS F-value Prob>F Model 2.98 5 5.973E-005 27.18 < 0.0001 Significant Residual 5.27 24 2.198E-006 Lack of Fit 0.51 19 2.776E-006 1.24673 Not significant Pure Error 3.545 0.000 Cor Total 3.514E-004 29 Std. Dev. 1.482E-003 R-Squared 0.9365 Mean 6.767E-003 Adj R-Squared 0.8159 C.V. % 21.91 Pred R-Squared 0.7634 Predicted residual error of sum of squares (PRESS) = 9.340E-005 Adeq Precision 36.436 c)For SR Sourse SS df MS F-value Prob>F Model 259.63 9 102.62 4.27 < 0.0001 Significant Residual 135.13 20 6.76 Lack of Fit 114.06 15 7.60 1.80 0.2699 Not significant Pure Error 21.07 5 4.21 Cor Total 394.76 29 Std. Dev. 2.60 R-Squared 0.8865 Mean 1.29 Adj R-Squared 0.8199 C.V. % 21.32 Pred R-Squared 0.7253 Predicted residual error of sum of squares (PRESS) = 306.74 Adeq Precision 22.093


Material Removal Rate (MRR): InCodedTerms: MRR =+0.46-0.029* B+0.044* C+0.068* D-0.11* B * C-0.17* B * D+0.13* C * D-0.29* B2+0.23 * C2 . (4) InActualFactors: MRR =+0.45604-0.028717* Current+0.044039* Ton+0.068283* Toff-0.10618* Current * Ton- 0.17487*Current*Toff+0.13429*Ton*Toff- 0.28971*Current2+0.23379*Ton2 . (5) Electrode wear rate: In coded Terms EWR= + 6.767E-003+3.111E-003*B + 1.778E-003*C - 1.222E-003*D +1.062E-003* B*C + 1.188E-003 * C*D . (6) In Actual Factors: EWR = + 6.7587E-003+3.19097E-003*Current – 1.77836E-003*Ton-1.22196E-003*Toff +1.06239E- 003*Current*Ton+1.1875E-003*Ton*Toff . (7) Surface Roughness: In Coded Terms:

SR =+13.01-0.28* A+0.82* B+1.52 * C+0.59* D- 1.21*A* D+1.42* B * C-1.17* B * D+4.55* A2- 5.91*D2 . (8) In Actual Factors: SR=+13.01098-0.27933*Voltage+ 0.81656* Current+1.51839* Ton+0.59122* Toff-1.20669* Voltage * Toff+1.42281*Current*Ton-1.17219* Current*Toff+4.54560*Voltage2-5.90740* Toff2 . (9)

3.2 Effect of process parameters on MRR

The discharge energy was normally smaller when the pulse current was smaller, hence the smaller discharge energy deliv-ered into the machining zone was associated with a lower MRR therefore the machined cavity was shallower and the debris was more easily expelled from the machining zone. In contrast higher the peak current higher the discharge energy, therefore deeper cavity was formed. However the cavity depth increases the debris normally became harder to expel from the machining zone [17]. This disturbs the electrical discharge and causes short-circuit, results in low MRR. Hence optimal value of pulse current is necessary to achieve maximum MRR. The experimental results for MRR, EWR and SR are given in Ta-ble 2.

Fig. 4 shows the estimated response surface for MRR in re-lation to the design parameters of pulse current, pulse on time and pulse off time. As can be seen from the Fig. 4(a) the MRR increases considerably with increase in pulse current and pulse on time, similarly for pulse off time as well shown in Fig. 4(b). However the MRR increases with respect to pulse current for any value of voltage. This is due to their dominant control over the input energy [18]. Thus the voltage is an insignificant parameter for MRR whereas Ton and Toff are significant parameters.

3.3 Effect of process parameters on EWR

The wear of tool electrode is a dynamic process which is simultaneously influenced by different parameters with vary-ing input values. While electrical discharges erode materials from both the tool electrode and work piece, the cracked car-bon from the dielectric fluid may be deposited on the surface of tool electrode which protects them from further erosion. Generally longer pulse duration, lower pulse current and pulse off time tends to increase the possibility of carbon deposition on the electrode surface, which helps to minimize the elec-trode wear [19]. The estimated response surface for EWR in relation to the design parameters of pulse current, pulse on time and pulse off time is shown in Fig. 5. As can be seen

(a)

(b)

(c)

Fig. 4. (a)-(c) shows the response of current, ton and toff on MRR.


from the Fig. 5(a), the EWR increases considerably with in-crease in pulse current and pulse on time. The EWR is more at higher value of Ton and Toff, whereas the EWR increases with respect to pulse current for any value of voltage. Thus the voltage is an insignificant parameter for EWR whereas Ton and Toff are significant parameters [20].

3.4 Effect of process parameters on SR

In case of surface roughness, the most influencing parame-ters are pulse current, pulse on time. When any one of this parameter is increased, it enhances the surface roughness value. The high energy pulse produces crater on the machined surface which leads to poor surface finish quality. The esti-

mated response surface for SR in relation to the design pa-rameters of pulse off time and voltage is shown in Fig. 6(a), pulse current and pulse on time in Fig. 6(b). As can be seen from this figure, the SR tends to increase as the pulse current increases, where as with voltage it increases up to 50 V and then decreases. The SR also increases with increase in pulse on time. This is due to their dominant control over the input energy.

4. Multi response optimization

Selection of the optimal machining parameter combination

(a)

(b)

(c)

Fig. 5. (a)-(c) shows the response of voltage, current, ton and toff on EWR.

(a)

(b)

(c)

Fig. 6. (a)-(c) shows the response of voltage, current, ton and toff on EWR.


for achieving improved process performance, e.g., material removal rate, electrode wear rate and surface roughness, is a challenging task in EDM operation due to the presence of a large number of process variables and complicated stochastic process mechanism. Derringer and Suich [14] describes a multiple response method called desirability. It is an attractive method for industry for optimization of multiple quality char-acteristics problems. The method makes use of an objective function D(X), called the desirability function (Utility transfer function) and transform an estimated response into a scale-free value (di) called desirability. The desirable range are from 0 to 1 (least to most desirable, respectively). A value of 1 repre-sents the ideal case; 0 indicates that one or more responses are outside their acceptable limits. Composite desirability is the weighted geometric mean of the individual desirability for the responses. The factor settings with maximum total desirability are considered to be the optimal parameter conditions. The simultaneous objective function is a geometric mean of all transformed responses [21]. This combination has been evalu-ated with the help of Design Expert Software. Three responses i.e., MRR, EWR, and SR, have been optimized simultane-ously using developed models, i.e., Eqs. (4)-(9), based on composite desirability optimization technique. In multi-response optimization, a measure of how the solution has sat-isfied the combined goals for all responses must be assured. The optimality solution is to evaluate the input process pa-rameters in experiment range for maximizing MRR and minimizing both EWR and SR. The optimum values of input parameters and the predicted values of responses are presented in Tables 4 and 5, respectively.

The range and goals of input parameters viz. voltage, pulse current, pulse on time and pulse off time and the output char-acteristics viz. material removal rate, electrode wear rate and surface roughness are given in Table 4. The goal of optimiza-tion is to find a set of conditions that will meet all the goals. It is not necessary that the desirability value is 1.0 as the value is completely dependent on how closely the lower and upper limits are set relative to the actual optimum.

A set 30 optimal solution is derived for the specific design space constraints for MRR, EWR and SR using Design Expert statistical software. The set of conditions possessing highest desirability value is selected as optimum condition for the desired responses. The optimal set of conditions with higher desirability function is given in Table 5. Once the optimal level of the process parameters is selected, the final step is to predict and verify the improvement of the performance char-acteristics using the optimal level of the machining parameters. Experiment was performed to machine and verify the EDM at the above optimal input parameter setting for MRR, EWR and SR compared with optimal response value. Table 6 shows the percentage of error for experimental validation of the devel-oped models for the responses with optimal parametric setting during EDM. From the analysis of Table 6, it can be observed that the error calculated is small. Obviously, this confirms excellent reproducibility of the experiment conclusions.

The ramp function graph and bar graph Figs. 7 and 8 show the desirability for output responses. The dot on each ramp reflects the factor setting or response prediction for that re-sponse characteristic. The height of the dot shows how much desirable it is. A linear ramp function is created between low value and the goal or the high value and the goal as the weight for each parameter was set equal to one. Bar graph shows the overall desirability function of the responses. Desirability varies from 0 to 1 depending upon the closeness of the re-

Table 4. Range of parameters and responses for desirability of Al+10wt%SiC MMC.

Process parameter Goal Lower limit

Upper limit

Impor-tance

Voltage In range 40 60 3

Current In range 6 14 3

Pulse on time In range 4 8 3

Pulse off time In range 5 9 3

MRR Maximize 0.0854 1.178 3

EWR Minimize 0 0.033 3

SR Minimize 6.245 21.324 3 Table 5. Optimum values of Al+10wt%SiC MMC.

Parameter Goal Optimum value

Voltage [V] In range 48.12

Pulse current [A] In range 6.00

Pulse on time [µs] In range 8.00

Pulse off time [µs] In range 8.97

Table 6. Predicted and observed values of Al+10wt%SiC MMC.

Response Goal Predicted Observed Error [%]

MRR[g/min] Maximize 1.2654 1.196 5.9

EWR[g/min] Minimize 0.0014 0.0015 -3.5

SR[µm] Minimize 9.913 10.648 -6.9

Fig. 7. Ramp function graph of Desirability for Al+10wt% SiC MMC.


sponse towards target. The bar graph describes how well each variable satisfies the criterion, value close to one are consid-ered good. Desirability 3D-plots were drawn by keeping the input parameters in range and output response MRR at maxi-mum, EWR at minimum and SR at minimum. Fig. 9 shows a plot of desirability function distribution of desired responses of Al+10wt% SiC MMC according to current and voltage. It can be interpreted that overall desirability value is less in the

region of high pulse current and voltage [22]. The near opti-mal region was located close to the right hand top region of the plot, which had overall desirability value greater than 0.898 that gradually reduced while moving left and down-wards. Hence the interpreted desirability of 0.898 which indi-cates the closeness of the response target.

The SEM picture of the confirmation experiments con-ducted sample was investigated and is shown in Fig. 10. It is evident from the SEM micrograph that there was a clear EDM damaged layer on the workpiece distinguished by the SiC grains and micro cracks because of higher pulse current [23]. The SiC grains are sparsely distributed in the damaged layer and many of them dislodged during EDM process.

5. Conclusion

An application of combined central composite design and desirability approach to improve the multiple performance characteristics of MRR, EWR and SR in EDM of aluminium metal matrix composite has been reported in this paper. As a result, this method greatly simplifies the optimization of com-plicated multiple performance characteristics.

(1) The predicted values match the experimental values rea-sonably well with R2 of MRR, EWR and SR.

(2) Pulse current was found to be the most important factor affecting all the tree output parameters MRR, EWR and SR.

(3) The main significant factors that affect the MRR are pulse current, pulse on time and pulse off time. The pulse current and pulse on time have statistical significance on both EWR and SR.

(4) The higher pulse off time offers lower the EWR value. On contrary, the EWR increases with increase in pulse current and pulse on time for any value of voltage.

(5) The value SR increases with increase in pulse current and pulse on time, whereas in age is concerned SR increases up to 50 V and then decreases with a further increase in volt-age.

(6) The optimum parameter of combination setting is Volt-age 48.12 V, Pulse current 6.00 A, Pulse on time 8.00µs and pulse off time 8.97µs for maximizing MRR, minimizing EWR and SR.

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S. Gopalakannan is currently working as Associate Professor in the Depart-ment of Mechanical Engineering Adhi-parasakthi Engineering College, Tamil-nadu, India. He received his B.E Me-chanical Engineering from University of Madras, M.E Production Engineering from Annamalai University and Ph.D. in

Mechanical Engineering from Pondicherry University. He published 10 International Journals, 8 International Confer-ences and 1 National Conference. His research areas include characterization of nanocomposites, machining of newer ma-terials, unconventional machining processes and optimization techniques.

T. Senthilvelan is working as Professor in the Department of Mechanical Engi-neering, Pondicherry Engineering Col-lege, Puducherry, India. He received his B.E Mechanical Engineering,, M.E Pro-duction Engineering and Ph.D. Manu-facturing Engineering from Annamalai University in 1986, 1988 and 2003 re-

spectively. He guided 5 Ph.D. scholars and currently guiding 9 scholars. He published 47 International Journals, 2 national Journals, 20 International Conferences and 18 National Con-ferences. His research interests include metal forming, powder metallurgy, fabrication and characterization of composites and nanocomposites, machining of newer materials and optimiza-tion techniques.

Optimization of machining parameters for EDM operations based on central composite design and...

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