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30120140501006

  1. 1. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING 6340(Print), ISSN 0976 – 6359(Online) Volume 5, Issue 1, January (2014), © IAEME AND TECHNOLOGY (IJMET) ISSN 0976 – 6340 (Print) ISSN 0976 – 6359 (Online) Volume 5, Issue 1, January (2014), pp. 57-67 © IAEME: www.iaeme.com/ijmet.asp Journal Impact Factor (2013): 5.7731 (Calculated by GISI) www.jifactor.com IJMET ©IAEME PROCESS PARAMETER OPTIMISATION IN WEDM OF HCHCR STEEL USING TAGUHI METHOD AND UTILTIY CONCEPT Ms. Shalaka Kulkarni* and ManikRodge** *Research Scholar, **Associate Professor Production Engineering Dept., SGGSIE&T, Nanded (India) ABSTRACT Wire Electrical Discharge Machining (WEDM) is used as a valuable machining tool in the world of non-traditional machining due to various features which includes higher degree of accuracy, fine surface quality and good productivity. WEDM consists of large number of process parameters, thus it is difficult to obtain a combination of optimum parameters which provides higher accuracy. Optimization of a single response is often carried out with the well known technique Taguchi method. This method results in the solution which gives optimum value of each response. During the manufacturing the performance of a product can be evaluated by several response variables. Optimization of a single response may result in the non optimum solution for the remaining responses; this problem can be solved by multi-characteristics response optimization. In this paper, an attempt is made to study the effect of various process parameters such as pulse on time, pulse off time, wire feed, wire tension, upper flush and lower flush for high carbon high chromium steel. The experimentation has been completed with the help of Taguchi’s L25 Orthogonal Array. Taguchi’s method and utility concept is used to optimize the process parameters on multiple performance characteristics such as Material removal rate, surface finish and kerf width.The experimental result analysis showed that the combination of higher levels of pulse on time, pulse off time, wire feed and lower flush and lower level of wire tension and upper flush is essential to achieve simultaneous maximization of material removal rate and minimization of surface roughness and kerf width. KEYWORDS: Genetic algorithm (GA), Gray based analysis (GRA), Orthogonal array (OA), Signal to noise ratio (S/N Ratio), Techniques for Order Preference by Similarity to Ideal Solution (TOPSIS). 57
  2. 2. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 5, Issue 1, January (2014), © IAEME 1. INTRODUCTION Accompanying the development of mechanical industry, the demands for alloy materials having high hardness, toughness and impact resistance are increasing. Wire EDM machines are able to cut the materials regardless of its hardness. The machines also specialize in cutting complex contours on fragile geometries that would be difficult to produce using conventional cutting methods. Such complex geometries can be found in intricate punches, dies, and various spindles. In the manufacturing of these products High carbon high chromium steel is used due to their good dimensional accuracy, good wear resistance, higher machinability, very high compressive strength, good corrosion resistance, and effective cost. WEDM is a non-traditional thermoelectric process, which erode materials from the workpiece by series of discrete sparks between tool and workpiece. Deionized water is used as a dielectric medium. This dielectric medium provides insulation and ionization to the system. Enormous amount of energy is produced after the generation of spark this causes heating of the tool and workpiece.This heat is carried away by dielectric medium. Flushing of the dielectric in the spark gap prevents the contamination of debris and premature discharge. Surface roughness is the most significant performance measure in quality of a product. Along with surface roughness material, removal rate is also an important characteristic in various manufacturing operation. [1]. Hence while manufacturing a product the main objective is to achieve a higher MRR at lower SR. This need will results in the process of optimization. Taguchi method is a well known tool of optimization. The parametric optimization of WEDM process has been described by Mahapatra S.S and Amar patnaik [2]. Results of the optimization shows the combinations of discharge current, pulse duration, pulse frequency, wire speed, wire tension and dielectric flow rate giving higher MRR and lower SR. While machining EN-31 tool steel for achieving higher MRR, discharge currentwas the most significant factor. Sivakiran S. et al [3] predicted MRR values with 6.77% error using regression analysis. Use of full factorial design solves the problem with minimum number of number of experiments [4]. The analysis gives a combination of process parameters for better surface finish. The authors [4], [5], [6], [7], [8], [9] also used taguchi method of optimization of process parameter but all of them have considered single response variable at a time. The optimum combination for one response variable may result in the non optimum solution for other responses, when number of responses are to be considered. This problem can be solved with the help of multiple response optimization method. Modelling and optimization of process parameters in powder mixed electrical discharge machining (PMEDM) has been studied by Farhad Kolahan [10]. The process output characteristics include MRR and EWR. Grain size of Aluminium powder (S), concentration of the powder (C), discharge current (I) pulse on time (T) are chosen as control variables to study the process performance. Regression models are developed using experimental results. A genetic algorithm (GA) has been employed to determine optimal process parameters for any desired output values of machining characteristics. Another effective way of solving multi response problem is to use of taguchi method [9]. This approach reduces complexity and effort of data analysis. Use of taguchi method to derive objective function and Gray based analysis (GRA) to carry out multi response optimization in turning process is evaluated by Yigit kazancoglue [11]. A multi-characteristics response optimization model based on Taguchi and Utility concept is used by M. Kaladhar et al [1]. From the literature it is found that there is the need of multi response optimization. Various techniques are available for such optimization which includes genetic algorithm, gray based analysis, TOPSIS, Taguchi method and utility concept. In this paper, we present a method for finding the fitness function (several objectives are to be combined to have fitness function) with the consideration of user preference. 58
  3. 3. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 5, Issue 1, January (2014), © IAEME 2. DESIGN OF EXPERIMENT 2.1 Selection of Orthogonal Array The experiment design is done based on the Taguchi Method. Genichi Taguchi a Japanese scientist developed a technique based on Orthogonal Array of experiments. This technique has been widely used in different fields of engineering to optimize the process parameters [1]. The control factors considered for the study are Pulse-on time (Ton), Pulse-off time (Toff), Wire feed (Wf), Wire tension(Wt), Upper flush (Uf), and Lower flush (Lf). Five levels for each control factor are be used. Based on number of control factors and their levels, L25 orthogonal array (OA) is selected.Table-1 represents various levels of control factors and Table-2 represents experimental plan with assigned values. Table-1: Levels of various control factors Factors Ton Toff Wf Wt Uf Lf level 1 4 3 5 500 6 5 level 2 5 4 6 600 7 6 level 3 6 5 7 700 8 7 level 4 7 6 8 800 9 8 level 5 8 7 9 900 10 9 Units µsecond µsecond mm/sec Gm kg/cm2 kg/cm2 Table -2: L25 OA with assigned values of control Factors Exp.No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Ton 4 4 4 4 4 5 5 5 5 5 6 6 6 6 6 7 7 7 7 7 8 8 8 8 8 Toff 3 4 5 6 7 3 4 5 6 7 3 4 5 6 7 3 4 5 6 7 3 4 5 6 7 Wf 5 6 7 8 9 8 9 5 6 7 6 7 8 9 5 9 5 6 7 8 7 8 9 5 6 59 Wt 500 600 700 800 900 900 500 600 700 800 800 900 500 600 700 700 800 900 500 600 600 700 800 900 500 Uf 6 7 8 9 10 7 8 9 10 6 8 9 10 6 7 9 10 6 7 8 10 6 7 8 9 Lf 5 6 7 8 9 7 8 9 5 6 9 5 6 7 8 6 7 8 9 5 8 9 5 6 7
  4. 4. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 5, Issue 1, January (2014), © IAEME 2.2 Selection of Material The work piece material used in this study is High Carbon High Chromium [HCHCr] steel grade D3 and its chemical composition is given in Table -3. Table-3: Chemical Composition of HCHCr steel % Carbon 2.05 % Silicon 0.43 % Manganese 0.25 % Phosphorous 0.029 % Sulphur 0.04 % Chromium 12.08 % Iron Remainder 2.3 Experimental Work The experiments were performed on Electronica make maxicut 734 CNC Wire-cut electrical discharge machining (WEDM). The basic parts of the WEDM machine consists of a wire Electrode, a work table, and a servo control system, a power supply and dielectric supply system. Maximum movement of X and Y axis is 300, 400 mm respectively. Maximum Taper angle is 15°per 100 mm. the wire material used is Zn coated Brass of diameter 0.25 mm. 2.4 Utility Concept To improve the rational decision making, the evaluations of various attributes should be combined to give a composite index. Such a composite index is known as utility of a product.The sum of utilities of each quality attribute represents the overall utility of a product. It is difficult to obtain the best combination of process parameters, when there are multi-responses to be optimized. The adoption of weights in the utility concept helps in these difficult situations by differentiating the relative importance of various responses. If xi represents the measure of effectiveness of i th process response characteristic and n represents no. of responses, then the overall utility function can be written as [9] Uሺxଵ , xଶ … . x୬ ሻ ൌ fሾUଵ ሺxଵ ሻ, Uଶ ሺxଶ ሻ, … U୬ ሺx୬ ሻሿ…………………………………..……...…(1) where U(X1, X2,...,Xn) is the overall utility of n process response characteristics and Ui(Xi) is utility of i th response characteristic. Assignment of weights is based on the requirements and priorities among the various responses. Therefore the general form or weighted from of equation can be expressed as 60
  5. 5. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 5, Issue 1, January (2014), © IAEME Uሺxଵ , xଶ … x୬ ሻ ൌ ∑୬ W୧ ‫ כ‬U୧ ሺx୧ ሻ…………………………………………………………. (2) ୧ୀଵ where, ∑୬ W୧ ൌ 1………………………………………………………………………………… (3) ୧ୀଵ where Wi is the weight assigned to the ith response characteristic 3. RESULT AND ANALYSIS The objective of the present work is to minimize surface roughness (SR), kerf width (KW) and maximize the MRR in WEDM process optimization.Taguchi technique uses S/N ratio as a performance measure to choose control levels. The S/N ratio considers both the mean and the variability. In the present work, a multi- response methodology based on Taguchi technique and Utility concept is used for optimizing the multi-responses (SR, KW and MRR). Taguchi proposed many different possible S/N ratios to obtain the optimum parameters setting. Two of them are selected for the present work. Those are, Larger the better S/N ratio for MRR ሾߟଵ ሿ ൌ െ10 ‫݃݋݈ כ‬ଵ଴ ቂ ଵ ெோோ మ ቃ……………………………………………………………….. (4) Smaller the better type S/N ratio for SR ሾߟଶ ሿ ൌ െ10 ‫݃݋݈ כ‬ଵ଴ ሾܴܵ ଶ ሿ………………………………………………………………… (5) Smaller the better type S/N ratio for KW ሾߟଷ ሿ ൌ െ10 ‫݃݋݈ כ‬ଵ଴ ሾ‫ ܹܭ‬ଶ ሿ……………………………………………………………….. (6) From the utility concept, the multi-response S/N ratio of the overall utility value is given by ߟை௕௦ ൌ ߟଵ ܹଵ ൅ ߟଶ ܹଶ ൅ ߟଷ ܹଷ ………………………………………………………….... (7) Where W1, W2&W3 are the weights assigned to the MRR, KW and SR respectively. Assignment of weights to the performance characteristics are based on experience of engineers, customer’s requirements and their priorities. In the present work, we have considered MRR as the first priority and is weighted 50%, the second priority was surface roughness and thus the weight is 30%, while KW was third priority and thus the weight is 20%. The analysis is done using the popular software specifically used for design of experiment applications known as MINITAB 15. The S/N ratio for MRR, SR and KW is computed using above equations for each treatment as shown in Table-4. 61
  6. 6. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 5, Issue 1, January (2014), © IAEME Table-4: Experimental results for MRR, SR, KW along with S/N ratios Exp. MRR η1 for MRR SR η2 for SR KW η3 for KW No. 1 2.7 8.6273 2.513 -8.0039 0.0416 27.6182 2 2.6 8.2995 1.841 -5.3011 0.0433 27.2703 3 2.4 7.6043 1.771 -4.9644 0.0433 27.2703 4 2.3 7.2346 1.549 -3.8011 0.044 27.131 5 2.3 7.2346 1.519 -3.6312 0.0466 26.6323 6 2.1 6.4444 1.488 -3.4521 0.0416 27.6182 7 2 6.0206 1.706 -4.6396 0.0383 28.3361 8 2.2 6.8485 1.656 -4.3813 0.044 27.131 9 2.1 6.4444 1.516 -3.614 0.0433 27.2703 10 2.2 6.8485 1.681 -4.5114 0.0423 27.4732 11 2.5 7.9589 2.923 -9.3166 0.0393 28.1122 12 2.3 7.2346 1.903 -5.5888 0.0423 27.4732 13 2.2 6.8485 1.683 -4.5217 0.0433 27.2703 14 2.1 6.4444 1.69 -4.5578 0.04 27.9589 15 2.2 6.8485 1.665 -4.4283 0.04 27.9589 16 2.4 7.6043 1.718 -4.7005 0.0417 27.5973 17 2.3 7.2346 1.683 -4.5217 0.045 26.9358 18 2.3 7.2346 1.6 -4.0824 0.04 27.9589 19 2.5 7.9589 1.911 -5.6253 0.0443 27.072 20 2.6 8.2995 1.846 -5.3247 0.045 26.9358 21 2.7 8.6273 1.731 -4.766 0.044 27.131 22 2.6 8.2995 1.763 -4.9251 0.045 26.9358 23 2.5 7.9589 1.88 -5.4832 0.04 27.9589 24 2.4 7.6043 1.559 -3.857 0.0426 27.4119 25 2.6 8.2995 1.81 -5.1536 0.044 27.131 3.1Singleresponseoptimization The optimal settings and the predicted optimal values for MRR, SR and KW are determined individually by Taguchi’s approach. Then, overall mean for S/N ratios MRR, SR and KW are calculated as average of all treatment responses for each level (Table-5, 6 and 7). The graphical representation of the effect of the six control factors on MRR, SR and KW is shown in Figure 1, 2 and 3 respectively. Table-5: Response Table for S/N ratios (larger the better) for MRR LEVEL TON TOFF WF WT UF LF 1 7.8 7.491 7.433 7.551 7.852 7.713 2 6.521 7.418 7.441 7.647 7.502 7.704 3 7.067 7.299 7.497 7.205 7.36 7.655 4 7.666 7.137 7.444 7.193 7.425 7.447 5 7.506 7.278 7.66 7.053 7.15 8.158 1.637 0.715 0.224 0.52 0.602 0.553 DELTA 1 2 6 5 3 4 RANK 62
  7. 7. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 5, Issue 1, January (2014), © IAEME Table-6: Response Table for S/N ratios (smaller the better) for SR LEVEL TON TOFF WF WT UF LF 1 -5.14 -6.048 -5.216 -5.603 -5.038 -5.589 2 -4.12 -4.995 -4.858 -4.578 -5.494 -4.866 3 -5.683 -4.687 -5.62 -4.53 -5.091 -4.526 4 -4.851 -4.291 -4.725 -4.343 -4.405 -5.527 5 -4.837 -4.61 -4.211 -5.576 -4.602 -4.122 DELTA 1.563 1.757 1.41 1.259 1.089 1.467 RANK 2 1 4 5 6 3 Table-7: Response Table for S/N ratios (smaller the better) for KW LEVEL TON TOFF WF WT UF LF 1 27.18 27.62 27.59 27.45 27.41 27.49 2 27.57 27.39 27.58 27.4 27.55 27.29 3 27.75 27.52 27.61 27.38 27.28 27.41 4 27.3 27.37 27.29 27.7 27.18 27.52 5 27.31 27.23 27.05 27.18 27.7 27.42 DELTA 0.57 0.39 0.57 0.53 0.52 0.24 RANK 1 5 2 3 4 6 Figure 1: Graphs for MRR 63
  8. 8. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 5, Issue 1, January (2014), © IAEME Figure 2: Graphs for SR Figure 3: Graphs for KW 3.2MULTI - RESPONSE OPTIMIZATION The optimal combination of process parameters for simultaneous optimization of material removal rate (MRR), Surface roughness (SR) and kerf width (KW) is obtained by the mean values of the multi-response S/N ratio of the overall utility value. Table 8 shows the values of S/N ratio for the individual response and the S/N ratio for the overall utility. 64
  9. 9. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 5, Issue 1, January (2014), © IAEME Table 8: L25 OA with multi-response S/N ratios EXP. NO η1 for MRR η2 for SR η3 for KW η OBSERVED 1 7.4361 8.6273 -8.0039 27.6182 2 8.0135 8.2995 -5.3011 27.2703 3 7.7669 7.6043 -4.9644 27.2703 4 7.9032 7.2346 -3.8011 27.131 5 7.8544 7.2346 -3.6312 26.6323 6 7.7102 6.4444 -3.4521 27.6182 7 7.2856 6.0206 -4.6396 28.3361 8 7.5361 6.8485 -4.3813 27.131 9 7.5921 6.4444 -3.614 27.2703 10 7.5655 6.8485 -4.5114 27.4732 11 6.8069 7.9589 -9.3166 28.1122 12 7.4353 7.2346 -5.5888 27.4732 13 7.5218 6.8485 -4.5217 27.2703 14 7.4466 6.4444 -4.5578 27.9589 15 7.6875 6.8485 -4.4283 27.9589 16 7.9115 7.6043 -4.7005 27.5973 17 7.648 7.2346 -4.5217 26.9358 18 7.9844 7.2346 -4.0824 27.9589 19 7.7063 7.9589 -5.6253 27.072 20 7.9395 8.2995 -5.3247 26.9358 21 8.3101 8.6273 -4.766 27.131 22 8.0594 8.2995 -4.9251 26.9358 23 7.9263 7.9589 -5.4832 27.9589 24 8.1274 7.6043 -3.857 27.4119 25 8.0299 8.2995 -5.1536 27.131 Table 9: Response table for multi objective optimization LEVEL TON TOFF WF WT UF LF 1 7.7948 7.635 7.687 7.5959 7.6984 7.6659 2 7.5379 7.6884 7.6854 7.8492 7.8279 7.8088 3 7.3796 7.7471 7.7568 7.8035 7.5853 7.7203 4 7.8379 7.7551 7.8268 7.57 7.7632 7.8342 5 8.0906 7.8154 7.6849 7.8223 7.78528 7.5926 4. DISCUSSION The purpose of the experimentation is to identify the factors which have strong effects on the machining performance. From mean of S/N ratios (Table 5) for MRR, it is found that pulse-on time has highest rank ‘1’. Therefore, it has most significant effect on MRR. The wire feed has least effect on MRR. The order of other influencing parameters for MRR: pulse-off time, upper flush, lower flush and wire tension. Also, from mean of S/N ratios (Table 6) for SR, it is observed that, the Pulse 65
  10. 10. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 5, Issue 1, January (2014), © IAEME off time has highest rank ‘1’ and therefore, it affects SR significantly. The Upper flush has least effect on SR. The order of other influencing parameters for SR is: Pulse on time, Lower Flush, Wire Feed, Wire tension. Table 7 shows that, for KW, the pulse-on time has highest rank ‘1’ and hence, it affects KW of the machined surface most significantly. The Lower flush has least effect on KW. The order of other influencing parameters of KW is: wire feed, wire tension, upper flush, and pulse-off time. From Table 5, the optimal combination of process parameters for maximum MRR is found to be: A5B1C2D1E3F2. The symbols A, B, C, D, E and F represents process parameters: Ton, Toff, WF, WT, UF and LF respectively and numbers represents the levels.This means, to have maximum MRR, Ton should be set on level 5, Toff on 1, WF on 2,WT on 1, UF on 3 and LF on 2. Similarly from Table 6, it is observed that, the optimalcombination of process parameters for minimum SR is: A2B4C5D4E4F5. This means,to have minimum SR, Ton should be set on level 2, Toff on 4, WF on 5, WT on4, UFon 4 and LF on 5. From Table 7, the optimalcombination of process parameters for KW is:A3B1C3D4E5F4. This means to have low KW Ton should be set on level 3, Toff on 1, WF on 3, WT on 4, UF on 5 and LF on 4. From table 6 optimal combination of process parameter for simultaneous optimization to obtain maximum MRR, minimum SR and minimum kerf width is found to be A5B5C4D2E2F4. The symbols A, B, C, D, E and F represents process parameters: Ton, Toff, WF, WT, UF and LF respectively and numbers represents their respective levels. 5. CONCLUSIONS Present work is concerned with determining the optimum settings of process parameters for single as well as multi response optimization during EDMing of high carbon high chromium steel on the basis of taguchi approach and utility concept. The L25 OA was used for experimental planning. In the first stage (single response) optimal settings of process parameters were obtained individually so as to obtain optimum values for MRR, SR and KW respectively. It is found that TON is the most influencing factor for both KW and MRR, while TOFF has significant effect on SR. In second stage (multi response) response table establishes the combination of higher levels of pulse on time, pulse off time, wire feed and lower flush and lower level of wire tension and upper flush is essential for obtaining optimal value of multiple performance for the predefined weightages. 6. REFERANCES [1] [2] [3] [4] Kaladhar M., Subbaiah K. V., Ch. SrinivasaRao and NarayanaRao K., ‘Application of Taguchi approach and Utility Concept in solving the Multi-objective Problem when turning AISI 202 Austenitic Stainless Steel’, Journal of Engineering Science and Technology Review 4 (1) (2011) 55-61. Amar Patnaik., Mahapatra S.S., ‘Parametric Optimization of Wire Electrical Discharge Machining (WEDM) Process using Taguchi Method’, Journal of Brazil society of mech. Sci. &Eng, Vol. 28, No. 4,December 2006, PP 422-429. Bhaskar Reddy C., Sivakiran S., Eswarareddy C., ‘Effect Of Process Parameters On Mrr In Wire Electrical Discharge Machining Of En31 Steel’, International Journal of Engineering Research and Applications (IJERA), Vol. 2, Issue 6, November- December 2012, pp.12211226. Atul Kumar, D. K. Singh, ‘Strategic Optimization and Investigation Effect of Process Parameters On Performance Of Wire Electric Discharge Machine (WEDM)’, International Journal of Engineering Science and Technology (IJEST). 66
  11. 11. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 5, Issue 1, January (2014), © IAEME [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] Manoj Malik, Rakesh Kumar Yadav, Nitesh Kumar, Deepak Sharma, Manoj, ‘Optimization of process parameters of wire EDM using zinc-coated brass wire’, International Journal of Advanced Technology & Engineering Research (IJATER). Konda R., Rajurkar K. P., Bishu R. R. , Guha A. and Parson M., ‘Design of experiments to study and optimize process performance’, Int. J. Quality, Reliability and Management, 16 (1) (1999) 56–71. Parashar Vishal, A. Rehaman, J. L. Bhagoria, Y.M. Puri, ‘Kerf width analysis for wire cut electro discharge machining of SS304L using design of experiments’, Indian Journal of Science and Technolgy, vol. 3 No. 4, 2010, 369-373. PujariUjariSrinivasaRao, Dr.KoonaRamji , Prof. BeelaSatyanarayana, ‘Prediction of Material removal rate for Aluminium BIS-24345 Alloy in wire-cut EDM’, International Journal of Engineering Science and Technology Vol. 2 (12), 2010, P.N. 7729-7739. Abbas Al-Refaie, Tai-Hsi Wu, Ming-Hsien Li, ‘An Effective Approach for Solving The Multi-Response Problem in Taguchi Method’, Jordan Journal of Mechanical and Industrial Engineering, Volume 2010, Pages 314 – 323. Farhad Kolahan, and Mohammad Bironro, ‘Modelling and Optimization of Process Parameters in PMEDM by Genetic Algorithm’, World Academy of Science, Engineering and Technology 48 2008. Yigit Kazancoglu, UgurEsme, MelihBayramoglu, OnurGuven, SuedaOzgun, ‘Multi objective optimization of the cutting forces in turning operations using the Grey-based Taguchi method’, Original scientific article. Maria Cristina Recchioni, ‘A Multiobjective Optimization Algorithm to Solve Nonlinear Systems’, Applied Mathematical Sciences, Vol. 6, 2012, no. 129, 6403 – 6423. MinghuiGao, LianfenHuang , ‘Intelligent Coverage Optimization with Multi- Objective Genetic Algorithm in Cellular System’ Gerald Weigert, Sebastian Werner, Dirk Hampel, Hendrikje Heinrich, and Wilfried Sauer, ‘Multi objective decision making – solutions for the optimization of manufacturing processes’. Gadakh V.S., ' Parametric optimization of Wire Electrical DiScharge machining usisng TOPSIS meyhod', Advances in production engineering and management 7 (2012) 3, 157-164. Prof. S.R.Nipanikar, ‘Parameter optimization of electro discharge machining of AISI D3 steel material by using Taguchi method’, Journal of Engineering Research and Studies. Sarkar S., Mitra S. and Bhattacharyya B., ‘Parametric analysis and optimization of wire electrical discharge machining of titanium aluminide alloy’, Journal of Materials Processing Technology 159 (2005) 286-294. Y.S.Sable, R.B.Patil and Dr.M.S.Kadam, “Investigation of MRR in WEDM for Wc-Co Sintered Composite”, International Journal of Mechanical Engineering & Technology (IJMET), Volume 4, Issue 3, 2013, pp. 349 - 358, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359. Brij Bhushan Tyagi, Mohd.Parvez, Rupesh Chalisgaonkar and Nitin Sharma, “Optimization of Process Parameters of Wire Electrical Discharge Machining of AISI 316L”, International Journal of Mechanical Engineering & Technology (IJMET), Volume 3, Issue 2, 2012, pp. 317 - 327, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359. S V Subrahmanyam and M. M. M. Sarcar, “Parametric Optimization for Cutting Speed – A Statistical Regression Modeling for WEDM”, International Journal of Advanced Research in Engineering & Technology (IJARET), Volume 4, Issue 1, 2013, pp. 142 - 150, ISSN Print: 0976-6480, ISSN Online: 0976-6499. 67

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