Optimization of Machining Process Parameters in Drilling of

Optimization of Machining Process Parameters in Drilling of CFRP Using Multi-Objective Taguchi Technique, TOPSIS and RSA Optimization of Machining Process Parameters in Drilling of CFRP Using Multi-Objective Taguchi Technique, TOPSIS and RSA Shunmugesh K. and Panneerselvam K. * Department of Production Engineering, NIT, Trichy Summary Carbon Fiber Reinforced Polymer (CFRP) are widely used in many engineering applications as replacement for various other elements to make use of the advantage of its high strength-weight ratio, durability and high corrosion resistance. The paper herein is an attempt to evaluate the drilling characteristics of CFRP by means of three different drill bit types (HSS, TiAlN and TiN) using Taguchi L 27 (3 13 ) orthogonal array under dry condition. Firstly, the machining process parameters (cutting speed and feed rate) are optimized with multiple performance characteristics using Multi-objective Taguchi technique and TOPSIS. Secondly, mathematical model is developed to correlate the machining process parameters and the performance characteristics (surface roughness, circularity and cylindricity) using response surface analysis. ANOVA is used to validate the developed mathematical model of the responses. The investigation reveals that the results of TOPSIS technique are in good agreement with the multi-objective Taguchi technique and also feed rate is the most predominant factor which affects the responses. Keywords: Surface roughness, Circularity, Cylindricity, TOPSIS, Multi-objective Taguchi technique 1. Introduction Fiber Reinforced Plastic (FRP) composites materials are now-a-days used for a spread of advanced and complicated applications in fashionable business. Typical materials are replaced by FRPs particularly Carbon Fiber Reinforced Polymer (CFRP) sort during a range of economic, domestic and engineering applications as another material for various elements to require advantage of its high strength-weight quantitative relation, sturdiness and high corrosion resistance. The fabric is well-suited for craft hulls and wings. The fabric behaviour depends on the fiber reinforcement content, fiber orientation, and kind of organic compound used. The drilling method is most typically employed in the engineering applications to suit the composite materials to different structural members of the system. *Email : kps@nitt.ed Smithers Information Ltd., 2017 Ozden et al. 1 investigated the holemaking method in CFRP using multilayered tungsten carbide tool and generated data on the result of feed rate and cutting speed on delamination damage and surface roughness. They suggest low feed rate and high cutting speed for optimum results in drilling CFRP. Krishnaraj et al. 2 conducted high speed drilling experiments on CFRP laminates by using K20 carbide drill bit and used multi-objective improvement to ascertain optimum machining conditions. They reported that the circularity of the drilled holes is affected by the spindle speed and reduced exit delamination damage with low feed rates. Palanikumar 3 studied the result of spindle speed and feed rate on the surface roughness, force and delamination issue using GRA. The result discovered that the feed rate is the most influencing factor which affects the response surface than the cutting speed. Peng et al. 4 have done an intensive analysis on machining technology using TOPSIS and developed a prototype system. Gok 5 developed a new technique to optimize the responses using TOPSIS coupled with fuzzy and grey relational analysis coupled with response surface analysis. Sonkar 6 optimized the machining process parameters with multiple performance characteristics using TOPSIS and Deng s solution. Salmasnia et al. 7 proposed a novel approach to optimize the process parameter with multiple performance characteristics based on Neuro-fuzzy technique linked with the desirability analysis. Abhishek et al. 8 investigated the influence of process parameter on drilling of CFRP using fuzzy logic coupled with the harmony search algorithm. Krishnamoorthy et al. 9 optimized the machining parameters with multiple performance characteristics using grey relational analysis linked with the fuzzy logic. Polymers & Polymer Composites, Vol. 25, No. 3, 2017 185

Shunmugesh K. and Panneerselvam K 2. Design of Experiments 2.1 Materials and methods In this paper CFRP manufactured by hand layup and auto clave was chosen as the work piece material for conducting drilling experiments. The work piece used for the experiments is CFRP (T300 Bi-directional carbon fibre/epoxy matrix with a volume fraction of 60%). The carbon fiber used in the material is of PAN-based and the average thickness of the fabric is 0.25 mm. The size of the specimen used was 150 x 15 x 8 mm. The experiments were carried out using BFW Ltd BMV 40T20 CNC vertical milling machine. The cutting tools used for the dry drilling are HSS drill (Miranda Tools India Ltd), Kennametal Solid Carbide Drill (TiAlN Black Coated-KC7325 Grade) and WIDIA Solid Carbide Drill (TiN Golden coated - WU25PD Grade). The drilling tools of 6 mm diameter and 2 flutes are used for all the experimental trials. The surface roughness value Ra was measured by using Mitutoyo SJ- 210 surface roughness tester and for better results, average of three replicates was found out. Circularity and cylindricity are measured using TESA micro-hite 3D CMM having an accuracy of 1 micron and repeatability of 1 micrometer. The schematic experimental setup is presented in Figure 1. Table 1. Machining parameters and their levels Process parameters Symbol Levels 1 2 3 Cutting speed, m/min v 30 40 50 Feed rate, mm/rev f 0.025 0.05 0.1 Drill bit type d HSS TiAlN TiN 2.3 Response Surface Analysis The method of least squares is mainly used for the regression analysis and to calculate the regression coefficients and the true functional relationship between Yxt and the set of process parameters in a multiple linear regression model. If the response variables are well modelled by a linear function of the input process variables, and the approximating function in the first order is given by: Polynomial of higher degree must be used, Such as the second order mode: Almost all the engineering problem use one of these models for their application. But, it is unlikely that the polynomial model will give accurate results for the true functional relationship over the entire space of the input process variables, but for a relatively small region they usually works quite well. Figure 1. Experimental setup and evaluation (1) (2) 2.2 Machining Parameters Taguchi s philosophy is principally explored for planning the process to research the consequences of the whole machining parameters through restricted variety of experimental runs. Taguchi s orthogonal array style of experiment is economic in addition as less time intense. During this study, 3 controllable method parameters: cutting speed, feed rate and drill bit type are elite and varied in 3 completely different levels as shown in Table 1. Within the gift work, the experiments were performed victimization Taguchi s L27 (3 13 ) orthogonal array. 186 Polymers & Polymer Composites, Vol. 25, No. 3, 2017

Optimization of Machining Process Parameters in Drilling of CFRP Using Multi-Objective Taguchi Technique, TOPSIS and RSA 2.4 Multi-objective Taguchi Membership Function (MOTMF) The objective of multi-objective Taguchi Membership function is to convert the multiple performance characteristics into a single objective. The steps involved in the multiobjective Taguchi Membership function are shown in the Figure 2. 2.5 TOPSIS Method The multiple performance characteristics are converted into a single performance characteristic by means of TOPSIS method. The steps involved in the TOPSIS are shown in the Figure 3. > 13 > 27 > 26 > 18 > 1 > 17 > 5 > 6 > 25 > 16 > 4 > 8 > 9 > 7. The higher value of proximity coefficient value indicates better performance. From the Table 3, it is evident that the experiment number 20 has attained the maximum value of proximity coefficient among the 27 number of experiment and the optimum condition to achieve the multiple performance characteristics (S/N ratio = 48.074, cutting speed = 50 m/min, feed rate = 0.025 mm/rev and TiAlN drill bit type). Thus, the results of TOPSIS technique are in conformity with the multi-objective Taguchi technique. 3.3 Effects of Machining Parameters The effects of machining parameters (cutting speed and feed rate) on surface roughness, circularity and cylindricity while drilling the CFRP is shown in Figure 4. The quality of drilled holes is reflected by its surface roughness value. The magnitude of surface roughness is controlled by the thrust force value and it is confirmed from the figure that the surface roughness value varies inversely proportional to cutting speed and proportional to feed rate for all the three drill bit type. Similar trend of results are obtained for circularity Figure 2. Flowchart of multi-objective Taguchi membership function 3. Result and discussions 3.1 MOTMF The aim of MOTMF is to covert the multi-objective function (MOF) into single objective function. Using the single objective function, the effect of process parameters on drilling of CFRP was analyzed. The higher value of S/N ratio indicates better performance. From the Table 2, it is evident that the experiment number 20 has attained the maximum value of S/N ratio among the 27 number of experiment and the optimum condition to achieve the multiple performance characteristics (S/N ratio = 48.074, cutting speed = 50 m/min, feed rate = 0.025 mm/rev and TiAlN drill bit type). Figure 3. Flowchart of TOPSIS 3.2 TOPSIS Study Initially the three experimental results of surface roughness, circularity and cylindricity are normalized and assigned equal weights. From the Table 3, it is evident that the experiment number 20 was the better performer. The order of the experimental run obtained by TOPSIS was given by 20 > 11 > 21 > 12 > 19 > 10 > 23 > 24 > 14 > 22 > 2 > 15 > 3 Polymers & Polymer Composites, Vol. 25, No. 3, 2017 187

Shunmugesh K. and Panneerselvam K Table 2. Experimental design, responses, normalized data, MOF and S/N ratio Exp No Control factor Responses Normalized data MOF S/N Ratio v f d R a (µm) (µm) (µm) R a 1 30 0.025 HSS 4.971 0.012 0.036 0.574 0.590 0.664 0.588 4.607 2 30 0.025 TiAlN 4.76 0.011 0.033 0.454 0.500 0.541 0.394 8.090 3 30 0.025 TiN 4.8 0.013 0.037 0.477 0.680 0.705 0.624 4.093 4 30 0.05 HSS 5.32 0.014 0.039 0.773 0.770 0.786 0.952 0.431 5 30 0.05 TiAlN 5 0.013 0.037 0.590 0.680 0.705 0.688 3.247 6 30 0.05 TiN 5.09 0.015 0.04 0.642 0.860 0.827 0.966 0.299 7 30 0.1 HSS 5.632 0.015 0.038 0.950 0.860 0.745 1.157-1.265 8 30 0.1 TiAlN 5.4 0.014 0.037 0.818 0.770 0.705 0.925 0.673 9 30 0.1 TiN 5.47 0.016 0.04 0.858 0.950 0.827 1.223-1.745 10 40 0.025 HSS 4.404 0.01 0.029 0.251 0.410 0.377 0.197 14.126 11 40 0.025 TiAlN 4.197 0.011 0.027 0.134 0.500 0.295 0.187 14.567 12 40 0.025 TiN 4.245 0.012 0.029 0.161 0.590 0.377 0.272 11.316 13 40 0.05 HSS 4.83 0.013 0.035 0.494 0.680 0.623 0.576 4.795 14 40 0.05 TiAlN 4.647 0.012 0.032 0.390 0.590 0.500 0.395 8.075 15 40 0.05 TiN 4.771 0.014 0.034 0.460 0.770 0.582 0.602 4.413 16 40 0.1 HSS 5.125 0.014 0.042 0.662 0.770 0.909 0.977 0.199 17 40 0.1 TiAlN 4.99 0.012 0.04 0.585 0.590 0.827 0.723 2.813 18 40 0.1 TiN 4.95 0.016 0.043 0.562 0.950 0.950 1.116-0.955 19 50 0.025 HSS 4.302 0.008 0.024 0.193 0.230 0.173 0.063 23.982 20 50 0.025 TiAlN 4.05 0.006 0.021 0.050 0.050 0.050 0.004 48.074 21 50 0.025 TiN 4.24 0.01 0.025 0.158 0.410 0.214 0.126 18.017 22 50 0.05 HSS 4.74 0.008 0.034 0.443 0.230 0.582 0.309 10.199 23 50 0.05 TiAlN 4.538 0.007 0.031 0.328 0.140 0.459 0.178 15.004 24 50 0.05 TiN 4.58 0.009 0.032 0.352 0.320 0.500 0.251 12.024 25 50 0.1 HSS 5.11 0.01 0.04 0.653 0.410 0.827 0.673 3.438 26 50 0.1 TiAlN 4.9 0.009 0.038 0.534 0.320 0.745 0.496 6.087 27 50 0.1 TiN 4.87 0.011 0.042 0.516 0.500 0.909 0.707 3.012 Table 3. TOPSIS Normalized, separation measures and closeness coefficient value Exp No Normalized data Weighted normalized data Separation measures Closeness coefficient R a R a S + I S I C 1 0.436 0.021 0.037 0.148 0.007 0.012 0.02817 0.02 0.41522 2 0.418 0.020 0.034 0.142 0.006 0.011 0.02177 0.0264 0.54804 3 0.421 0.023 0.038 0.143 0.008 0.013 0.02339 0.02496 0.51624 4 0.467 0.025 0.040 0.159 0.008 0.013 0.03866 0.00948 0.1969 5 0.439 0.023 0.038 0.149 0.008 0.013 0.02915 0.01904 0.39515 6 0.447 0.027 0.041 0.152 0.009 0.014 0.03213 0.01621 0.33533 7 0.494 0.027 0.039 0.168 0.009 0.013 0.04784 0.0018 0.03636 8 0.474 0.025 0.038 0.161 0.008 0.013 0.04091 0.00731 0.15164 9 0.480 0.029 0.041 0.163 0.009 0.014 0.04325 0.00494 0.1025 10 0.386 0.018 0.030 0.131 0.006 0.010 0.01116 0.03711 0.76883 11 0.368 0.020 0.028 0.125 0.006 0.009 0.00566 0.04325 0.88424 12 0.372 0.021 0.030 0.127 0.007 0.010 0.00733 0.04171 0.85054 13 0.424 0.023 0.036 0.144 0.008 0.012 0.02411 0.02414 0.50038 188 Polymers & Polymer Composites, Vol. 25, No. 3, 2017

Optimization of Machining Process Parameters in Drilling of CFRP Using Multi-Objective Taguchi Technique, TOPSIS and RSA Table 3. Cont'd... Exp No Normalized data Weighted normalized data Separation measures Closeness coefficient R a R a S + I S I C 14 0.408 0.021 0.033 0.139 0.007 0.011 0.01854 0.02971 0.6158 15 0.419 0.025 0.035 0.142 0.008 0.012 0.02246 0.02589 0.53552 16 0.450 0.025 0.043 0.153 0.008 0.014 0.03319 0.01517 0.31371 17 0.438 0.021 0.041 0.149 0.007 0.014 0.02899 0.01932 0.39989 18 0.434 0.029 0.044 0.148 0.009 0.015 0.02849 0.02034 0.41659 19 0.377 0.014 0.025 0.128 0.005 0.008 0.00768 0.04047 0.84056 20 0.355 0.011 0.022 0.121 0.004 0.007 0 0.04814 1 21 0.372 0.018 0.026 0.126 0.006 0.009 0.00629 0.04212 0.87014 22 0.416 0.014 0.035 0.141 0.005 0.012 0.02109 0.02719 0.56324 23 0.398 0.012 0.032 0.135 0.004 0.011 0.01496 0.03331 0.69005 24 0.402 0.016 0.033 0.137 0.005 0.011 0.01634 0.03187 0.66101 25 0.448 0.018 0.041 0.152 0.006 0.014 0.03236 0.016 0.33081 26 0.430 0.016 0.039 0.146 0.005 0.013 0.02607 0.02228 0.46086 27 0.427 0.020 0.043 0.145 0.006 0.014 0.02566 0.02292 0.47184 Figure 4. Main effect plot of (a) R a (b) and (c) Polymers & Polymer Composites, Vol. 25, No. 3, 2017 189

Shunmugesh K. and Panneerselvam K and cylindricity also, during drilling of CFRP along the transverse and longitudinal direction of the material. 3.4 ANOVA The adequacy of the model was validated by ANOVA and the results are shown in Tables 4-6. The response surface methodology (RSM) performed by the statistical software (MINITAB 16) was used for mathematical modelling of the experimental results. The second order regression model for surface roughness, circularity and cylindricity were obtained and are represented by Eqs. (3-11) as mentioned in Table 7. The predicted values of R a, and were obtained from Eqs. (6-8), and compared with experimental results. It is clear and evident that the predicted values are in good agreement with the experimental results as shown in Figure 5. 4. Conclusions 1. From the multi-objective Taguchi technique, the most predominant process parameter was found to be feed rate, followed by cutting speed and drill bit type respectively. 2. Minimum surface roughness (R a = 4.05 µm), minimum circularity error ( = 0.006 µm) and minimum cylindricity error ( = 0.021 µm) was obtained from the optimum combination of process parameters (v3f1d2). 3. The results of TOPSIS technique are in good agreement with the multi-objective Taguchi technique. 4. From the results of ANOVA feed rate is found to be the most predominant factor which affects the responses. References 1. Isbilir O. & Elaheh G. Journal of Reinforced Plastics and Composites., 31, (2012), 717-727. 2. Krishnaraj et al. Composites Part B: Engineering., 43, (2012), 1791-1799. Table 4. Analysis of variance for surface roughness, using adjusted SS for tests Tools Source DF Seq SS Adj SS Adj MS F C (%) HSS V 2 0.62504 0.62504 0.31252 200.73 43.59 f 2 0.80244 0.80244 0.40122 257.70 55.98 Error 4 0.00623 0.00623 0.00156 0.43 Total 8 1.43371 100 S = 0.0394581 R-Sq = 99.57% R-Sq(adj) = 99.13% TiAlN V 2 0.51929 0.51929 0.25964 51.12 36.87 f 2 0.86898 0.86898 0.43449 85.54 61.69 Error 4 0.02032 0.02032 0.00508 1.44 Total 8 1.40858 100 S = 0.0712702 R-Sq = 98.56% R-Sq(adj) = 97.12% TiN V 2 0.54275 0.54275 0.27137 75.71 44.08 f 2 0.67427 0.67427 0.33713 94.06 54.76 Error 4 0.01434 0.01434 0.00358 1.16 Total 8 1.23135 100 S = 0.0598688 R-Sq = 98.84% R-Sq(adj) = 97.67% Table 5. Analysis of variance for circularity, using adjusted SS for tests Tools Source DF Seq SS Adj SS Adj MS F C (%) HSS V 2 0.0000169 0.0000169 0.0000084 19.00 28.69 f 2 0.0000402 0.0000402 0.0000201 45.25 68.25 Error 4 0.0000018 0.0000018 0.0000004 3.06 Total 8 0.0000589 100 S = 0.000666667 R-Sq = 96.98% R-Sq(adj) = 93.96% TiAlN Source DF Seq SS Adj SS Adj MS F C (%) V 2 0.0000096 0.0000096 0.0000048 17.20 27.51 f 2 0.0000242 0.0000242 0.0000121 43.60 69.34 Error 4 0.0000011 0.0000011 0.0000003 3.15 S = 0.000527046 R-Sq = 96.82% R-Sq(adj) = 93.63% TiN V 2 0.0000127 0.0000127 0.0000063 19.00 45.36 f 2 0.0000140 0.0000140 0.0000070 21.00 50.00 Error 4 0.0000013 0.0000013 0.0000003 4.64 Total 8 0.0000280 100 S = 0.000577350 R-Sq = 95.24% R-Sq(adj) = 90.48% Table 6. Analysis of variance for cylindricity, using adjusted SS for tests Tools Source DF Seq SS Adj SS Adj MS F C (%) HSS V 2 0.0000549 0.0000549 0.0000274 12.05 22.23 f 2 0.0001829 0.0001829 0.0000914 40.15 74.08 Error 4 0.0000091 0.0000091 0.0000023 3.69 Total 8 0.0002469 100 S = 0.00150923 R-Sq = 96.31% R-Sq(adj) = 92.62% TiAlN Source DF Seq SS Adj SS Adj MS F C (%) V 2 0.0000676 0.0000676 0.0000338 9.81 23.46 f 2 0.0002069 0.0002069 0.0001034 30.03 71.80 Error 4 0.0000138 0.0000138 0.0000034 4.74 Total 8 0.0002882 100 S = 0.00185592 R-Sq = 95.22% R-Sq(adj) = 90.44% TiN V 2 0.0000762 0.0000762 0.0000381 11.06 25.90 f 2 0.0002042 0.0002042 0.0001021 29.65 69.40 Error 4 0.0000138 0.0000138 0.0000034 4.70 Total 8 0.0002942 100 S = 0.00185592 R-Sq = 95.32% R-Sq(adj) = 90.63% 190 Polymers & Polymer Composites, Vol. 25, No. 3, 2017

Optimization of Machining Process Parameters in Drilling of CFRP Using Multi-Objective Taguchi Technique, TOPSIS and RSA Table 7. Mathematical model for surface roughness, circularity and cylindricity Drill Bit Type Regression Equation for Performance Characteristics (Ra, Ci and Cy) R 2 Value Eq. No HSS 99.57% 3 96.98% 4 96.31% 5 TiAlN 98.56% 6 96.82% 7 95.22% 8 TiN 98.84% 9 95.24% 10 95.32% 11 Figure 5. Comparison of actual and predicted values (a) R a (b) and (c) 3. Palanikumar, K. Measurement., 44, (2011), 2138-2148. 4. Peng C., Hanheng D. & Warren L. Robotics and Computer-Integrated Manufacturing (2015) (In-press). 5. Gok A. Measurement., 70, (2015), 100-109. 6. Sonkar V., Kumar A., Saurav D. & Siba S. Procedia Materials Science., 6, (2014), 538-543. 7. Salmasnia A. & Mohammad M. Neurocomputing., 91, (2012), 56-66. 8. Abhishek K., Saurav D. & Siba S.M. Measurement., 77, (2016), 222-239. 9. Krishnamoorthy A., Rajendra B S., Palanikumar K. & Davim P. Measurement., 45, (2012), 1286-1296. 10. Montgomery DC. Design and analysis of experiments, John Wiley & Sons, (2008). Polymers & Polymer Composites, Vol. 25, No. 3, 2017 191

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