Optimization of machining parameters in edm of cfrp composite using taguchi technique

Optimization of machining parameters in edm of cfrp composite using taguchi technique

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  INTERNATIONAL JOURNAL OFMECHANICAL ENGINEERING International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online), Volume 5, Issue 10, October (2014), pp. 70-77 © IAEME AND TECHNOLOGY (IJMET) ISSN 0976 – 6340 (Print) ISSN 0976 – 6359 (Online) Volume 5, Issue 10, October (2014), pp. 70-77 © IAEME:
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  Journal Impact Factor(2014): 7.5377 (Calculated by GISI) IJMET © I A E M E OPTIMIZATION OF MACHINING PARAMETERS IN EDM OF CFRP COMPOSITE USING TAGUCHI TECHNIQUE 70 Brajesh Kumar Lodhi1, Deepak Verma2, Rahul Shukla3 1, 3(Assistant professor, Department of Mechanical Engineering, Bundelkhand University, Jhansi-284128, INDIA) 2(Assistant professor, Department of Mechanical Engineering, S.R. Group of institution, Jhansi-284001, INDIA) ABSTRACT Electrical discharge machining (EDM) is a nontraditional manufacturing technique that has been widely used in the production of tools and dies throughout the world in recent years. The most important performance measure in EDM is the surface roughness. In this study, the effect and optimization of machining parameters on surface roughness in an EDM operation was investigated by using the Taguchi method. The experimental studies were conducted under varying gap voltage, discharge current, and pulse-on time. An orthogonal array, the signal-to-noise (S/N) ratio, and the analysis of variance (ANOVA) were employed to the study the surface roughness in the EDM of CFRP composite. It was observed that the discharge current was the most influential factors on the surface roughness. To validate the study, confirmation experiment has been carried out at optimum set of parameters and predicted results have been found to be in good agreement with experimental findings. Keywords: Analysis of Variance, EDM, Surface Roughness, Taguchi Method. 1. INTRODUCTION Electrical Discharge Machining is a non-traditional manufacturing process used in industry for high-precision machining of all types of conductive materials such as metals, metallic alloys, graphite, or even some ceramic materials, of whatever intensity of hardness. In EDM process material is removed by melting and vaporization of work material due to rapidly occurring electrical sparks within a dielectric medium [1]. Carbon fiber reinforced plastic is a highly potential composite material which is widely used in aerospace and aeronautical industries, automobile industries. Due to their high strength to weight ratio which is favorable for aerospace and aeronautical industries. CFRP posses honeycomb structure which provides a high stiffness to density ratio which makes it a very
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  International Journal ofMechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online), Volume 5, Issue 10, October (2014), pp. 70-77 © IAEME stiff material ten times more to steel. Due to these properties it is a difficult to machine material with conventional process. Also apart from these properties this material encountered some other problems like deboning, Delamination, presence of burrs during machining with conventional process [2]. So to eliminate these problems some nonconventional processes are used out of which EDM is known to be favorable method for machining these types of materials. In setting the machining parameters, the main goal is the minimum surface roughness. 71 Electric Discharge Machining (EDM) is an essential operation in several manufacturing in some industries, which gives importance to variety, precision and accuracy. Several researchers have attempted to improve the performance characteristics namely the surface roughness, Delamination factor, tool wear rate and material removal rate etc. but the full potential utilization of this process is not completely solved because of its complex and stochastic nature and more number of variables involved in this operation. George et al. optimize the machining parameters in the EDM machining of C–C composite using Taguchi method. The process variables affects electrode wear rate and MRR, according to their relative significance, are Vg, Ip and Ton, respectively [3]. Guu et al. reported in their work that to prevent Delamination defects around holes in composite the pulse energy should small and increase in discharge energy can cause increase in surface roughness due to increase in temperature [4]. Kanagarajan et al. studied the effect of process parameters such as pulse current, pulse on time, flussing pressure on tungsten carbide [5]. H. S. Lu et al. examined the optimal design of the cutting parameters such as milling type, radial depth of cut for rough cutting processes in high speed end milling on SKD61 tool steel by using GRA with PCA [6]. W.S. Lau et al. investigated the copper electrode prove to be better than graphite in terms of tool wear and surface finish and positive polarity should be used for machining carbon fiber composite material in order to achieve a low tool wear ratio [7]. M.K. Pradhan presented a hybrid approach for determination of the process parameters of EDM in AISI D2 tool steel [8]. Lin and Lin examined the performance characteristics of the EDM process such as material removal rate, surface roughness, and electrode wear ratio are improved together by using Gary relation analysis and Taguchi analysis [9]. Kiyak et al. [10] the experimental study of the EDM of 40CrMnNiMo864 tool steel (AISI P20) tool steel they Examine machining parameters on surface roughness in EDM of tool steel. Marafona Conclude that the black layer composition varies with an interaction of electrical discharge machining (EDM) input parameters, which affects the electrode wear ratio (EWR), using the Taguchi methodology [11]. The aim of present work is to obtain the optimum machining conditions for EDM of CFRP composite material, for minimizing the surface roughness based on Taguchi technique. Experiments were carried out to study the effect of various parameters viz. gap voltage, discharge current, and pulse-duration, on the surface finish. The levels of significance on the surface roughness were statistically evaluated by using analysis of variance (ANOVA). 2. EXPERIMENTAL SETUP The experimental studies were performed on a SMART ZNC EDM machine tool. The properties of Carbon fibre reinforced plastic work-piece material used for experimentation in this work is a given in Table 1. EDM oil 40 is used as a dielectric fluid during the operation which is a fully synthetic EDM fluid. Copper tool with 2 mm diameter (8930Kg/mm3) was used in the experiments. The machining parameters, selected for different settings of gap voltage, pulse on time and current were used in the experiments (Table 2). The photographic view of Experimental setup and machining zone has been shown Fig.1and 2. The surface roughness measured by with Talusurf-6 on the work-piece after machining.
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  International Journal ofMechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online), Volume 5, Issue 10, October (2014), pp. 70-77 © IAEME 72 Table 1: Properties of work piece material CFRP composites Work material Electric Resistivity Type of resin Density (Kg/m3) Volume fraction of carbon (%) Fiber Diameter (μm) CFRP 87×10-3 LY-556 1850 70 7.45 Fig. 1: Experimental setup Fig.2: Experimental Machining Zone
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  International Journal ofMechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online), Volume 5, Issue 10, October (2014), pp. 70-77 © IAEME 1 N − = N I= 73 Fig. 3: Machined work-piece CFRP Table 2: Machining parameters with their levels Sr. No. Parameters symbol Units Level 1 Level 2 Level 3 1 Gap Voltage (Vg) V 50 60 70 2 Pulse-on-time (Ton) μs 70 80 90 3 Current (Ip) A 2 4 6 2.1 Design of experiment based on Taguchi method To evaluate the effects of machining parameters of EDM process in terms of machining performance characteristics such as Surface Roughness a Taguchi method used here to model the EDM process. In this study, Taguchi method, a powerful tool for parameter design of performance characteristics, for the purpose of designing and improving the product quality [12]. In the Taguchi method, process parameters which influence the products are separated into two main groups: control factors and noise factors. The control factors are used to select the best conditions for stability in design or manufacturing process, whereas the noise factors denote all factors that cause variation. According to Taguchi based methodology, the characteristic that the smaller value indicates the better machining performance, such as Surface roughness is addressed as the-smaller-the-better type of problem. The S/N Ratio, i.e. , can be calculated as shown below: (1) 10log 1 2 yRa h Table 3: Experimental results of surface roughness using L9 orthogonal array Exp. No. Factor Assignment Surface roughness S/N ratio Vg Ton I (db) (V) (μs) (A) 1. 50 70 2 3.106 -9.8440 2. 50 80 4 3.658 -11.2649 3. 50 90 6 3.953 -11.9385 4. 60 70 4 3.200 -10.1030 5. 60 80 6 4.275 -12.6187 6. 60 90 2 3.820 -11.6413 7. 70 70 6 4.240 -12.5473 8. 70 80 2 3.540 -10.9801 9. 70 90 4 4.210 -12.4856
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  International Journal ofMechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online), Volume 5, Issue 10, October (2014), pp. 70-77 © IAEME Main Effects Plot (data means) for Means Vg Ton 74 3. RESULT AND DISCUSSION The experimental results are collected for surface roughness and 9 experiments were conducted using Taguchi (L9) experimental design methodology and there are two replicates for each experiment to obtain S/N values. In the present study all the designs, plots and analysis have been carried out using Minitab-14 statistical software. Lower amount of surface roughness show the high productivity of EDM process. Therefore, small the better are applied to calculate the S/N ratio of surface roughness respectively. Regardless of category of the performance characteristics, a greater value corresponds to a better performance. Therefore, optimal level with the greatest value. By applying Eq. (1) the values for each experiment of L9 (Table 4) was calculated Table (3). The optimal machining performance for SR was obtained as 50 V gap voltage (Level 1), 70 μs pulse-on time (Level 1) and 2 A current (Level 1) settings that give the minimum SR. Table 4: Response Table for Signal to Noise Ratio Level/Parameters Vg Ton Ip 1 -11.02 -10.83 -10.82 2 -11.45 -11.62 -11.28 3 -12.00 -12.02 -12.37 Delta 0.99 1.19 1.55 Rank 3 2 1 Mean of Means 50 60 70 4.2 4.0 3.8 3.6 3.4 70 80 90 Ip 2 4 6 4.2 4.0 3.8 3.6 3.4 Fig.4: Factor effects on mean data for Surface roughness (Ra) The main effect plot for means that with increase in gap voltage the surface roughness is increases similarly with increase in pulse on time and current the surface roughness is also increases from Fig. 4. This is because if current is increase the electron flow will increase and due to this the temperature will increase so if more electrons will strike to the surface more will be the temperature of the surface and more melting and evaporation of the surface will take place and this will lead to
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  International Journal ofMechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online), Volume 5, Issue 10, October (2014), pp. 70-77 © IAEME increase surface roughness similarly with increase in pulse on time the pulse duration will increase due to which the discharge energy increases which tends to more temperature on the surface due to which more cracks generation on the surface takes place. So with the lower parameters we can get small surface roughness or we can say good quality surface.
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  75 Theanalysis of variance was used to establish statistically significant machining parameters and percent contribution of these parameters on the SR. A better feel for the relative effect of the different machining parameters on the SR was obtained by decomposition of variances, which is called analysis of variance [12].The relative importance of the machining parameters with respect to the SR was investigated to determine more accurately the optimum combinations of the machining parameters by using ANOVA. The results of ANOVA for the machining outputs are presented in Tables 4. Statistically, F-test provides a decision at some confidence level as to whether these estimates are significantly different. Larger F-value indicates that the variation of the process parameter makes a big change on the performance characteristics. Table 5: Analysis of variance of mean data for surface roughness Source DOF Sum of square Variance F-ratio P (%) Vg 2 0.2708 0.1354 1.30 17.629 Ton 2 0.3538 0.1769 1.70 23.03 Ip 2 0.7034 0.3517 3.38 45.78 Error 2 0.2081 0.1041 13.57 Total 8 1.5362 At 95% confidence level According to F-test analysis, the significant parameters on the SR are peak current. Peak current is found to be the major factor affecting the SR (45.78%). The percent contributions of gap voltage and pulse-on time SR are 17.629 and 23.03%, respectively.
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  Fig. 5: PercentageContribution of Control factors for Ra 4.1 Confirmation experiment The confirmation experiment is performed by conducting a test using a specific combination of the factors and levels previously evaluated. The sample size of confirmation experiment is larger than the sample size of any specific trial in the previous factorial experiment.
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  International Journal ofMechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online), Volume 5, Issue 10, October (2014), pp. 70-77 © IAEME Optimal machining parameters Prediction Experiment Level Vg1, TON1, Ip1 Vg1, TON1, Ip1 S/N ratio for SR (db) -9.650 -9.891 76 The final step of the Taguchi’s parameter design after selecting the optimal parameters is to be predict any verify the improvement of the performance characteristics with the selected optimal machining parameters [15]. The predicted S/N ratio using the optimal levels of the machining parameters can be calculated with the help of following prediction equation:
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  Here, opt isthe predicted optimal S/N ratio, m is the total mean of the S/N ratios, j is the mean S/N ratio of at optimal levels and k is the number of main design parameters that affect the quality characteristics. The results of experimental confirmation using optimal machining parameters are shown in Tables 6. From the above observations, it can be interpreted that the obtained SR have reasonable accuracy for resulting model because an error of 2.427% for S/N ratio of SR is measured. Table 6: Confirmation experiment result for SR 4. CONCLUSION This paper described the optimization of the EDM process using parametric design of Taguchi methodology. It was observed that the Taguchi’s parameter design is a simple, systematic, reliable, and more efficient tool for optimization of the machining parameters. • The effect of various machining parameter such as gap voltage, pulse-on time and peak current has been studied though machining of CFRP Composites material. It was identified that the pulse on time and current have influenced more than the other parameters considered in this study. • Based on the experimental results, we can conclude that the optimal conditions for smaller surface roughness is Vg = 50 V, Ton = 70μm, Ip = 2A. We can get minimum surface roughness on these lower parameters. • The selection of optimum values is essential for the process automation and implementation of a computer integrated manufacturing system. • The confirmation experiment has been conducted. Result shows that the error associated with SR is only 2.427 %. 5. ACKNOWLEDGEMENTS The author would like to thank MSME – Development Institute, Kanpur, India and Banaras Hindu University, Varanasi, India for providing their facilities to carry out the research work.
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  International Journal ofMechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online), Volume 5, Issue 10, October (2014), pp. 70-77 © IAEME 77 REFERENCES [1] K. H. HO, S. T. Newman, State of the art of Electrical discharge machining (EDM), Int. J. of Machine Tools and Manufacture 43, 2003, 1287-1300. [2] E. Fitzer and W. Huttner, Structure and strength of carbon/carbon composite. J. Phy. D: Appl. Phy., 14, 1982, 347-371. [3] P.M. George, B. K. Raghunath, L.M. Manocha, Ashish M. Warrier, EDM machining of carbon-carbon composite a Taguchi approach, Journal of material processing technology 145, 2004, 66-71. [4] Y. H. Guu, H. Hocheng, N. H. Tai, S. Y. Liu, Effect of electrical discharge machining on the characteristics of carbon fiber reinforced carbon composite, Journal of Material Science, 36, 2001, 2037-2043. [5] D. Kanagarajan, R. Karthikeyan, K. Palanikumar, P. Sivaraj, Influence of process parameters on electric discharge machining of WC/30%Co composites’, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 222 (7), 2008, 807–815. [6] H.S Lu., C.K. Chang, N.C. Hwang, C.T. Chung, Grey relational analysis coupled with Principal component analysis for optimization design of the cutting parameters in high-speed end milling” Journal of Materials Processing Technology, 209(8), 2009, 3808–3817. [7] W.S Lau, M. Wang, W. B. Lee, Electrical discharge machining of carbon fiber composite materials, International Journal Machine Tools and Manufacture, 30(2), 1990, 297-308. [8] M. K. Pradhan, and C. K. Biswas, Multi-response optimization of EDM AISI D2 tool steel using response surface methodology. International Journal of Machining and Machinability of Materials (IJMMM), 9, 2011, 66–85. [9] J. L. Lin, C. L. Lin, The use of the orthogonal array with grey relational analysis to optimize the electrical discharge machining process with multiple performance characteristics. Int. J. of machine tool and manuf. 42, 2002, 237-244 [10] M. Kiyak, O. Cakir, Examination of machining parameters on surface roughness in EDM of tool steel, Journal of Materials Processing Technology 191, 2007, 141–144. [11] J. Marafona, Black layer characterization and electrode wear ratio in electrical discharge machining (EDM), Journal of Materials Processing Technology, 184, 2007, 27–31. [12] Ranjit K. Roy, Design of experiments using the Taguchi approach: 16 steps to product and process improvement, John Wiley Sons, Inc. New York, 2001. [13] S. K. Sahu and Saipad Sahu, “A Comparative Study on Material Removal Rate by Experimental Method and Finite Element Modelling in Electrical Discharge Machining”, International Journal of Mechanical Engineering Technology (IJMET), Volume 4, Issue 5, 2013, pp. 173 - 181, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359. [14] Mane S.G. and Hargude N.V., “An Overview of Experimental Investigation of Near Dry Electrical Discharge Machining Process”, International Journal of Advanced Research in Engineering Technology (IJARET), Volume 3, Issue 2, 2012, pp. 22 - 36, ISSN Print: 0976-6480, ISSN Online: 0976-6499. [15] P.B.Wagh, R.R.Deshmukh and S.D.Deshmukh, “Process Parameters Optimization for Surface Roughness in EDM for AISI D2 Steel by Response Surface Methodology”, International Journal of Mechanical Engineering Technology (IJMET), Volume 4, Issue 1, 2013, pp. 203 - 208, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359. [16] A. Parshuramulu, K. Buschaiah and P. Laxminarayana, “A Study on Influence of Polarity on the Machining Characteristics of Sinker EDM”, International Journal of Advanced Research in Engineering Technology (IJARET), Volume 4, Issue 3, 2013, pp. 158 - 162, ISSN Print: 0976-6480, ISSN Online: 0976-6499.