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Parametric optimization of electrical discharge machining process on α–β brass using grey relational analysis

Published online by Cambridge University Press:  09 June 2016

S. Marichamy ,
M. Saravanan ,
M. Ravichandran  and
G. Veerappan
S. Marichamy*
Affiliation:
Department of Mechanical Engineering, Vickram College of Engineering, Anna University Chennai, Sivagangai-630 561, Tamilnadu, India
M. Saravanan
Affiliation:
Department of Mechanical Engineering, Sri Subramanya College of Engineering and Technology, Anna University Chennai, Palani-624 615, Tamilnadu, India
M. Ravichandran
Affiliation:
Department of Mechanical Engineering, Chendhuran College of Engineering and Technology, Anna University Chennai, Pudukkottai-622 507, Tamilnadu, India
G. Veerappan
Affiliation:
Department of Mechanical Engineering, Vickram College of Engineering, Anna University Chennai, Sivagangai-630 561, Tamilnadu, India
*
a)Address all correspondence to this author. e-mail: [email protected]
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Abstract

In the present work, a multi response optimization technique based on Taguchi method coupled with grey relational analysis is used for electrical discharge machining operations on duplex (α–β) brass. Stir casting technique was used to fabricate the duplex brass plates. The mechanical properties of the material are reported. Experiments were conducted with three machining variables such as current, pulse-on time and spark voltage and planned as per Taguchi technique. Material removal rate (MRR), electrode wear rate (EWR), and surface roughness (SR) are chosen as output parameters for this study. Results showed that, peak current and spark voltage were the significant parameters to affect MRR, EWR, and SR as per grey relational grade. The optimal combination parameters were identified as A3B3C2 i.e., pulse current at 14 A, pulse on-time at 200 μs, and voltage at 50 V. Analysis of variance was used for analyzing the results. The confirmation tests were performed to validate the results obtained by grey relational analysis and the improvement was achieved.

Keywords

SEMduplex brasselectrical discharge machiningTaguchigrey relational analysis
Type
Articles
Information
Journal of Materials Research , Volume 31 , Issue 16 , 29 August 2016 , pp. 2531 - 2537
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Copyright
Copyright © Materials Research Society 2016 

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References

REFERENCES

Moshkovich, A., Perfilyev, V., Lapsker, I., and Rapoport, L.: Friction, wear and plastic deformation of Cu and α/β brass under lubrication conditions. Wear 320, 34 (2014).Google Scholar
Amirat, M., Zaïdi, H., Djamaï, A., Necib, D., and Eyidi, D.: Influence of the gas environment on the transferred film of the brass (Cu64Zn36)/steel AISI 1045 couple. Wear 267, 433 (2009).Google Scholar
Gao, Y., Nakata, K., Nagatsuka, K., Shibata, Y., and Amano, M.: Optimizing tool diameter for friction stir welded brass/steel lap joint. J. Mater. Process. Technol. 229, 313 (2016).CrossRefGoogle Scholar
Li, S., Imai, H., Atsumi, H., and Kondoh, K.: Contribution of Ti addition to characteristics of extruded Cu40Zn brass alloy prepared by powder metallurgy. Mater. Des. 32, 192 (2011).Google Scholar
Du, D., Guan, G., Gagnoud, A., Fautrelle, Y., Ren, Z., Lu, X., Wang, H., Dai, Y., Wang, Q., and Li, X.: Effect of a high magnetic field on the growth of ε-CuZn5 dendrite during directionally solidified Zn-rich Zn–Cu alloys. Mater. Charact. 111, 31–42 (2016).Google Scholar
Khanna, R., Kumar, A., Pal Garg, M., Singh, A., and Sharma, N.: Multiple performance characteristics optimization for Al 7075 on electric discharge drilling by Taguchi grey relational theory. Int. J. Ind. Eng. 11(4), 459 (2015).CrossRefGoogle Scholar
Vilarinho, C., Davim, J.P., Soares, D., Castro, F., and Barbosa, J.: Influence of the chemical composition on the machinability of brasses. J. Mater. Process. Technol. 170, 441–447 (2005).Google Scholar
Peng, K., Su, L., Shawb, L.L., and Qian, K-W.: Grain refinement and crack prevention in constrained groove pressing of two-phase Cu–Zn alloys. Scr. Mater. 56, 987–990 (2007).Google Scholar
Imai, H., Kosaka, Y., Kojima, A., Li, S., Kondoh, K., Umeda, J., and Atsumi, H.: Characteristics and machinability of lead-free P/M Cu60–Zn40 brass alloys dispersed with graphite. Powder Technol. 198, 417–421 (2010).CrossRefGoogle Scholar
Nanimina, A.M., Abdul-Rani, A.M., Ahmad, F., Zainuddin, A., and Jason, S.H.L.: Effects of electro- discharge machining on aluminum metal matrix composite. J. Appl. Sci. 11, 1668 (2011).Google Scholar
Senthil, P., Sekar, V., and Singh, A.K.: Parametric optimization of EDM on Al–Cu/TiB2 in situ metal matrix composites using TOPSIS method. Int. J. Mach. Mach. Mater. 16(1), 80 (2014).Google Scholar
Radhika, N., Chandran, G.K., Shivaram, P., and Vijay Kumar, K.T.: Multi-objective optimization of EDM parameters using grey relational analysis. J. Eng. Sci. Technol. 10(1), 1 (2015).Google Scholar
Sidhu, S.S., Kumar, S., and Batish, A.: Electric discharge machining of 10 vol% Al2O3/Al metal matrix composite—An experimental study. Mater. Sci. Forum 751, 9 (2013).Google Scholar
Dhar, S., Purohit, R., Saini, N., Sharma, A., and Hemath Kumar, G.: Mathematical modeling of electric discharge machining of cast Al–4Cu–6Si alloy–10 wt% SiCP composites. J. Mater. Process. Technol. 194(1–3), 24 (2007).Google Scholar
Rengasamy, N.V., Rajkumar, M., and Senthil Kumaran, S.: An analysis of mechanical properties and optimization of EDM process parameters of Al 4032 alloy reinforced with Zrb2 and Tib2 in situ composites. J. Alloys Compd. 662, 325 (2015).Google Scholar
Hourmand, M., Farahany, S., Sarhan, A.A.D., and Noordin, M.Y.: Investigating the electrical discharge machining (EDM) parameter effects on Al–Mg2Si metal matrix composite (MMC) for high material removal rate (MRR) and less EWR–RSM approach. Int. J. Adv. Des. Manuf. Technol. 77, 831 (2015).Google Scholar
Singh, S.: Optimization of machining characteristics in electric discharge machining of 6061Al/Al2O3p/20P composites by grey relational analysis. Int. J. Adv. Des. Manuf. Technol. 63(9), 1191 (2012).CrossRefGoogle Scholar
Balamurugan, M., Biswanath, M., and Sukamal, G.: Optimisation of machining parameters for hard machining: Grey relational theory approach and ANOVA. Int. J. Adv. Des. Manuf. Technol. 45, 1068 (2009).Google Scholar
El-Taweel, T.A.: Multi-response optimization of EDM with Al–Cu–Si–TiC P/M composite electrode. Int. J. Adv. Des. Manuf. Technol. 44, 100 (2009).Google Scholar
Pate, K.M., Pandeya, P.M., and Venkateswara Rao, P.: Determination of an optimum parametric combination using a surface roughness prediction model for EDM of Al2O3/SiCw/TiC ceramic composite. Mater. Manuf. Processes 24, 675 (2009).Google Scholar
Narender Singh, P., Raghukandan, K., and Pai, B.C.: Optimization by grey relational analysis of EDM parameters on machining Al–10% SiC composites. J. Mater. Process. Technol. 155, 1658 (2004).Google Scholar
Ravichandran, M., Naveen Sait, A., and Anandakrishnan, V.: Workability studies on Al–TiO2–Gr powder metallurgy composites under tri-axial stress state during cold upsetting. Mater. Res. 17(6), 1489 (2014).CrossRefGoogle Scholar
Nobel, C., Hofmann, U., Klocke, F., Veselovac, D., and Pul, H.: Application of a new, severe-condition friction test method to understand the machining characteristics of Cu–Zn alloys using coated cutting tools. Wear 344, 58 (2015).Google Scholar
Tzeng, Y-f. and Chen, F-c.: Multi-objective optimisation of high-speed electrical discharge machining process using a Taguchi fuzzy-based approach. Mater. Des. 28, 1159 (2007).Google Scholar
Wang, C-C., Chow, H-M., Yang, L-D., and Lu, C-T.: Recast layer removal after electrical discharge machining via Taguchi analysis: A feasibility study. J. Mater. Process. Technol. 209, 4134 (2009).Google Scholar
Tripathy, S. and Tripathy, D.K.: Multi-attribute optimization of machining process parameters in powder mixed electro-discharge machining using TOPSIS and grey relational analysis. Int. J. Eng. Sci. Technol. 19, 62 (2016).Google Scholar
Bobbili, R., Madhu, V., and Gogia, A.K.: Multi response optimization of wire-EDM process parameters of ballistic grade aluminium alloy. Int. J. Eng. Sci. Technol. 18(4), 720 (2015).Google Scholar
Selvakumar, G., Sornalatha, G., Sarkar, S., and Mitra, S.: Experimental investigation and multi-objective optimization of wire electrical discharge machining (WEDM) of 5083 aluminum alloy. Trans. Nonferrous Met. Soc. China 24, 373 (2014).Google Scholar
Kandpal, B.C., Kumar, J., and Singh, H.: Machining of aluminium metal matrix composites with Electrical discharge machining—A review. Mater. Today: Proceed. 2, 1665 (2015).Google Scholar
Pramanik, A., Basak, A.K., Islam, M.N., and Littlefair, G.: Electrical discharge machining of 6061 aluminium alloy. Trans. Nonferrous Met. Soc. China 25, 2866 (2015).Google Scholar
Mathan Kumar, N., Senthil Kumaran, S., and Kumaraswamidhas, L.A.: An investigation of mechanical properties and material removal rate, tool wear rate in EDM machining process of AL2618 alloy reinforced with Si3N4, AlN and ZrB2 composites. J. Alloys Compd. 650, 318 (2015).Google Scholar