MCDM Optimization of Parameters for Wire-EDM Machined Stainless Steel using Hybrid RSM-TOPSIS, Genetic Algorithm and Simulated Annealing
Dilip Kumar Bagal1, Abhishek Barua2, Siddharth Jeet3, Pratyashi Satapathy4, Dulu Patnaik5
1Dilip Kumar Bagal*, Department of Mechanical Engineering, Government College of Engineering Kalahandi, Bhawanipatna, Odisha, India.
2Abhishek Barua, Department of Mechanical Engineering, Centre for Advanced Post Graduate Studies, BPUT, Rourkela, Odisha, India.
3Siddharth Jeet, Department of Mechanical Engineering, Centre for Advanced Post Graduate Studies, BPUT, Rourkela, Odisha, India.
4Pratyashi Satapathy, Department of Computer Science and Engineering, National Institute of Technology, Rourkela, Odisha, India.
5Dulu Patnaik, Department of Electrical Engineering, Government College of Engineering Kalahandi, Bhawanipatna, Odisha, India.
Manuscript received on September 22, 2019. | Revised Manuscript received on October 05, 2019. | Manuscript published on October 30, 2019. | PP: 366-371 | Volume-9 Issue-1, October 2019 | Retrieval Number: A9349109119/2019©BEIESP | DOI: 10.35940/ijeat.A9349.109119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In this investigation, the influence of WEDM process constraints on tool wear rate, kerf width, surface roughness of SS 304 grade stainless steel was studied. Fifteen experimental runs were carried out based on Box-Behnken method of Response surface methodology and TOPSIS method was used for finding an optimum parameter setting. From the ANOVA results, pulse ON time was found as most significant factor for tool wear rate, kerf width and surface roughness. Genetic Algorithm and Simulated Annealing was also used for the calculation of the optimum setting along with the forecast of fitness values. It was found that every optimization technique gives similar factor setting.
Keywords: ANOVA, Box-Behnken, Genetic Algorithm, Response surface methodology, TOPSIS method, Simulated Annealing.