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Nature-Inspired Metaheuristic Approach for Multi-Objective Optimization During WEDM Process

Nature-Inspired Metaheuristic Approach for Multi-Objective Optimization During WEDM Process

Goutam Kumar Bose, Pritam Pain
Copyright: © 2018 |Pages: 32
ISBN13: 9781522530350|ISBN10: 1522530355|EISBN13: 9781522530367
DOI: 10.4018/978-1-5225-3035-0.ch004
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MLA

Bose, Goutam Kumar, and Pritam Pain. "Nature-Inspired Metaheuristic Approach for Multi-Objective Optimization During WEDM Process." Soft Computing Techniques and Applications in Mechanical Engineering, edited by Mangey Ram and J. Paulo Davim, IGI Global, 2018, pp. 91-122. https://doi.org/10.4018/978-1-5225-3035-0.ch004

APA

Bose, G. K. & Pain, P. (2018). Nature-Inspired Metaheuristic Approach for Multi-Objective Optimization During WEDM Process. In M. Ram & J. Davim (Eds.), Soft Computing Techniques and Applications in Mechanical Engineering (pp. 91-122). IGI Global. https://doi.org/10.4018/978-1-5225-3035-0.ch004

Chicago

Bose, Goutam Kumar, and Pritam Pain. "Nature-Inspired Metaheuristic Approach for Multi-Objective Optimization During WEDM Process." In Soft Computing Techniques and Applications in Mechanical Engineering, edited by Mangey Ram and J. Paulo Davim, 91-122. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-3035-0.ch004

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Abstract

In this research paper Wire-Electric Discharge Machining (WEDM) is applied to machine AISI-D3 material in order to measure the performance of multi-objective responses like high material removal rate and low roughness. This contradictory objective is accomplished by the control parameters like Pulse on Time (Ton), Pulse off Time (Toff), Wire Feed (W/Feed) and Wire Tension (W/Ten) employing brass wire. Here the orthogonal array is used to developed 625 parametric combinations. The optimization of the contradictory responses is carried out in a metaheuristic environment. Artificial Neural Network is employed to train and validate the experimental result. Primarily the individual responses are optimized by employing Firefly algorithm (FA). This is followed by a multi-objective optimization through Genetic algorithm (GA) approach. As the results obtained through GA infer a domain of solutions, therefore Grey Relation Analysis (GRA) is applied where the weights are considered through Fuzzy set theory to ascertain the best parametric combination amongst the set of feasible alternatives.

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