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Multi-Objective Optimization of Die-Sinking EDM Process on AISI P-20 Tool Steel Using Cuckoo Search and Genetic Algorithm

Multi-Objective Optimization of Die-Sinking EDM Process on AISI P-20 Tool Steel Using Cuckoo Search and Genetic Algorithm

Goutam Kumar Bose, Pritam Pain, Sayak Mukhopadhyay
Copyright: © 2017 |Pages: 19
ISBN13: 9781522516392|ISBN10: 1522516395|EISBN13: 9781522516408
DOI: 10.4018/978-1-5225-1639-2.ch006
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MLA

Bose, Goutam Kumar, et al. "Multi-Objective Optimization of Die-Sinking EDM Process on AISI P-20 Tool Steel Using Cuckoo Search and Genetic Algorithm." Mathematical Concepts and Applications in Mechanical Engineering and Mechatronics, edited by Mangey Ram and J. Paulo Davim, IGI Global, 2017, pp. 111-129. https://doi.org/10.4018/978-1-5225-1639-2.ch006

APA

Bose, G. K., Pain, P., & Mukhopadhyay, S. (2017). Multi-Objective Optimization of Die-Sinking EDM Process on AISI P-20 Tool Steel Using Cuckoo Search and Genetic Algorithm. In M. Ram & J. Davim (Eds.), Mathematical Concepts and Applications in Mechanical Engineering and Mechatronics (pp. 111-129). IGI Global. https://doi.org/10.4018/978-1-5225-1639-2.ch006

Chicago

Bose, Goutam Kumar, Pritam Pain, and Sayak Mukhopadhyay. "Multi-Objective Optimization of Die-Sinking EDM Process on AISI P-20 Tool Steel Using Cuckoo Search and Genetic Algorithm." In Mathematical Concepts and Applications in Mechanical Engineering and Mechatronics, edited by Mangey Ram and J. Paulo Davim, 111-129. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-1639-2.ch006

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Abstract

Electrical Discharge Machining (EDM) is nontraditional machining processes applied for precise machining and developing intricate geometries on work materials which are difficult to machine by conventional process. The present research work emphases on the die sinking EDM of AISI P20 tool steel, to study the effect of machining parameters such as pulse on time (POT), pulse off time (POF), discharge current (GI) and spark gap (SG) on performance response like Material removal rate (MRR), Surface Roughness (Ra) and Overcut (OC) using square-shaped Cu tool with Lateral flushing. The experimentation is performed using L27 orthogonal array and significant process parameters are ascertained using Regression analysis. The influence of the important process parameters on individual responses are detected using Cuckoo search algorithm. The present chapter is aimed at multi-response optimization i.e. higher MRR, lower Ra and minimum OC, which is conceded out using Genetic Algorithm.

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