RBF and NSGA-II based EDM process parameters optimization with multiple constraints

Xiaoke Li; Fuhong Yan; Jun Ma; Zhenzhong Chen; Xiaoyu Wen; Yang Cao; Xiaoke Li; Fuhong Yan; Jun Ma; Zhenzhong Chen; Xiaoyu Wen; Yang Cao

doi:10.3934/mbe.2019289

Mathematical Biosciences and Engineering

2019, Volume 16, Issue 5: 5788-5803. doi: 10.3934/mbe.2019289

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Research article Special Issues

RBF and NSGA-II based EDM process parameters optimization with multiple constraints

1.
Henan Key Laboratory of Mechanical Equipment Intelligent Manufacturing, School of Mechanical and Electrical Engineering, Zhengzhou University of Light Industry, Zhengzhou, MO 450002, China
2.
College of Mechanical Engineering, Donghua University, Shanghai, MO 201620, China

Received: 17 April 2019 Accepted: 13 June 2019 Published: 21 June 2019

In this study, the radial basis function (RBF) which has good performance for nonlinear problem is introduced to approximate the implicit relationships between EDM parameters and performance responses for 304 steel. The fitting precision of RBF is compared with the second order polynomial response surface (PRS), support vector regression (SVR) and Kriging model (KRG) using the multiple correlation coefficient (R2) based cross validation error method. Then the RBF model is called to conduct multi-objective optimization using non-dominated sorting genetic algorithm II (NSGA-II) method. The energy consumption index unit energy consumption (UEC) and the air-pollution indices PM2.5 and PM10 are considered in proposed multi-objective optimization model. UEC is considered as the objective function to reduce the machining cost and the PM indices are termed as the constraints to protect the operators' health. The pulse current, time period and duty cycle are considered as the main factors affecting the EDM responses. According to the Pareto plots of multi-objective optimization model, conclusion can be drawn that SR and PM10 play significant roles in multi-optimization and PM2.5 has less influence on optimization results. The results of the present study reveal that using maximum material removal rate (MRR) and minimum UEC as objective and using surface roughness (SR), PM2.5 and PM10 as constraints can be an effective method to provide appropriate process parameters reference for EDM machining.

Keywords:

Citation: Xiaoke Li, Fuhong Yan, Jun Ma, Zhenzhong Chen, Xiaoyu Wen, Yang Cao. RBF and NSGA-II based EDM process parameters optimization with multiple constraints[J]. Mathematical Biosciences and Engineering, 2019, 16(5): 5788-5803. doi: 10.3934/mbe.2019289

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Abstract

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