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An Intelligent Predictive Model-Based Multi-Response Optimization of EDM Process

N. Ganesh1, R. K. Ghadai2, A. K. Bhoi3, K. Kalita4,*, Xiao-Zhi Gao5

1 Department of Computer Science and Engineering, Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai, 600062, India
2 Department of Mechanical Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majhitar, 737136, India
3 Department of Electrical and Electronics Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majhitar, 737136, India
4 Department of Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, 600062, India
5 School of Computing, University of Eastern Finland, Kuopio, 70211, Finland

* Corresponding Author: K. Kalita. Email: email

Computer Modeling in Engineering & Sciences 2020, 124(2), 459-476. https://doi.org/10.32604/cmes.2020.09645

Abstract

Electrical Discharge Machining (EDM) is a popular non-traditional machining process that is widely used due to its ability to machine hard and brittle materials. It does not require a cutting tool and can machine complex geometries easily. However, it suffers from drawbacks like a poor rate of machining and excessive tool wear. In this research, an attempt is made to address these issues by using an intelligent predictive model coupled global optimization approach to predict suitable combinations of input parameters (current, pulse on-time and pulse off-time) that would effectively increase the material removal rate and minimize the tool wear. The predictive models, which are based on the symbolic regression approach exploit the machine intelligence of Genetic Programming (GP). As compared to traditional polynomial response surface (PRS) predictive models, the GP predictive models show compactness as well as better prediction capability. The developed GP predictive models are deployed in conjunction with NSGA-II to predict Pareto optimal solutions.

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APA Style
Ganesh, N., Ghadai, R.K., Bhoi, A.K., Kalita, K., Gao, X. (2020). An intelligent predictive model-based multi-response optimization of EDM process. Computer Modeling in Engineering & Sciences, 124(2), 459-476. https://doi.org/10.32604/cmes.2020.09645
Vancouver Style
Ganesh N, Ghadai RK, Bhoi AK, Kalita K, Gao X. An intelligent predictive model-based multi-response optimization of EDM process. Comput Model Eng Sci. 2020;124(2):459-476 https://doi.org/10.32604/cmes.2020.09645
IEEE Style
N. Ganesh, R.K. Ghadai, A.K. Bhoi, K. Kalita, and X. Gao "An Intelligent Predictive Model-Based Multi-Response Optimization of EDM Process," Comput. Model. Eng. Sci., vol. 124, no. 2, pp. 459-476. 2020. https://doi.org/10.32604/cmes.2020.09645

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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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