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

• Published in: Computer modeling in engineering & sciences Vol. 124; no. 2; pp. 459 - 476
• Main Authors: Ganesh, N., Ghadai, Bhoi, Kalita, Gao, Xiao-Zhi
• Format: Journal Article
• Language: English
• Published: Henderson Tech Science Press 01.01.2020
• Subjects: Brittle materials; Edm; Electric discharge machining; Genetic Algorithm; Genetic algorithms; Genetic Programming; Global optimization; Machine shops; Machine tools; Material removal rate (machining); Micro-Machining; Optimization; Polynomials; Prediction models; Predictions; Response surface methodology; Tool wear
• ISSN: 1526-1492; 1526-1506; 1526-1506
• DOI: 10.32604/cmes.2020.09645

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 mini- mize 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.