An intelligent predictive model-based multi-response optimization of EDM process

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.