Optimization of electro-discharge machining process using rapid tool electrodes via metaheuristic algorithms

The present study explores the application of rapid prototyping (RP) for manufacturing tool electrodes in electro-discharge machining process. The performance of a metallic electrode built via selective laser sintering is compared to solid copper and brass tools during machining of D2 tool steel. In order to efficiently evaluate the influence of several parameters, Taguchi’s L₁₈ design is adopted to plan the experimental layout. The machining parameters considered in this study are tool type, a categorical parameter and three quantitative parameters such as duty cycle, pulse-on-time and peak current. Multiple performance measures such as material removal rate, tool wear rate, surface roughness and radial over cut of the machined cavity are considered. The multiple performance responses are converted into an equivalent single response known as grey relational grade using grey relational analysis. A nonlinear regression model is developed to relate grey relational grade with process parameters with a coefficient of determination of 0.97. In order to obtain optimal parameter settings satisfying the performance measures, three meta-heuristic algorithms are used due to their computational elegance. The comparative study indicates that particle swarm optimization and simple optimization are effective in delivering the optimized results in substantially less time compared to teaching-learning-based optimization algorithms. It is found that RP tool can perform in a superior manner for simultaneous optimization of multiple responses when compared to copper and brass tools.