Optimization of material removal rate in wire EDM by polynomial neural network models

Abstract

Maximization of material removal rate may yield impractical results if the cost for generation of removal rate is not included in the optimization. Presently, there are many studies which have applied different linear, nonlinear, and metaheuristics optimization techniques to maximize material removal rate but none of the approaches have the capability of self‐adaptation. In none of these studies impact of cost on removal rate was considered. That is why, in the present study, the Group Method of Data Handling which is a technique to develop polynomial neural network (PNN) models was utilized for the first time to maximize material removal rate with the help of the on‐ and off‐stage current as the design variables constrained by the operating costs. The PNN architecture is widely used in many fields of technology and science. However, application of this architecture is scarce in case of optimization of material removal rate. According to the results the accuracy level and time of convergence in case of PNN was found to be better compared to that from artificial neural network and linear models both of which were utilized for estimation of the output variable.