Optimization of electrochemical machining process parameters: Combining response surface methodology and differential evolution algorithm

Abstract

Electrochemical machining is a unique prevalent nonconventional manufacturing process used in different industries involving various process parameters, which greatly influence machining performance. Therefore, selection of proper and optimal parameters setting is a challenging issue. In this paper, differential evolution algorithm is applied to look for the optimum solution to this problem. Four parameters, i.e. voltage, tool feed rate, electrolyte flow rate, and electrolyte concentration; and two machining criteria, i.e. material removal rate and surface roughness (Ra) are considered as input variables and responses, respectively. The main purpose is to maximize material removal rate and minimize Ra to achieve better machining performance. In this way, comprehensive mathematical models have first been developed using response surface methodology through experimentation based on central composite design plan. Then, differential evolution algorithm has been utilized for optimizing the process parameters; both single- and multiobjective optimizations are considered, and optimal Pareto front is determined. Finally, optimization result of a trade-off design point in the Pareto front of Ra and material removal rate was also verified experimentally. This machined surface was examined with field-emission scanning electron microscope images. The results showed that the proposed approach is an effective and suitable strategy for optimization of the electrochemical machining process.