Comparison of Intelligent Optimization Algorithms for Wire Electrical Discharge Machining Parameters

In this research the influence of wire electrical discharge machining on surface roughness and volumetric material removal rate is conducted. With use of experimental result analysis, design of experiments method and mathematical modeling, the correlation between corresponding parameters and process output characterization are studied. The investigated input parameters include electrical current, pulse-off time, open- circuit voltage and gap voltage. With use of experimental results and, subsequently, with exploitation of variance analysis, importance and effective percentages of each parameter are studied. In order to find optimal conditions, outputs extracted from Non-dominated Sorting Genetic and Tabu search algorithms compared with each other led in achieving appropriate models. Tabu search algorithm and Non-dominated Sorting Genetic Algorithm were compared with each other proving the superiority of Non-dominated Sorting Genetic Algorithm over Tabu search algorithm in optimizing machining parameters.