Modeling and Combined Application of MOEA/D and TOPSIS to Optimize WEDM Performances of A286 Superalloy

Superalloys are categorized as difficult to process materials with a broad spectrum of applications in industries. Process modeling and optimization of WEDM performances on nickel- and titanium-based superalloys are widely investigated. However, such investigations on iron-based superalloy are still lacking and hence probed in the present article. Thus, the paper targets modeling the correlation between the performance parameters and the control parameters with two popular techniques: response surface methodology (RSM) and artificial neural network (ANN) for WEDM of a typical iron-based superalloy, i.e., A286 superalloy. A comparison between the model estimates and the experimental values is made to check ANN and RSM's prediction accuracy. The estimates by the ANN model are exact and consistent with the experimental results. An analysis of variance (ANOVA) test is performed to perceive the degree of statistical significance of parameters. Moreover, a novel two-stage procedure, i.e., MOEA/D in collaboration with TOPSIS method, is implemented to search the optimal condition for process performances. The quality of Pareto-optimal solutions acquired using MOEA/D is compared to that of Pareto-optimal solutions obtained using NSGA II, PESA II, and MMOPSO through the use of a hypervolume (HV) parameter. Wilcoxon’s test is performed to identify the statistical difference between MOEA/D and competing algorithms. The optimal parametric combination recommended by the proposed optimization approach is Ton = 130 µs, Toff = 52 µs, Ipeak = 12 A, Wf = 5 m/min and SV = 30 V. The proposed optimization technique can also be exploited in other manufacturing processes.