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

Abstract Superalloys are categorized as difficult to process materials with a broad spectrum of applications in industries. Process modeling and optimization of Wire Electric Discharge Machining (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 first part of the paper targets modelling 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 is carried out between the model estimates and the experimental values 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, in the second part, a novel two-stage procedure, i.e., a multiobjective evolutionary algorithm based on decomposition (MOEA/D) in collaboration with a decision-making method, i.e., a technique for order preference by similarity to ideal solution (TOPSIS) method is implemented to search the optimal condition for process performances. The optimal parametric combination recommended by the proposed optimization approach is Ton = 130 µs, Toff = 52 µs, Ipeak= 12A, Wf = 5 m/min and SV = 30 volt. The proposed optimization technique can also be exploited in other manufacturing processes.