Optimization and comparison of machining characteristics of SKD61 steel in powder-mixed EDM process by TOPSIS and desirability approach

In this paper, tungsten carbide powder adding the dielectric liquid during electro-discharge machining (EDM) process for processing SKD61 steel was explored. Firstly, the influence of main process variables, comprising peak current ( I p ), pulse on time ( T on ), and amount of powder ( A p ) on material removal rate (MRR), tool wear rate (TWR), and surface roughness (R a ) was explored. Secondly, an optimal combination of these process variables is sought to enhance the quality of surfaces, MRR, and reduce TWR. A series of 15 experiments of the Box-Behnken design was performed. Subsequently, adequate mathematical models for MRR, TWR, and R a were established, with the application of analysis of variance (ANOVA) to evaluate the adequacy of these models. Finally, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and desirability approach (DA) were adopted for the multi-attribute optimization. Besides, Non-Dominated Sorting Genetic Algorithm II (NSGA II)-evaluation by an area-based method of ranking (EAMR) was also conducted and compared with both DA and TOPSIS for the most appropriate choice. The outcomes indicated that I p demonstrates the strongest influence on R a , MRR, and TWR, followed by T on and A p for MRR, while the proceeding effect is A p and T on for TWR and R a . In comparison with TOPSIS, DA provides the best solution with a decline of 41.5% in TWR and an increment of 22.7% in MRR, while TOPSIS contributes the best solution with a drop of 13.89% in R a when compared with DA. In addition, TOPSIS provides better surface quality than DA.