Machinability of 9CrSi steel as processed by powder-added electric discharge machining: Investigation and optimization for boosted machining characteristics

Machinability investigation of 9CrSi steel by electric discharge machining (EDM with the addition of tungsten powder alloy is rarely investigated. Therefore, in this study, the impact of control parameters {comprising peak-current (Ip), pulse-on time (Ton), and powder amount (Cp)} on machining features including material removal rate (MRR), tool wear rate (TWR), and surface roughness (Ra), was explored. Furthermore, determining the optimal domain of control parameters is meaningful in improving the MRR, Ra, and reducing TWR. In order to achieve this, the MRR, Ra, and TWR prediction models were established and assessed using analysis of variance (ANOVA) to verify the models' suitability and accuracy. Eventually, the Grey Relational Analysis (GRA) technique and the Desired Approach (DA) were used for the multi-criteria optimization. The results revealed that Ip proves the most robust influence on MRR, TWR, whilst Ton has the most impact on Ra. However, the sequent influence is Ton and Cp for MRR and TWR, and Ip and Cp for Ra . Compared to GRA, the MRR value derived from DA is 399.3% higher. For TWR and Ra, the GRA provides the best optimal solution, with comparable drops of 22.34% and 48.3% as compared to DA. In addition, the surface characteristics (defects, compositional chemistry, and recast layer thickness) obtained from optimal parameters of two algorithms were also explored.