Experimental Investigations on the Electrochemical Machining of D3 Die Steel Material and Multi-Objective Parameter Optimization

Abstract

The removal of metal in the form of sludge can be termed an ECM process, having primary salient features consisting of the tool, workpiece, power supply and electrolyte solution. ECM is a potential nontraditional machining process that includes too many factors contributing to process performance. Therefore, obtaining optimal factor combinations for higher efficiency is complex. In this research work, machining parameters are voltage, powder concentration and electrolyte concentration, based on which material removal rate (MRR) and surface roughness (SR) are derived. Regression analysis, signal-to-noise ratios and analysis of variance (ANOVA) are performed to determine the optimal levels and to examine the effects of machining parameters on MRR and SR. The results derived from the experiments noticed that the MRR decreases when the voltage level is increased and surface roughness decreases for the applied voltage. In case of an increase in powder concentration, MRR decreases from range 4 to 6 and further reduces to 6–8. In case of SR, it additionally decreases on the maximum level of powder concentration. In electrolyte concentration, the MRR decreases on the increment of electrolyte and increases after a short period of time; simultaneously SR is increased from range value 10–20 and decreased with a maximum level 20–30 of NaCl solution. A comparison of MOORA-PCA and TOPSIS-PCA demonstrates the superiority of TOPSIS over MOORA technique. Statistical results (95% confidence level) indicate that the voltage, powder concentration, and electrolyte concentration affect the surface roughness by 69.50%, 8.67% and 14.59% in the electrochemical machining of d3 die steel. Therefore, the optimum combination of process parameters corresponding to voltage: 45[Formula: see text]V, powder concentration: 4[Formula: see text]g/L, and electrolyte concentration: 10 NaCl/L, respectively, were found to yield the desired result. The prediction accuracy of the TOPSIS-PCA hybrid approach model is found better than MOORA-PCA technique.