Multi-response optimization of laser-assisted jet electrochemical machining parameters based on gray relational analysis

Abstract

The gray relational analysis is an important technique can be effectively used for forecasting, decision making in different areas of manufacturing and products processing. In this research, the gray relational analysis has been employed to optimize the input process parameters of the developed laser-assisted jet electrochemical machine (LA-JECM) for better machining performance characteristics. Taguchi method-based design of experiment L16 (44) orthogonal array was employed and experiments were carried out for investigation. The optimal parametric combination for multi-response optimization was identified based on the collective implementation of Taguchi methodology and gray relational analysis during microdrilling of Inconel-718. A LA-JECM has been developed and utilized for experimental investigation. The experimental results revealed that there is 29.16% increase in MRR; 48.43% decrease in taper and 36.83% reduction in surface roughness height, Ra (µm) when experiments were carried out on LA-JECM over JECM. The laser assistance with JECM improves the machining quality and reduces machining time. Taguchi methodology and gray relational analysis based multi-optimization found that the parametric setting, i.e., at supply voltage 80 V, electrolyte concentration 40 g/l, inter-electrode gap 3 mm, and duty cycle 60% gives maximum material removal rate with minimum taper angle and surface roughness height (Ra, µm) of the machined hole.