Multi-Objective Optimization for Improving Performance Characteristics of Novel Curved EDM Process Using Jaya Algorithm

Abstract

Advanced machining processes are used for machining hard and brittle materials and for producing complex shapes with a high degree of precision and accuracy. In this chapter, a novel curved electrical discharge machining (EDM) process is proposed for machining curved channels used in the plastic injection mould. The mechanism is built up with a programmable electronic control system required for operating the mechanical and electrical components. The experimental design and performance characteristics are studied using Taguchi orthogonal array (L12) consisting of eight control variables (pulse ON time, duty cycle, sparking current, bi-pulse current, sparking time, lifting time, gap voltage and sensitivity). The electrode wear rate (EWR) and material removal rate (MRR) are considered as machining responses for evaluating the performance of the novel curved EDM. It is found that the major contributing factors to EWR and MRR are pulse ON time (48.52%) and sparking current (25.01%). The statistical hypothesis testing indicates that the probability values of current and sensitivity are the two major control variables primarily affecting the machining responses. Further, regression analysis is implemented to establish the relation between input variables and machining responses through regression equations. The estimated coefficients of regression model, viz. coefficient of determination (R 2) and adjusted R 2 (R 2 adj), are 99.83%, 98.10% and 99.86%, 98.49% for EWR and MRR, respectively. Later on, these regression equations are considered as objective functions for multi-objective optimization. The advanced algorithm Jaya was applied for improving the performance characteristics. The obtained optimum values of minimum EWR and maximum MRR are 0.4445 and 0.779 mm3/min, respectively. Furthermore, on comparative analysis of the experimental values with the optimized values obtained using the advanced Jaya algorithm, the performance improvement observed in EWR and MRR was 24.71% and 70.60%, respectively. Hence, it can be ascertained that the Jaya algorithm is successfully implemented in the optimization of machining responses of the novel curved EDM.