HYBRID MODELING OF PROCESS PARAMETERS PRODUCED BY ELECTRIC DISCHARGE MACHINING USING DIFFERENTIAL EVALUATION AND PARTICLE SWARM OPTIMIZATION

Abstract

One of the important advanced manufacturing technologies in machining hard-to-cut complicated contour is electrical discharge machining (EDM) and it is highly appreciated because it is economical and effective for moulding materials of heat resistance and high strength into complex one. The EDM process deals with erosion of electrically conductive materials by the way of initiating rapid and repetitive spark discharge between the work piece and electrode tool, with the separation of gap of 0.05 to 0.5 mm which is called spark gap. A die electric fluid soakes this spark gap. In EDM process machining parameters are considered to be important to achieve optimal machining performance in high heat resistance and hard material. The hand book provides all the details about the opted machining parameters but it does not guarantee that the obtained parameters are close to optimal performance. The EDM process should have the features of robustness, accuracy, process capability to confront with the challenges of the changing world. The EDM process should be modeled and optimized to achieve perfection as mentioned above. The researcher uses RSM to investigate the effect of five controllable input variables (viz) peak current, discharge voltage, pulse on time, pulse off time, and oil pressure. MRR and SR, two output variables are the responses. Experiments were administered on AISI 1020 steel with copper electrode. The response is modeled using RSM on experimental data. The remarkable coefficients are received by performing analysis of variance (ANOVA) at the level of 5%. This study shows the MRR and SR have been significantly influenced by peak current, pulse on time, pulse off time, discharge voltage and oil pressure. The soft computing tools differential evolution and PSO are framed, developed, experimented as predictive models.