GA based Optimization for the Production of Quality Jobs with Minimum Power Consumption in EDM of Hybrid MMCs

Abstract

Metal matrix composites (MMCs) are commonly used in various manufacturing sectors such as automotive, defence and aerospace sectors due to their higher strength and stiffness, lower weights and resistance to wear and damage. The MMCs reinforced with two particulates i.e. the hybrid MMCs take advantage of both characteristics of reinforcement materials used in MMCs. Here, the experimental studies focus on optimizing the input machining parameters of electric discharge machining (EDM) method during machining of hybrid aluminium based metal matrix composites (AMCs) added with SiC and B4C reinforcement materials. Here pulse current (I), pulse duty factor (s), pulse-on-time (Ton), and gap voltage (V) are the important process parameters used to study their impact on the end results of machining by EDM process namely power consumed (P) and surface roughness (SR). Predictive models were developed and the predicted performance of the model shows an error percentage 6.10% and is 6.84% for SR and P respectively. The results from ANOVA analysis shows that pulse current (I) have significant contribution on both the output parameters. GA is used to as multi objective optimization tool. The GA optimized Pareto optimal fronts provide different optimum cutting conditions for different SR and P values. We need to select the preferred SR value of minimum P to get the optimal machining conditions. The approach is found to be an effective tool and can be developed with minimum effort and help shop floor engineer.