Abstract
Al-Mg-based composite is used in producing a variety of components. To improve the machinability of the composite, MoS2 is added. For characterizing the machining of the Al-Mg-based composite, different wt.% (2, 4, and 6) of MoS2 are added as reinforcement. Wire Electrical Discharge Machining (WEDM) process is performed to analyze the kerf width and surface roughness. Due to the complex nature of the WEDM process, the necessity for its optimization through the use of innovative methods is well-proven in the process of research. Evolutionary algorithms, specifically genetic algorithm based on NSGA-II and Multiple Objective Particle Swarm Optimization (MOPSO), are used for optimizing kerf width and surface roughness. For assessing the impact of current, pulse on time, and gap voltage on kerf width and surface roughness, an analysis of the selected WEDM process parameters is performed. MOPSO takes lesser iterations as compared to NSGA-II in giving nearly the same optimal fronts for achieving low kerf width and surface roughness. The 10−12 A of current, 50−57 µs of pulse on time, and 30−33 V of gap voltage are used for the WEDM process based on the Pareto-optimal solutions and better performance is achieved on the samples. In addition, the supplementary DOE method is applied to determine the relationship between factors affecting a process and the response. The analysis revealed that current has played a major part in the governance of kerf width and surface roughness over pulse on time and gap voltage for Al-Mg-MoS2 composite.