Performance Enhancement of End Milling Process Using Particle Swarm Optimization

End milling is a crucial machining process that has a significant impact on manufacturing productivity and part quality. Although gun metal alloys are widely used in industry, there is little research on their machinability. This work focuses on concurrently optimizing two machining responses; material removal rate and surface roughness while end milling gun metal. Particle Swarm Optimization (PSO) algorithm has used for this purpose. A structured methodology is proposed, which integrates Grey Relational Analysis to convert multiple responses to single response, trailed by PSO optimization of cutting speed, feed rate, and coolant application. This two-way tactic is used to recognize best machining parameters that boost productivity while sustaining better surface finish. The PSO-based technique fruitfully detects better machining parameters, as evidenced by experimental results. The study offers practical insights on selecting the optimal cutting conditions for industrial gun metal machining applications, which can lead to improved process efficiency and part quality.