Multi-Objective Particle Swarm Optimization for Al 6351-T6 Alloy CNC Turning Parameter Optimization

CNC turning is a popular machining process where material is removed from a rotating workpiece using a cutting tool. Productivity in CNC turning depends on parameters such as cutting speed, feed rate, and depth of cut. This paper presents an optimization approach using ANOVA to enhance production time, cost, and surface finish in CNC turning. The study examined the effects of cutting speed, feed rate, and depth of cut on machining Al 6351-T6 alloy. Key parameters were identified using ANOVA, and the multiobjective Particle Swarm Optimization (MOPSO) algorithm was employed to find optimal cutting conditions that minimize production time and cost while maximizing surface finish. A manufacturing time of 593.19 minutes, a manufacturing cost of Rs. 47.69, and a surface finish of 1.16 µm were achieved with the optimized parameters that included a feed rate of 0.107 mm/rev, cutting speed of 82 m/min, and depth of cut of 1.4 mm. This approach can significantly enhance CNC turning efficiency and reduce costs in manufacturing.