Optimisation of multipass turning operations using PSO and GA-AIS algorithms

Abstract

In this paper, two evolutionary heuristics are proposed to determine the cutting parameters in multipass turning operations. Optimal selection of cutting speed, feed rate, and depth of cut is important in machining operations due to significant influence of these parameters on machining quality and machining economics. This paper is about optimising these three parameters using heuristic methods. The first heuristic implemented in the paper is a simple Particle Swarm Optimisation (PSO) algorithm, and the second heuristic implemented is a hybrid of Genetic Algorithm and Artificial Immune System (GA-AIS). A multipass turning operation with rough machining and then finish machining is considered in this paper. The objective function considered is minimisation of unit production cost that optimises the machining parameters. The performance of the PSO and GA-AIS are evaluated by comparing with the heuristics reported in the literature using a problem set used by other researchers for this problem. The results are presented in tables and figures and indicates the better performance of GA-AIS over PSO and other heuristics reported in the literature.