Optimization of Machining Parameters in Turning Operation Using PSO and AIS Algorithms: A Survey

In recent manufacturing, the optimization of turning processes is one of important problems which aim to increase competitiveness and product quality. However, the choice of optimal machining parameters is difficult and complex. Traditionally, the selections is heavily relies on trial and error methods which is tedious and unreliable. Metaheuristics methods have been proposed over the last decade to overcome these problems. This paper presents a survey for optimizing the parameters of turning operation using Particle Swarm Optimization (PSO) and Artificial Immune System (AIS). This study deals with different machining performance in turning operation like surface roughness, material removal rate , tool wear , tool life, production cost, machining time and cutting temperature. Most papers in the field of turning parameters optimization are based on (PSO) algorithms, but only a few efforts that are using (AIS) algorithms. In addition, there is a gap of several machining operation parameters especially for cutting temperature optimization in turning operation using PSO and AIS