Multi-Objective Optimization of Turning Process during Machining of AISI 1025 on CNC machine Using Multi-objective particle swarm optimization

Abstract

Parametric optimization of the transformation process is a task of improving multiple objectives. In general, no single set of input parameters can provide the best hardness of the metal and the best finish of the surface at the same time. While when using the genetic algorithm proved that it is possible to obtain more than one goal of the function at the same time. In the current research the use of Multi-objective particle swarm optimization (MOPSO) to determine the best hardness of the metal and less roughness of the surface operator at the same time. A mathematical model was also developed to predict the hardness of the metal and surface roughness using Taguchi technology depending on the input variables (cutting speed, cutting depth and feeding rate). Experiments were conducted on a CNC machine in turning carbon steel AISI 1025 using carbide cutting tool. It was found that the most influential factor on the surface roughness is the feeding  rate, while the most important effect on the hardness is the cutting speed.