Optimization of Cutting Parameters for Surface Roughness in the Ball-End Milling Process Using Genetic Algorithm

Abstract

The aim of this is to demonstrate the possibilities of applying a genetic algorithm to optimize the input parameters of the ball-end milling process when machining hardened steels as a function of the minimum surface roughness. The experimental investigations were carried out using a four-factor experimental design. RSM was used to determine the basic relationship between the input parameters of the process (spindle speed, feed per tooth, axial and radial depth) and the surface roughness. The developed second-order model was used as a reference model for the GA application. The obtained GA model of surface roughness was a function of the goal of the genetic algorithm, which required finding a minimum value of surface roughness Ra. Based on certain optimal values of the input parameters, a confirmation experiment was performed. The measured value of the surface roughness showed a good agreement with the value obtained by GA. The results obtained show the efficiency of the GA application for modeling and optimization of ball-end milling processes.