Genetic Algorithm for optimizing cutting conditions of uncoated carbide (WC-Co) in milling machining operation

Abstract

This paper presents the capability of Genetic Algorithm (GA) technique in obtaining the optimal machining parameters for uncoated carbide (WC-Co) tool to minimize the surface roughness (Re) value in milling process. The optimal machining parameters are generated using MATLAB Optimization toolbox. Regression technique is applied to create the surface roughness predicted equation to be taken as a fitness function of the GA. Result of this study indicated that the GA technique capable to estimate the optimal cutting conditions that yields to the minimum R(a) value. With high speed, low feed and high radial rake angle of the cutting conditions rate, GA technique recommended 0.17533 mu m as the best minimum predicted surface roughness value. Consequently, the GA technique has decreased the minimum surface roughness value of the experimental data by about 25.7%.