Determining the Effect of Cutting Parameters on Surface Roughness Using Genetic Algorithm

Abstract

The aim of present research focuses on the prediction of machining parameters that improve the quality of surface finish. The surface  roughness is one of the important properties of work piece quality in the CNC (Computer Numerical Control) turning process. An effective approach of optimization techniques genetic algorithm (GA) and response surface
methodology (RSM) was implemented to investigate the effect of the  cutting parameters such as cutting speed, feed rate, and depth of cut on the surface roughness. In this study, the surface roughness is measured during turning operation at different cutting parameters such as speed, feed, and depth of cut on Alumunium 6063 using coated carbide tool. The
second order mathematical model is developed using RSM of central  composite method to predict the surface roughness standards. The  regression equation is solved using genetic algorithm approach for   optimizing the cutting parameters for minimizing surface roughness, this study attempts the application of GA technique using Matlab 8.0 is  recommends 1.512ìm as the best minimum predicted surface roughness value for the optimal solution of the cutting conditions was 80 m/min, 0.18 mm/rev,0.3mm.