Multi-objective Optimization of End Milling Process Parameters for Minimum Surface Roughness Using MOGA

Resumo

In this paper, a multi-objective genetic algorithm (MOGA) has been used for the optimization of end milling process parameters for minimum surface roughness and machining time. The regression equation has been generated and used in the multi-objective genetic algorithm tool in MATLAB R2015a. In the present work, an experiment has been carried out on the end milling process of AISI1020 with a tungsten carbide tool by varying cutting speed, feed rate and depth of cut. The responses such as surface roughness and machining time have been measured using a Mitutoyo surface roughness tester and stopwatch, respectively. Optimize the machining parameters for minimum surface roughness and machining time. This study finds the interactive effect of input parameters such as cutting speed, feed and depth of cut on surface roughness. The result achieved by MOGA has been validated through experimentation. A good correlation has been found between MOGA and experimentation. It proves MOGA can be efficiently utilized to optimize end milling process parameters for minimum surface roughness and machining time.