Optimization of Surface Roughness of AlMg1SiCu in Turning Operation Using Genetic Algorithm

Genetic algorithm has been proven as one of the most popular optimization techniques for the parametric optimization of conventional machining processes. In this study genetic algorithm has used to optimize the process parameters. Aim of the present study was to develop empirical model for predicting surface roughness in terms of spindle speed, feed rate and depth of cut using multiple regressions modelling method. Experiments were carried out on NC controlled machine tool by taking AlMg1SiCu as workpiece material and carbide inserted cutting tool. Finally, genetic algorithm has been employed to find out the optimal setting of process parameters that optimize surface roughness. This provides flexibility to the manufacturing industries to choose the best setting depending on applications.