Multi-objective Optimization of Turning Process During Machining of AlMg1SiCu Using Non-dominated Sorted Genetic Algorithm

Abstract Parametric optimization of turning process is a multi-objective optimization task. In general no single combination of input parameters can provide the best material removal rate and the best surface finish simultaneously. Genetic algorithm has been proven as one of the most popular multi-objective optimization techniques for the parametric optimization of conventional machining processes. In this study non-dominated sorted genetic algorithm has used to optimize the process parameters. Aim of the present study was to develop empirical models for predicting material removal rate and surface roughness in terms of spindle speed, feed rate and depth of cut using multiple regressions modeling method. Experiments were carried out on NC controlled machine tool by taking AlMg1SiCu as workpiece material and carbide inserted cutting tool. Finally, a non-dominated sorted genetic algorithm has been employed to find out the optimal setting of process parameters that simultaneously maximize material removal rate and minimize surface roughness. The set of Pareto-optimal front provides flexibility to the manufacturing industries to choose the best setting depending on applications.