Evolutionary Techniques for Process Modelling and Optimization in Turning Ti-6al-4v Alloy

Abstract

Turning of Ti6Al4V alloy presents a great challenge and opportunity for the machinist. In this paper, multi-target and process modelling advancement of a machining parameters in plain turning of Ti6Al4V alloy are presented using two evolutionary approaches namely Gene Expression Program (GEP) and Non-Dominated Sorting Genetic Algorithm II (NSGA II).The three controlling factors in turning namely, speed (N), feed rate (f)and depth of cut (Dc) are designed as a input parameters, while Material Removal Rate (MRR) and Surface Roughness(Ra) are the measured outputs. The data used in the GEP model is taken by doing several turning experiments within the experimental domain. As the responses MRR and Ra are conflicting in nature, so that NSGA-II has been used as it is a multi-objective optimization technique to obtain the optimal solutions.