Use of Hybrid Algorithm for Surface Roughness Optimization in Ti-6Al-4V Machining

Abstract

In this study, an effort has been made to develop a hybrid optimization algorithm based on Particle Swarm Optimization (PSO), a widely used optimization technique and Bat Algorithm (BA) a newly introduced metaheuristic algorithm. The machining parameters namely cutting speed (V c ), feed rate (f), depth of cut (d) along with tool wear (VB) and cutting tool vibrations (V y ) are the inputs. Further a Hybrid PSO-BA algorithm has been implemented in order to optimize surface roughness in High Speed Turning (HST) of Ti-6Al-4V. It was observed that the proposed hybrid PSO-BA algorithm increases the accuracy and convergence by 1.92% and 4.17% for R a and R t , which are surface roughness parameters compared to BA. Validation experiments have been performed based on PSO-BA predicted values. The experimental values are close to the predictive values with an error of 0.47% for R a and 1.159% for R t which is considerably better than that obtained from BA only.