Particle Swarm Optimization for Milling Titanium Alloy

Abstract

ABSTRACTOptimum machining parameters are of great concern in manufacturing environments, where economy of machining operation plays a key role in competitiveness in the market. Many researchers have dealt with the optimization of machining parameters for milling operations. In this paper, optimization procedures based on particle swarm optimization algorithm are developed for find machining parameters in milling operation. It describes development and utilization of the methodology that determines optimum Pareto’s front analyzing feed, speed and depth for milling operation. The relationships between machining parameters and the performance measures of interest are obtained by using experimental data and a swarm intelligent neural network system. Results show that particle swarm optimization is an effective method for solving multi-objective optimization problems, and also, that an integrated system of neural networks and swarm intelligence can be used to solve complex machining optimization problems.