Optimization of Milling Operation Using Genetic and PSO Algorithm

Abstract

Metal cutting is one of the important and widely used manufacturing processes in engineering industries. Optimizing the machining parameters has become an essential one in order to be competitive and to meet customer demands quickly. For this purpose several optimization techniques are used. Among those techniques Particle Swarm Optimization and Genetic Algorithm is used in this paper because of its better ability. A genetic algorithm (GA) is a search heuristic that mimics the process of natural evolution. This heuristic is routinely used to generate useful solutions to optimization and search problems. Genetic algorithms belong to the larger class of Evolutionary Algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover. Particle Swarm Optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. Such methods are commonly known as metaheuristics as they make few or no assumptions about the problem being optimized and can search very large spaces of candidate solutions. These techniques are used to optimize the machining parameters like depth of cut, feed rate and cutting speed. This will help in better optimization of milling operation. The developed techniques are evaluated with a case study.