Application of Particle Swarm Optimization technique for achieving desired milled surface roughness in minimum machining time
Face milling is a widely used machining operation to produce various components. The finished component depends not only on the dimensional accuracy but also on the surface finish. The present method of selection of machining parameters by trial and error, previous work experience of the process planner and machining hand books are time consuming and very tedious. There is a need to develop a technique that could able to find the optimal machining parameters for the required surface roughness in machining. In this work, experimental investigations are carried out on aluminium material to study the effect of machining parameters such as cutting speed, feed, and depth of cut on the surface roughness and to obtain the desired surface roughness on face milling process. Mathematical model has been developed for surface roughness prediction using Particle Swarm Optimization (PSO) on the basis of experimental results. The model developed for optimization has been validated by confirmation experiments. Physical constraints for both experiment and theoretical approach are the proposed machining parameters and surface roughness. It has been found that the predicted roughness using PSO is in good agreement with the actual roughness. (C) 2011 Elsevier Ltd. All rights reserved.
particle swarm optimization surface roughness machining time face milling machining parameters