Comparative Study of Estimated Surface Roughness Using GA and PSO Techniques for Milling of Thin-Walled Structures

Abstract

Thin-walled structures, due to their lightweight, have found significant applications in the aerospace industry. For the manufacturing of any component, its surface quality index is of prime importance. A very well-known measure of this surface quality is surface roughness. For a product of high quality, the surface roughness value is often desired to be minimum. However, the machining parameters for the production of such surfaces often rely on the engineer's experience and expertise, which always do not lead to the best possible results. In this study, a neural network was first created for surface roughness estimation, then evolutionary algorithms such as Genetic Algorithm and Particle Swarm Optimization were used to minimize the surface roughness value. During this process, the impact of milling parameters such as rake angle, nose radius, and approach angle on the surface roughness value was also studied with the aid of surface plots of surface roughness developed by taking two parameters at a time and holding the third parameter constant.