ANALYSIS OF SURFACE ROUGHNESS FOR TURNING OF ALUMINIUM (6061) AND BRASS (IS 319, GRADE I) USING REGRESSION ANALYSIS AND NEURAL NETWORK

Abstract

Surface roughness and tolerances are among the most critical quality measures in many mechanical products. As competition grows closer, customers now have increasingly high demands of quality, making surface roughness become one of the most competitive dimensions in today's manufacturing industry. Surfaces of a mechanical product can be created with a number of manufacturing processes. This work applies the fractional factorial experimentation approach to studying the impact of turning parameters on the roughness of turned surfaces. Analysis of variances is used to examine the impact of tu models to predict the surface roughness of a machined work piece. Other objectives of this study are: 1. To develop prediction models. 2. Comparing the surface roughness values obtained by regression analysis and neural network analysis. rning factors and factor interactions on surface roughness. A considerable number of studies have studied the effects of the speed, feed and depth of cut on t...