MODELLING OF SURFACE ROUGHNESS IN TURNING OF LEADED GUN METAL (BS 1400:LG2) USING REGRESSION,NEURAL NETWORK AND PARETO-ANOVA ANALYSIS

Abstract

Turning is one of the most widely used metal cutting processes. The increasing importance of turning operations is gaining new dimensions in the present industrial age, in which the growing competition calls for all the efforts to be directed towards the economical manufacture of machined parts as well as surface finish is one of the most critical quality measure in mechanical products. In present work, a neural network is adopted to construct a prediction model for surface roughness. Once the process parameters (cutting speed, feed, and depth of cut) are given, the surface roughness can be predicted. The work piece material is Leaded Gun Metal (BS 1400:LG2) which is processed by carbide-inserted tool. All the experiment work is conducted on CNC lathe. The experiments carried out by using an L-27 orthogonal array. Regression analysis is done considering Linear as well as Non-Linear model. A neural network model is generated in MATLAB to verify the accuracy of the regression analysis...