MODELLING AND ANALYSIS OF SURFACE ROUGHNESS FOR BEARING METAL TURING

Abstract

Surface roughness of a machined product can affect several of the product’s functional attributes such as surface friction between mating parts, wear, heat transmission, ability of distributing and holding a lubricant, coating and resisting fatigue. In this study modelling and analysis of surface roughness of bearing material are done. Aluminium bronze is used as a bearing material because of its availability and common use in journal bearing, where surface roughness may play a major role in its functionality. Both external turning and internal (boring) turning are performed on CNC lathe using cutting speed, feed rate and depth of cut as the process parameters. The values for process parameters of external turning and internal are similar. Design of experiments (DOE) is used to obtain the optimum surface roughness. Response surface methodology (RSM) and artificial neural network (ANN) are implemented to model the surface roughness of external turning and internal turning. Regression models are developed for external turning and internal turning by using full block central composite design and analysis of experimental results are done by using analysis of variance (ANOVA). Predicted values of surface roughness of external turning and internal turnings are compared with experimental values, which show high accuracy.