PREDICTION OF SURFACE ROUGHNESS IN END-MILLING USING FUZZY LOGIC AND ITS COMPARISON TO REGRESSION ANALYSIS

Abstract

. This study focuses on developing empirical prediction models using regression analysis and fuzzy logic for surface roughness prediction in end-milling. The model considers the following cutting parameters: feed, cutting speed, depth of cut. Two competing data mining techniques, linear regression analysis and fuzzy logic, are used in developing empirical models. The values of surface roughness predicted by these models are then compared with those from measured – representing procedures for validation and comparison of models. In addition, as surface roughness parameters are used 3D surfaces roughness parameters, especially Sa, instead of 2D roughness parameters. This 3D approach gives more precise look at development of surface roughness in end-milling. Further research could be done in implementing these models in CNC adaptive control mechanisms