An Approach on Fuzzy and Regression Modeling for Hard Milling Process

Abstract

This paper proposes the prediction of cutting temperature, tool wear and metal removal rate using fuzzy and regression modeling techniques for the hard milling process. The feed per tooth, radial depth of cut, axial depth of cut and cutting speed were used as process state variables.The experiements were conducted using RSM based central composite rotatable design methodology. Regression and fuzzy modeling were used to evaluate the input – output relationship in the process. It is interesting to observe that the R 2 and average error values for each response are very consistent with small variations were obtained.Also, the confirmation results show that very less relative error varitions. Thus, the developed fuzzy models directly integrated in manufacturing systems to reduce the more computational complexity in the process planning activities.