Adaptive neuro-fuzzy modeling of thermal voltage parameters for tool life assessment in face milling

Abstract

The focus of this paper is to develop a reliable procedure to predict tool life during face milling process. This procedure involves a combination of Method of Least Squares and Neuro Fuzzy system. The factorial designs combined with the ANFIS techniques were applied to perform the prediction of thermal voltage. A least-squares linear regression is applied to perform the prediction of tool life from thermal-voltage signals. In this contribution we also discussed the construction of an ANFIS system that tends to provide a linguistic model for the estimation of thermal voltage obtained with different membership functions. This research focuses on developing ANFIS models using triangular and Gaussian membership functions. The work shows that the membership functions have the dominant effect among the on the accuracy model. The results indicate that the training of ANFIS with the Gaussian membership function obtains a higher accuracy rate in the prediction of thermal voltage, respectively tool life.