Adaptive network based inference system for estimation of flank wear in end-milling

The focus of this paper is to develop a reliable method to predict flank wear during end-milling process. A neural-fuzzy scheme is applied to perform the prediction of flank wear from cutting force signals. In this contribution we also discussed the construction of a ANFIS system that seeks to provide a linguistic model for the estimation of tool wear from the knowledge embedded in the neural network. Machining experiments conducted using the proposed method indicate that using an appropriate maximum force signals, the flank wear can be predicted within 4% of the actual wear for various end-milling conditions.