Feature-filtered fuzzy clustering for condition monitoring of tool wear

Condition monitoring is of vital importance in order to assess the state of tool wear in unattended manufacturing. Various methods have been attempted, and it is considered that fuzzy clustering techniques may provide a realistic solution to the classification of tool wear states. Unlike fuzzy clustering methods used previously, which postulate cutting condition parameters as constants and define clustering centres subjectively, this paper presents a fuzzy clustering method based on filtered features for the monitoring of tool wear under different cutting conditions. The method uses partial factorial experimental design and regression analysis for the determination of coefficients of a filter, then calculates clustering centres for filtering the effect of various cutting conditions, and finally uses a developed mathematical model of membership functions for fuzzy classification. The validity and reliability of the method are experimentally illustrated using a CNC machining centre for milling.