Abstract

Tool wear estimation and prediction are keys of maintenance decision-making for milling machine. Various discrete-state degradation models have been developed for tool wear estimation and prediction. However, previous research assume that the number of discrete wear states is fixed based on prior understanding of tool degradation process. To break this limitation, a data-driven approach based on Hierarchical Dirichlet process-Hidden Markov model (HDP-HMM) is proposed. The number of states, transition probability matrix and omission probability distribution of hidden Markov model (HMM) can be automatically updated using observation data through a hierarchical Dirichlet process (HDP). Compared with weighted HMM and Conventional HMM, experiments on real data from high-speed CNC milling machine cutters demonstrates that the proposed approach yielded greater accuracy on tool wear estimation and kept a high reliability in tool life prediction.