Abstract:Aiming at the problem of the identification of cutting tool wear condition, and according to the merit of Hidden Markov Model (HMM) with perfect classifying ability, the system of the tool wear condition monitoring based on continuous Gaussian mixture HMM (CHMM) was presented. With the milling force as the monitoring signal, the wavelet package theory was adopted to analyze and de noise the milling force signal, and extracts energy feature of the signal as import vectors of CHMM, trains CHMM.Then the unknown state of tool wear was monitored and identified using the trained model. The results of the experiment indicated that the model can exactly recognize the tool wear state and need small training samples, has significant realistic meaning to tool intelligent monitoring.