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论文类型：会议论文

发表时间：2009-08-05

收录刊物：EI、CPCI-S、Scopus

页面范围：1563-+

关键字：Tool wear; Intelligence Measure; Discrete Hidden Markov Model (DHMM); Pattern Recognition

摘要：A method of tool wear intelligence measure based on Discrete Hidden Markov Models (DHMM) is proposed to monitor tool wear and to predict tool failure. FFT features are first extracted from the vibration signal and cutting force in cutting process, and then FFT vectors are presorted and converted into integers by SOM. Finally, these codes are introduced to DHMM for machine learning and 3 models for different tool wear stage are built up. Pattern of HMM is recognised by calculating probability. The results of tool wear intelligence measure and pattern recognition of tool wear experiments show that the method is effective.