Hybrid SVM/HMM Method for Tool Wear Intelligence Measure in Cutting Process

A new method of tool wear intelligence measure based on Support Vector Machine(SVM) and Hidden Markov Models (HMM) is proposed to monitor tool wear and to predict tool failure. At first, FFT features are extracted from the model signal of the tool in cutting process, then FFT vectors are introduced to SVM-HMM for machine learning and classification. The signal of Tool wear in cutting process is introduced to the HMM-SVM model. The results show that the method is effective. The performance of the proposed method is compared with that of HMM based method, which shows the performance increase in both recall and precision of tool wear.