Cutting Chatter Monitoring Using Hidden Markov Models

Monitoring of cutting chatter in metal cutting process is a very important economical consideration in automated manufacturing. However, the metal cutting process is a complicated process. The cutting chatter is still an unsolved problem in metal cutting process. In this paper, a new method for cutting chatter monitoring is developed. First, it uses fast Fourier transform (FFT) to process the monitoring signals of the cutting process and to extract the feature vectors. Then, it uses the Hidden Markov Model (HMM) as the classifiers to recognize the cutting chatter. The experimental results show that the proposed method is feasible and effective.