A Method of State Recognition in Machining Process Based on Wavelet and Hidden Markov Model

:The state recognition in machining process, especially recognition of chatter is very important for mechanical manufacturing process. In order to avoid processing chatter effectively, a method based on wavelet packet analysis and hidden Markov model(HMM) is proposed for state recognition in machining process. Wavelet packet decomposition, which can image the information in the different frequency band,is applied as the method of feature extraction. The normalized root mean square (RMS) values of the wavelet packet coefficients in different frequency bands were taken as the observation sequence vector. The method of HMM pattern recognition was used to recognize states of machining process. Based on choosing the suitable standard samples of different states,this method can correctly recognize the test samples states of machining process after being trained by the standard samples. Results of experiment showed that the proposed method is suitable for recognition implementation in machining process.