Prediction of Cutting Chatter Based on Hidden Markov Model


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Self-chatter is a serious problem in cutting process. This paper aims to solve the problem by establishing time series model of vibration acceleration signal in cutting process based on Hidden Markov Model (HMM) technology and achieve the purpose of chatter recognition and prediction. Features which can indicate cutting state are extracted from the acceleration signal. HMM parameters are obtained by model training, and the reference models database is built. Then cutting state recognition is performed according to the feature matching level. Simulations and experiments are conducted, and the results show that the proposed method is feasible and it could get high recognition



Key Engineering Materials (Volumes 353-358)

Edited by:

Yu Zhou, Shan-Tung Tu and Xishan Xie




Z. H. Yao et al., "Prediction of Cutting Chatter Based on Hidden Markov Model", Key Engineering Materials, Vols. 353-358, pp. 2712-2715, 2007

Online since:

September 2007




[1] L.Y. Fu, J.Y. Yu: Journal of Jilin University Of Technology, Vol. 3 (1997), p.109 (In Chinese).

[2] J. H. Xie: Hidden Markov Model and Application in Speech Recognition (Huazhong University of Technology and Science Press, Wuhan 1995). (In Chinese).

[3] L. Rabiner, B.H. Juang: Fundamentals Of Speech Recognition (Prentice Hall PTR, New Jersey 1993).

[4] Y. Linde, A. Buzo, R.M. Grey: IEEE Trans. Communication, COM-28 (1980), p.84.

[5] R. I. A. Davis, B. C. Lovell, in: Proceedings of 16th International Conference on Pattern Recognition, Quebec City, Canada (2002), p.168.

[6] X. D. Hoang, J. Hu, in: Proceedings of 12th IEEE International Conference on Networks (2004), vol. 2, p.470 recognition state cutting state.

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