Paper
13 October 2006 Hybrid HMM/SVM method for predicting cutting chatter
Yongtao Jiang, Chunliang Zhang
Author Affiliations +
Proceedings Volume 6280, Third International Symposium on Precision Mechanical Measurements; 62801Q (2006) https://doi.org/10.1117/12.716150
Event: Third International Symposium on Precision Mechanical Measurements, 2006, Urumqi, China
Abstract
According to the properties of cutting chatter, a new chatter forecast system has been developed based on Hidden Markov Model (HMM) and Support Vector Machine (SVM). This system uses HMM as the recognition method and SVM as the prediction method. At the same time, means like wavelet package decomposition are also employed to extract the cutting features. The basic idea and general steps of this method are as follow. Firstly, the cutting signals are analyzed step by step in the same interval using wavelet packet decomposition. Secondly, the energy in every spectrum section are calculated and scaled in order to get general property. As a result, the energy distribution information and energy transition curve of different spectrum section can be retrieved. Then, SVR algorithm is applied to predict the trend of energy transition. The results, after scalar quantized, at last are input into HMMs to determine whether in chatter period. Certainly, current state still needs to be distinguished. The simulation results indicate that the new predicting method has good discriminating performances and high forecast accuracy.
© (2006) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yongtao Jiang and Chunliang Zhang "Hybrid HMM/SVM method for predicting cutting chatter", Proc. SPIE 6280, Third International Symposium on Precision Mechanical Measurements, 62801Q (13 October 2006); https://doi.org/10.1117/12.716150
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Cited by 6 scholarly publications.
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KEYWORDS
Quantization

Signal processing

Wavelets

Neural networks

Process modeling

Wavelet packet decomposition

Signal analyzers

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