DocumentCode :
3482024
Title :
Pattern recognition of chatter gestation based on SVM — HMM method
Author :
Shao, Qiang ; Shao, Cheng ; Qiang Shao ; LiNa, Guan
Author_Institution :
Inst. of Adv. Control Technol., Dalian Univ. of Technol., Dalian, China
fYear :
2009
fDate :
5-7 Aug. 2009
Firstpage :
1489
Lastpage :
1494
Abstract :
To distinguish chatter gestation, a new method of chatter gestation based on HMM-SVM method is proposed for dynamic patterns of chatter gestation in cutting process. At first, FFT features are extracted from the model signal of cutting process, then FFT vectors are introduced to HMM-SVM (hidden Markov model-support vector machine) for machine learning and classification. the vibration signal of cutting process is introduced to the HMM-SVM model. Finally, the results of chatter gestation recognition and chatter prediction experiments are presented and show that the method proposed is executable and effective.
Keywords :
cutting; fast Fourier transforms; feature extraction; hidden Markov models; learning (artificial intelligence); machining chatter; vibrations; FFT; HMM-SVM method; chatter gestation; cutting process; feature extraction; hidden Markov model; machine learning; pattern recognition; support vector machine; vibration signal; Automation; Feature extraction; Hidden Markov models; Pattern recognition; Predictive models; Signal processing; Support vector machine classification; Support vector machines; Training data; Vibrations; HMM; SVM; chatter gestation; pattern recognition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Automation and Logistics, 2009. ICAL '09. IEEE International Conference on
Conference_Location :
Shenyang
Print_ISBN :
978-1-4244-4794-7
Electronic_ISBN :
978-1-4244-4795-4
Type :
conf
DOI :
10.1109/ICAL.2009.5262734
Filename :
5262734
Link To Document :
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