• 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