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Chatter Detection Based on ARMAX Model-Based Monitoring Method in Thin Wall Turning Operation

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Intelligent Robotics and Applications (ICIRA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10984))

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Abstract

The ARMAX model-based monitoring method is proposed for chatter detection in thin wall turning operation. ARMAX modelling is deduced to fit the cutting force for the time varying dynamic process caused by the stiffness variation of thin wall workpiece. Residuals closely connected with chatter are extracted and monitored by control charts in real time. Cutting experiments for two different depth of cuts are performed for verification, from which it is found that the ARMAX model-based monitoring process has lower false alarm rate than the typical ARMA modelling. In addition, the model parameters affected by the varying stiffness is also time dependent, so the RELS parameter estimation algorithm is employed. The forgetting factor in the RELS algorithm is optimized through experiments to further reduce the false alarm rate. It is observed that the RELS ARMAX model-based algorithm with forgetting factors between 0.85 and 0.9 has the best monitoring performance with zero false alarms.

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Authors and Affiliations

  1. School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Yang Liu & Zhenhua Xiong

Authors
  1. Yang Liu
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  2. Zhenhua Xiong
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Correspondence to Zhenhua Xiong .

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Editors and Affiliations

  1. University of Newcastle, Callaghan, New South Wales, Australia

    Zhiyong Chen

  2. University of Newcastle, Callaghan, New South Wales, Australia

    Alexandre Mendes

  3. University of Newcastle, Callaghan, New South Wales, Australia

    Yamin Yan

  4. Shenzhen Institutes of Advanced Technology, Shenzhen, China

    Shifeng Chen

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Liu, Y., Xiong, Z. (2018). Chatter Detection Based on ARMAX Model-Based Monitoring Method in Thin Wall Turning Operation. In: Chen, Z., Mendes, A., Yan, Y., Chen, S. (eds) Intelligent Robotics and Applications. ICIRA 2018. Lecture Notes in Computer Science(), vol 10984. Springer, Cham. https://doi.org/10.1007/978-3-319-97586-3_29

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  • DOI: https://doi.org/10.1007/978-3-319-97586-3_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-97585-6

  • Online ISBN: 978-3-319-97586-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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