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Neural Network Approximation Based Multi-dimensional Active Control of Regenerative Chatter in Micro-milling

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Advances in Neural Networks – ISNN 2016 (ISNN 2016)

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Abstract

In this paper, an active control approach with the employments of two piezoelectric actuators, the Neural Networks (NNs) as approximators and the Lyapunov-Krasovskii functional which is used to deal with the time delayed tool vibrations is investigated for suppressing the 2-dof regenerative chatter in micro-milling. A dynamic model of micro-milling process and corresponding controlled system are established. Simulations are presented to validate the control performances of developed control approach.

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Author information

Authors and Affiliations

  1. College of Automation Science and Engineering, South China University of Technology, Guangzhou, 510641, China

    Xiaoli Liu, Chun-Yi Su & Zhijun Li

  2. Department of Mechanical and Industrial Engineering, Concordia University, Montréal, QC, H3B 1R6, Canada

    Chun-Yi Su

Authors
  1. Xiaoli Liu
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  2. Chun-Yi Su
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  3. Zhijun Li
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Corresponding author

Correspondence to Chun-Yi Su .

Editor information

Editors and Affiliations

  1. The Chinese Academy of Sciences, Beijing, China

    Long Cheng

  2. Huazhong University of Science and Tech., Wuhan, Jiangsu, China

    Qingshan Liu

  3. Russian Academy of Sciences, SPIIRAS, St. Petersburg, Russia

    Andrey Ronzhin

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© 2016 Springer International Publishing Switzerland

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Liu, X., Su, CY., Li, Z. (2016). Neural Network Approximation Based Multi-dimensional Active Control of Regenerative Chatter in Micro-milling. In: Cheng, L., Liu, Q., Ronzhin, A. (eds) Advances in Neural Networks – ISNN 2016. ISNN 2016. Lecture Notes in Computer Science(), vol 9719. Springer, Cham. https://doi.org/10.1007/978-3-319-40663-3_29

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  • DOI: https://doi.org/10.1007/978-3-319-40663-3_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-40662-6

  • Online ISBN: 978-3-319-40663-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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