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Nonlinear time-series modeling of feed drive system based on motion states classification

Author

Listed:
  • Yakun Jiang

    (Huazhong University of Science and Technology)

  • Jihong Chen

    (Huazhong University of Science and Technology)

  • Huicheng Zhou

    (Huazhong University of Science and Technology)

  • Jianzhong Yang

    (Huazhong University of Science and Technology)

  • Guangda Xu

    (Huazhong University of Science and Technology)

Abstract

This paper proposed a novel modeling method using the running process data, i.e., the reference input positions and the actual output positions, based on the Naïve Bayes method and a nonlinear autoregressive long-short term memory network (i.e., the NAR-LSTM) to address the nonlinear time-series modeling problem for increasing the prediction accuracy of the model of CNC machine feed drive system. A Naïve Bayes based motion states classifier (i.e., the NB-MSC) is proposed to automatically classify the motion states with knowledge of dynamic characteristics of the feed drive system for constructing the submodels of different motion states (startup state, reverse state, etc.). In addition, a model based multi-objective optimization method is presented to extract samples for the training of the NB-MSC. Then by modifying the basic long-short term memory (LSTM) network, the NAR-LSTM network is proposed to construct those submodels. Compared to the existing modeling methods via dynamic analysis, the proposed method is better because it can achieve higher prediction accuracy on highly nonlinear motion states such as the reverse state. To validate the proposed methods, a set of experiments are conducted to prove the feasibility of the feed drive model as well as the advantages of the NB-MSC and the NAR-LSTM network in improving the modeling performance.

Suggested Citation

  • Yakun Jiang & Jihong Chen & Huicheng Zhou & Jianzhong Yang & Guangda Xu, 2020. "Nonlinear time-series modeling of feed drive system based on motion states classification," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1935-1948, December.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:8:d:10.1007_s10845-020-01546-5
    DOI: 10.1007/s10845-020-01546-5
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    References listed on IDEAS

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    1. Congbo Li & Lingling Li & Ying Tang & Yantao Zhu & Li Li, 2019. "A comprehensive approach to parameters optimization of energy-aware CNC milling," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 123-138, January.
    2. G. Shao & A. Brodsky & R. Miller, 2018. "Modeling and optimization of manufacturing process performance using Modelica graphical representation and process analytics formalism," Journal of Intelligent Manufacturing, Springer, vol. 29(6), pages 1287-1301, August.
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    Cited by:

    1. Chi Ma & Hongquan Gui & Jialan Liu, 2023. "Self learning-empowered thermal error control method of precision machine tools based on digital twin," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 695-717, February.

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