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Milling force prediction model based on transfer learning and neural network

Author

Listed:
  • Juncheng Wang

    (Shandong University
    Shandong University, Ministry of Education
    Shandong University)

  • Bin Zou

    (Shandong University
    Shandong University, Ministry of Education
    Shandong University)

  • Mingfang Liu

    (Shanghai Aerospace Equipments Manufacture Co., Ltd)

  • Yishang Li

    (Shandong University
    Shandong University, Ministry of Education
    Shandong University)

  • Hongjian Ding

    (Shandong University
    Shandong University, Ministry of Education
    Shandong University)

  • Kai Xue

    (Shandong University
    Shandong University, Ministry of Education
    Shandong University)

Abstract

In recent years, the growing popularity of artificial neural networks has urged more and more researchers to try introduce these methods to the machining field, with some of them actually producing good results. The acquisition of cutting data often means higher cost and time, limiting the application of neural network in the machining sector, to a certain extent. In this paper, for the task of cutting force prediction, a “transfer network” was established, based on data obtained by simulation, combined with the theory and method in the field of transfer learning. Compared to “ordinary network”, that is, traditional back-propagation neural network based on experimental samples alone, transfer network exhibits obvious performance advantages. On one hand, this means that, using the same experimental samples, the prediction error of transfer network will be controlled; while on the other hand, when the same prediction error is achieved, the number of experimental samples required by the transfer network will be less.

Suggested Citation

  • Juncheng Wang & Bin Zou & Mingfang Liu & Yishang Li & Hongjian Ding & Kai Xue, 2021. "Milling force prediction model based on transfer learning and neural network," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 947-956, April.
    • Handle: RePEc:spr:joinma:v:32:y:2021:i:4:d:10.1007_s10845-020-01595-w
    DOI: 10.1007/s10845-020-01595-w
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    References listed on IDEAS

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    1. Zoran Jurkovic & Goran Cukor & Miran Brezocnik & Tomislav Brajkovic, 2018. "A comparison of machine learning methods for cutting parameters prediction in high speed turning process," Journal of Intelligent Manufacturing, Springer, vol. 29(8), pages 1683-1693, December.
    2. Chujie Tian & Jian Ma & Chunhong Zhang & Panpan Zhan, 2018. "A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network," Energies, MDPI, vol. 11(12), pages 1-13, December.
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    Cited by:

    1. Cinzia Giannetti & Aniekan Essien, 2022. "Towards scalable and reusable predictive models for cyber twins in manufacturing systems," Journal of Intelligent Manufacturing, Springer, vol. 33(2), pages 441-455, February.
    2. Han, Kunlun & Yang, Kai & Yin, Linfei, 2022. "Lightweight actor-critic generative adversarial networks for real-time smart generation control of microgrids," Applied Energy, Elsevier, vol. 317(C).
    3. Aniket Nagargoje & Pavan Kumar Kankar & Prashant Kumar Jain & Puneet Tandon, 2023. "Application of artificial intelligence techniques in incremental forming: a state-of-the-art review," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 985-1002, March.

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