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Investigation on eXtreme Gradient Boosting for cutting force prediction in milling

Author

Listed:
  • Thomas Heitz

    (Nanjing University of Aeronautics and Astronautics)

  • Ning He

    (Nanjing University of Aeronautics and Astronautics)

  • Addi Ait-Mlouk

    (University of Skövde)

  • Daniel Bachrathy

    (Budapest University of Technology and Economics)

  • Ni Chen

    (Nanjing University of Aeronautics and Astronautics)

  • Guolong Zhao

    (Nanjing University of Aeronautics and Astronautics)

  • Liang Li

    (Nanjing University of Aeronautics and Astronautics)

Abstract

Accurate prediction of cutting forces is critical in milling operations, with implications for cost reduction and improved manufacturing efficiency. While traditional mechanistic models provide high accuracy, their reliance on extensive milling data for force coefficient fitting poses challenges. The eXtreme Gradient Boosting algorithm offers a potential solution with reduced data requirements, yet the optimal utilization of eXtreme Gradient Boosting remains unexplored. This study investigates its effectiveness in predicting cutting forces during down-milling of Al2024. A novel framework is proposed optimizing its precision, efficiency, and user-friendliness. The model training incorporates the mechanistic force model in both time and frequency domains as new features. Through rigorous experimentation, various aspects of the eXtreme Gradient Boosting configuration are explored, including identifying the optimal number of periods for the training dataset, determining the best normalization and scaling technique, and assessing the hyperparameters’ impact on model performance in terms of accuracy and computational time. The results show the remarkable effectiveness of the eXtreme Gradient Boosting model with an average normalized root mean square error of 14.7%, surpassing the 21.9% obtained by the mechanistic force model. Additionally, the machine learning model could capture the runout effect. These findings enable optimized milling operations regarding cost, accuracy and computation time.

Suggested Citation

  • Thomas Heitz & Ning He & Addi Ait-Mlouk & Daniel Bachrathy & Ni Chen & Guolong Zhao & Liang Li, 2025. "Investigation on eXtreme Gradient Boosting for cutting force prediction in milling," Journal of Intelligent Manufacturing, Springer, vol. 36(1), pages 285-301, January.
  • Handle: RePEc:spr:joinma:v:36:y:2025:i:1:d:10.1007_s10845-023-02243-9
    DOI: 10.1007/s10845-023-02243-9
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    References listed on IDEAS

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    1. 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.
    2. E. Traini & G. Bruno & F. Lombardi, 2021. "Tool condition monitoring framework for predictive maintenance: a case study on milling process," International Journal of Production Research, Taylor & Francis Journals, vol. 59(23), pages 7179-7193, December.
    3. Shubham Vaishnav & Ankit Agarwal & K. A. Desai, 2020. "Machine learning-based instantaneous cutting force model for end milling operation," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1353-1366, August.
    4. Hasan Tercan & Tobias Meisen, 2022. "Machine learning and deep learning based predictive quality in manufacturing: a systematic review," Journal of Intelligent Manufacturing, Springer, vol. 33(7), pages 1879-1905, October.
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