Investigation on eXtreme Gradient Boosting for cutting force prediction in milling
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DOI: 10.1007/s10845-023-02243-9
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References listed on IDEAS
- 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.
- 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.
- 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.
- 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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Keywords
Cutting force prediction; Machine learning; Milling; Optimization; XGBoost;All these keywords.
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