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Tool remaining useful life prediction and parameters optimization in milling 508III steel

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Abstract

Tool remaining useful life prediction (RUL) and parameters optimization are very important for the normal operation of the machining system, the full use of the tool excellent performance, and the tool health management. This paper proposes a RUL prediction based on WOA-optimized XGBoost with multidomain feature fusion. Based on the milling 508III experiment, Kernel principal component analysis (KPCA) is used for multi-domain sensitive feature dimensionality reduction fusion. The whale optimization algorithm (WOA) is used to optimize the extreme gradient lifting integrated learning (XGBoost) method, and a WOA-XGBoost correlation model integrating features and tool life evolution is established to predict the RUL. The effectiveness and reliability are verified by comparison with other methods and milling experiments. A parameter optimization method combining multi-objective genetic algorithm and multi-attribute decision-making is proposed. Based on tool life and cutting force experiments, the multi-objective cutting parameter optimization problem is modeled to provide a theoretical basis for cutting parameter optimization. Taking tool life, cutting efficiency, and cutting force as evaluation indicators, the optimal cutting parameter combination that can be accepted by all optimization objectives is obtained. Results show that the prediction performance of the RUL prediction method proposed is better compared with other ensemble learning methods adopted in this paper. Based on the limited experimental data, the RUL can be predicted effectively and reliably. Through the parameter optimization method proposed, the acceptable optimal combination is: the cutting speed is 284.78 m/min, feed per tooth is 0.3 mm/z, and axial cutting depth is 1.999 mm. These findings may provide theoretical basis and technical support for promoting the intelligent development of tool status warning and cutting process optimization in machining.

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Acknowledgements

Besides, the authors are grateful to the anonymous reviewers for valuable comments and suggestions, which helped to improve this study.

Funding

This work was financially supported by National Natural Science Foundation of China (No. 52175394) and Joint Guidance Project of Heilongjiang Provincial Natural Science Foundation (No. LH2021E083).

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Authors and Affiliations

  1. The Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin, 150080, People’s Republic of China

    Xiaoyu Gai, Yaonan Cheng, Rui Guan, Yingbo Jin, Mengda Lu, Shilong Zhou & Jing Xue

  2. School of Mechanical and Power Engineering, Harbin University of Science and Technology, No. 52, Xuefu Road, Nangang District, P.O. BOX 92, 150080, Harbin, People’s Republic of China

    Yaonan Cheng

Authors
  1. Xiaoyu Gai
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  2. Yaonan Cheng
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  4. Yingbo Jin
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  5. Mengda Lu
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  6. Shilong Zhou
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  7. Jing Xue
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Contributions

Study conception and design: Yaonan Cheng, Xiaoyu Gai.

Drafting of manuscript: Xiaoyu Gai.

Method proposed and interpretation of data: Xiaoyu Gai, Rui Guan.

Acquisition and analysis of data: Yingbo Jin, Mengda Lu, Shilong Zhou, Jing Xue.

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Correspondence to Yaonan Cheng.

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Gai, X., Cheng, Y., Guan, R. et al. Tool remaining useful life prediction and parameters optimization in milling 508III steel. Int J Adv Manuf Technol 129, 1741–1757 (2023). https://doi.org/10.1007/s00170-023-12310-3

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  • DOI: https://doi.org/10.1007/s00170-023-12310-3

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