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Research on tool wear prediction for milling high strength steel based on DenseNet-ResNet-GRU

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Abstract

Tool condition monitoring is an important basis to ensure workpiece quality and machining efficiency. It is also a key factor in improving machining efficiency, ensuring machining accuracy. Therefore, a new method for predicting tool wear based on DenseNet-ResNet-GRU is proposed. Firstly, statistical theory and an improved wavelet threshold denoising method are used to improve the signal quality. In addition, the asymptotic semi-soft threshold function is applied to reduce the noise of the cutting force signal. Secondly, DenseNet, ResNet, and GRU (gate recurrent unit) deep learning networks are integrated to create a new tool wear prediction model to realize the nonlinear map** relationship between the tool wear amount and the cutting force characteristic. Finally, the tool wear prediction model is verified by high-strength steel experiment. The experimental results verify the accuracy and reliability of the method, which has a better training effect and higher prediction accuracy compared with the CNN-GRU model.

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Acknowledgments

The authors would like to acknowledge National Natural Science Foundation of China (No. 52175394) and the Joint Guidance Project of Heilongjiang Provincial Natural Science Foundation (No. LH2021E083) in the production of this work. The authors are grateful to the anonymous reviewers for valuable comments and suggestions, which helped to improve this study.

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Authors and Affiliations

  1. College of Mechanical and Power Engineering, Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin, 150080, China

    Rui Guan, Yaonan Cheng, Yingbo **, Shilong Zhou, **aoyu Gai & Mengda Lu

  2. Harbin Vocational and Technical College, Harbin, China

    Rui Guan

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  1. Rui Guan
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  2. Yaonan Cheng
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  3. Yingbo **
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  4. Shilong Zhou
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  5. **aoyu Gai
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  6. Mengda Lu
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Corresponding author

Correspondence to Yaonan Cheng.

Additional information

Rui Guan is a doctoral student of Harbin University of Science and Technology. She is also a teacher in Harbin Vocational and Technical College. Her main research focuses on intelligent monitoring technology for cutting tool wear or breakage in cutting difficult-to-machine materials, metal cutting principles and tools.

Yaonan Cheng received his Ph.D. from Harbin University of Science and Technology, China. He is currently a Professor at College of Mechanical and Power Engineering, Harbin University of Science and Technology. His current research focuses on metal cutting theory and tool technology, intelligent manufacturing technology and efficient machining technology for difficult-to-machine materials.

Yingbo ** is a master student of Harbin University of Science and Technology. His main research focuses on intelligent monitoring technology for difficult-to-machine materials, metal cutting principles and tools.

Shilong Zhou is a master student of Harbin University of Science and Technology, Harbin, China. His current research focuses on intelligent monitoring technology for difficult-to-machine materials, metal cutting principles and tools.

**aoyu Gai is a doctoral student of Harbin University of Science and Technology, Harbin, China. His current research focuses on intelligent monitoring technology for difficult-to-machine materials, metal cutting principles and tools.

Mengda Lu is a master student of Harbin University of Science and Technology. His main research focuses on intelligent monitoring technology for difficult-to-machine materials, metal cutting principles and tools.

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Guan, R., Cheng, Y., **, Y. et al. Research on tool wear prediction for milling high strength steel based on DenseNet-ResNet-GRU. J Mech Sci Technol (2024). https://doi.org/10.1007/s12206-024-0632-9

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  • DOI: https://doi.org/10.1007/s12206-024-0632-9

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