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A novel method based on deep transfer learning for tool wear state prediction under cross-dataset

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Abstract

With the wide application of deep learning in industry, the use of deep learning methods to predict tool wear has emerged as a hot research topic in recent years. However, tool wear prediction based on deep learning is usually for the same type of tools under the same operating condition. Accurately predicting tool wear under different tools and operating conditions can optimize machining performance, given the complex and varied machining conditions in practice. In this paper, a novel method based on deep transfer learning for tool wear state prediction under cross-dataset is proposed. Firstly, a multidimensional pixel convolutional neural network (MP-CNN) is proposed to extract the features of ponderous raw industrial data. Subsequently, MP-CNN and a model called Sample Convolution and Interaction Neural Network (SCINet) form a pre-trained network. The network relies on pre-training to learn robust data representations, which can be fine-tuned for specific tasks. Finally, the parameters of the SCINet model are transferred for fine-tuning the tool wear prediction model on the target dataset, which is demonstrated and validated using the IEEE PHM 2010 challenge dataset as the source dataset and the UC Berkeley Milling Dataset as the target dataset. The experimental results show that the proposed method can perform superior tool wear state prediction under cross-dataset conditions.

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Data availability

The acquisition website of these data sets is provided in this study.

Code availability

It declares that codes are not available for this research.

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Funding

This study was supported by National Natural Science Foundation of China (No. 62073312), Applied Basic Research Program of Liaoning Province (2023JH2/101300148, 2023JH2/101300228, and 2022JH2/101300207), and Natural Science Foundation of Liaoning Province (2022-MS-033).

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Authors and Affiliations

  1. Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, People’s Republic of China

    Yifan Wang, Gao Jie, Wei Wang, Jinsong Du & Xu Yang

  2. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, People’s Republic of China

    Yifan Wang, Gao Jie, Wei Wang, Jinsong Du & Xu Yang

  3. University of Chinese Academy of Sciences, Beijing, 100049, People’s Republic of China

    Yifan Wang, Gao Jie, Wei Wang, Jinsong Du & Xu Yang

  4. Key Laboratory On Intelligent Detection and Equipment Technology of Liaoning Province, Shenyang, 110179, People’s Republic of China

    Yifan Wang, Gao Jie, Wei Wang, Jinsong Du & Xu Yang

Authors
  1. Yifan Wang
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  2. Gao Jie
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  3. Wei Wang
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  4. Jinsong Du
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  5. Xu Yang
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Contributions

YW: methodology, experiment, software, and writing-original draft. JG: supervision, project administration, funding acquisition, and writing-review and editing. WW: validation and investigation. JD: data curation. XY: visualization.

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Correspondence to Gao Jie.

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Wang, Y., Gao, J., Wang, W. et al. A novel method based on deep transfer learning for tool wear state prediction under cross-dataset. Int J Adv Manuf Technol 131, 171–182 (2024). https://doi.org/10.1007/s00170-024-13055-3

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