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Cutting force prediction between different machine tool systems based on transfer learning method

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Abstract

Milling force can generally be predicted from orthogonal cutting data. However, the accuracy of the predicted result is largely affected by the dynamic properties of the machine tool system, and a large number of repeated milling tests are required for the different types of machine tool to reduce the prediction error. A data-driven method, which could predict the cutting force of different machine tools through only a small number of tests, is proposed in this paper based on the transfer learning method. First, the cutting force coefficients are obtained through orthogonal cutting test. Then, the influence of the dynamic properties of the machine tool system is considered by introducing a correction coefficient to improve the conversion accuracy from orthogonal cutting to helical milling process. After obtaining the cutting force of the source machine tool with sufficient cutting data, the regional adaptive method combining with neural network is utilized to predict the cutting force of the target machine tool with only a small amount cutting data. Finally, a series of milling tests are carried out on different machine tools to verify the accuracy of the proposed method. The results indicate that the developed method can predict the milling force with a high accuracy.

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All data generated or analyzed during this study are included in this published article.

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Funding

This study is supported by the National Natural Science Foundation of China (No. 52005413) and the Fundamental Research Funds for the Central Universities (Grant No. 3102021bzb004) and the Fundamental Research Funds for the Central Universities (No.D5000220135).

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Authors and Affiliations

  1. Ministry of Education, Engineering Research Center of Advanced Manufacturing Technology for Aero Engine (Northwestern Polytechnical University), Xi’an, 710072, People’s Republic of China

    Xi Chen, Zhao Zhang, Qi Wang, Dinghua Zhang & Ming Luo

  2. Ministry of Industry and Information Technology, Key Laboratory of High Performance Manufacturing for Aero Engine (Northwestern Polytechnical University), Xi’an, 710072, People’s Republic of China

    Xi Chen, Zhao Zhang, Qi Wang, Dinghua Zhang & Ming Luo

Authors
  1. Xi Chen
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  2. Zhao Zhang
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  3. Qi Wang
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  4. Dinghua Zhang
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  5. Ming Luo
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Contributions

Xi Chen wrote the paper and carried out the experiments; Zhao Zhang designed the framework of the paper and provided data analysis; Dinghua Zhang contributed to the main idea of the paper; Ming Luo provided the experimental condition; Qi Wang helped with the experiments.

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Correspondence to Zhao Zhang.

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Chen, X., Zhang, Z., Wang, Q. et al. Cutting force prediction between different machine tool systems based on transfer learning method. Int J Adv Manuf Technol 121, 619–631 (2022). https://doi.org/10.1007/s00170-022-09316-8

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  • DOI: https://doi.org/10.1007/s00170-022-09316-8

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