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Fuzzy rule-based neural network for high-speed train manufacturing system scheduling problem

  • S.I.: Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIOT 2021)
  • Published:
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Abstract

Our country has a vast territory, and rail transit is very important to the development of our country's national economy. In this paper, key technologies for a digital twin-based shop floor management and control system are investigated, and the concept is designed and implemented. By adding a digital twin between the business management layer and the production execution layer of the traditional workshop management and control system through the fuzzy rule neural network, a new workshop management and control system architecture on the basis of the virtual is formed, enabling intellectual management and control of the workshop. The results of the study found that the integration of the digital twin into the conventional shop floor management and control system led to changes in the composition, processes and information integration of the management and support system. For the purpose of comparing the system scheduling of the high-speed railway on the basis of the vague rule neural network with the traditional method, we made statistics on the system scheduling before and after the transformation. In terms of manufacturing volume, after the output exceeds 200, the speed of the traditional manufacturing method lags behind the fuzzy rule neural network by nearly 50%.

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Acknowledgements

This work was undertaken with the support of Science and Technology Nova Plan of Beijing City Fengtai District 2020-kjxx202006, and Beijing Nova Program (Z211100002121140).

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Authors and Affiliations

  1. Department of Industrial Engineering, Tsinghua University, Haidian, Beijing, 100084, China

    Fei Peng & Li Zheng

  2. CRRC Academy Company Limited, Fengtai, Beijing, 100070, China

    Fei Peng

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  1. Fei Peng
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  2. Li Zheng
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Correspondence to Fei Peng.

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Peng, F., Zheng, L. Fuzzy rule-based neural network for high-speed train manufacturing system scheduling problem. Neural Comput & Applic 35, 2077–2088 (2023). https://doi.org/10.1007/s00521-022-07190-9

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  • DOI: https://doi.org/10.1007/s00521-022-07190-9

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