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An online prediction method for array antenna assembly performance based on digital twin

Author

Listed:
  • Xuepeng Guo

    (Nanjing University of Science and Technology)

  • Linyan Liu

    (Nanjing University of Science and Technology)

  • HuiFen Wang

    (Nanjing University of Science and Technology)

  • Yue Li

    (Nanjing University of Science and Technology)

  • XiaoDong Du

    (China Electronics Technology Group Corporation)

  • JianCheng Shi

    (China Electronics Technology Group Corporation)

  • Yue Wang

    (Chongqing University of Posts and Telecommunications)

Abstract

In the actual array antenna assembly process, changes in the assembly process parameters strongly impact the solder joints. Thus, it is always necessary to pay attention to the stress‒strain situation of solder joints to avoid their failure and affecting the overall performance of the resulting antenna products. In this paper, based on digital twin and cloud-edge collaboration technology, an online prediction method for array antenna assembly performance based on digital twinning and cloud-edge collaboration technology is proposed. First, key assembly process parameters such as the tightening torque, tightening sequence and part flatness of the array antenna are used as simulation inputs to carry out the finite element simulation. The mapping relationships between the assembly process parameters and assembly performance are determined, and simulation data samples are constructed. Then, a prediction method that combines ensemble learning and a BP neural network is proposed for assembly performance prediction of array antennas, and an assembly performance prediction model is constructed to realize the online prediction of assembly performance. The results show that the prediction is as expected. Finally, a digital twin-cloud-edge collaborative antenna assembly process performance prediction system is constructed, and real-time monitoring of antenna assembly performance is performed when applied to an array antenna assembly process. The instance validation showed that the developed online prediction model has an average accuracy of 94.35%. This real-time monitoring method can ensure critical solder joint performance and improve the success rate of antenna assembly.

Suggested Citation

  • Xuepeng Guo & Linyan Liu & HuiFen Wang & Yue Li & XiaoDong Du & JianCheng Shi & Yue Wang, 2025. "An online prediction method for array antenna assembly performance based on digital twin," Journal of Intelligent Manufacturing, Springer, vol. 36(4), pages 2727-2748, April.
    • Handle: RePEc:spr:joinma:v:36:y:2025:i:4:d:10.1007_s10845-024-02384-5
    DOI: 10.1007/s10845-024-02384-5
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    References listed on IDEAS

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    1. Janitza, Silke & Tutz, Gerhard & Boulesteix, Anne-Laure, 2016. "Random forest for ordinal responses: Prediction and variable selection," Computational Statistics & Data Analysis, Elsevier, vol. 96(C), pages 57-73.
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