An online prediction method for array antenna assembly performance based on digital twin
Author:
Funder
National Key R&D Program of China
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10845-024-02384-5.pdf
Reference31 articles.
1. Chen, Z., Chen, C. Y., & Zhang, G. B. (2021). Assembly quality prediction model of CNC machine tools based on GA-SVM. Manufacturing Technology & Machine Tool, 09, 97–106. https://doi.org/10.19287/j.cnki.1005-2402.2021.09.019.
2. Deng, W. Q., Mo, R., Chen, K., Feng, X., Xia, H. T., Sun, H. B., & Chang, Z. Y. (2022). Prediction of rotor blade tip assembly clearance based on measured data for aero-engine. Journal of Aerospace Power, 37(06), 1273–1283. https://doi.org/10.13224/j.cnki.jasp.20210255.
3. Dimitris, M. (2020). Simulation in the design and operation of manufacturing systems:State of the art and new trends. International Journal of Production Research, 58(7), 1927–1949. https://doi.org/10.1080/00207543.2019.1636321
4. Elisa, V., Gianfranco, G., Maurizio, G., & Fiorenzo, F. (2022). Defect prediction for assembled products: A novel model based on the structural complexity paradigm. The International Journal of Advanced Manufacturing Technology, 120, 3405–3426. https://doi.org/10.1007/s00170-022-08942-6.
5. Gregorio, J. L., Lartigue, C., Thiebaut, F., & Lebrun, R. (2021). A digital twin-based approach for the management of geometrical deviations during assembly processes. Journal of Manufacturing Systems, 58, 108–117. https://doi.org/10.1016/j.jmsy.2020.04.020.
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