Content area

Abstract

To achieve timeliness and accuracy when conducting a performance analysis of a complex product assembly process, we consider the performance analysis of an array antenna assembly process, and investigate the measurability of the assembly process’ performance. In this study, a digital twin modeling method is proposed for the assembly performance analysis of an array antenna. This method integrates finite elements, mesh model simplification, and surrogate models. To achieve real-time prediction and rapid visualization of the assembly performance of the assembly process, a mesh simplification algorithm for a finite element mesh model based on edge collapse is proposed and a Gaussian process regression surrogate model for product assembly performance is constructed to complete the construction of a digital twin model for assembly performance analysis. Finally, the proposed method is verified by an example of the assembly performance analysis of a typical array antenna. Results show that the digital twin model proposed achieves a high response speed and fidelity in the analysis of assembly performance and provides a valuable reference to application of digital twin technology in product assembly processes.

Details

Title
A digital twin modeling method for array antenna assembly performance real-time analysis
Author
Guo, Xuepeng 1 ; Liu, Linyan 1 ; Huang, Jinghong 1 ; Wang, HuiFen 1 ; Du, XiaoDong 2 ; Shi, JianCheng 2 ; Wang, Yue 3 

 School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, China (GRID:grid.410579.e) (ISNI:0000 0000 9116 9901) 
 The 29Th Research Institute, China Electronics Technology Group Corporation, Chengdu, China (GRID:grid.464269.b) (ISNI:0000 0004 0369 6090) 
 College of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China (GRID:grid.411587.e) (ISNI:0000 0001 0381 4112) 
Pages
3765-3781
Publication year
2023
Publication date
Jun 2023
Publisher
Springer Nature B.V.
ISSN
02683768
e-ISSN
14333015
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2813457889
Copyright
© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.