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A Boundary Element Reconstruction (BER) Model for Moving Morphable Component Topology Optimization
1 School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang, 471003, China
2 College of Vehicle and Traffic Engineering, Henan University of Science and Technology, Luoyang, 471003, China
* Corresponding Author: Hongyu Xu. Email:
Computers, Materials & Continua 2026, 86(1), 1-18. https://doi.org/10.32604/cmc.2025.068763
Received 05 June 2025; Accepted 24 September 2025; Issue published 10 November 2025
Abstract
The moving morphable component (MMC) topology optimization method, as a typical explicit topology optimization method, has been widely concerned. In the MMC topology optimization framework, the surrogate material model is mainly used for finite element analysis at present, and the effectiveness of the surrogate material model has been fully confirmed. However, there are some accuracy problems when dealing with boundary elements using the surrogate material model, which will affect the topology optimization results. In this study, a boundary element reconstruction (BER) model is proposed based on the surrogate material model under the MMC topology optimization framework to improve the accuracy of topology optimization. The proposed BER model can reconstruct the boundary elements by refining the local meshes and obtaining new nodes in boundary elements. Then the density of boundary elements is recalculated using the new node information, which is more accurate than the original model. Based on the new density of boundary elements, the material properties and volume information of the boundary elements are updated. Compared with other finite element analysis methods, the BER model is simple and feasible and can improve computational accuracy. Finally, the effectiveness and superiority of the proposed method are verified by comparing it with the optimization results of the original surrogate material model through several numerical examples.Keywords
Cite This Article
Copyright © 2026 The Author(s). Published by Tech Science Press.This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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