JOURNAL ARTICLE
Converting pixel‐type topology optimization results to MMC‐representation based on sparse optimization and its applications.
Published In: International Journal for Numerical Methods in Engineering, 2024, v. 125, n. 8. P. 1 1 of 3
Database: Applied Science & Technology Source Ultimate 2 of 3
Authored By: Ling, Ran; Xu, Gang; Zhang, Xiaoyu; Xu, Jinlan; Guo, Xu 3 of 3
Abstract
How to realize the switching between various topology optimization approaches such as SIMP and moving morphable component (MMC) method, is a crucial challenge in the field of structural design. In this article, a robust conversion framework is proposed to convert a pixel‐type topology optimization result to MMC representation. Based on the sparse optimization approach, the framework enables the determination of the minimum number of components with a specified shape error. This method provides an efficient bridge for these two types of geometric descriptions, and promotes the free switching between the topology optimization frameworks with pixel‐based and MMC‐based design domains. The proposed procedure contains a pre‐processing of resolution improvement, symmetry axis extraction with sparse optimization, and variational shape approximation. Two practical applications are demonstrated using the proposed framework. First, it can be applied to the intermediate results of SIMP, to achieve faster optimization convergence. Furthermore, a stress‐based shape optimization approach can be applied to the obtained MMCs, and novel progressive continuity constraints are also introduced to maintain boundary continuity. Several examples demonstrate advantages of the proposed framework. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:International Journal for Numerical Methods in Engineering. 2024/04, Vol. 125, Issue 8, p1
- Document Type:Article
- Subject Area:Mathematics
- Publication Date:2024
- ISSN:00295981
- DOI:10.1002/nme.7437
- Accession Number:175964776
- Copyright Statement:Copyright of International Journal for Numerical Methods in Engineering is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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