La Trobe
AAM_1220850_Lee,S.pdf (10.6 MB)

Topology optimization using super-resolution image reconstruction methods

Download (10.6 MB)
journal contribution
posted on 2023-05-03, 06:52 authored by S Lee, QX Lieu, Thuc VoThuc Vo, J Kang, J Lee
This paper proposes a new topology optimization method to obtain super-resolution images without increasing mesh refinement by using various methods. For traditional process, low-resolution (LR) images are fed into the Solid Isotropic Material with Penalization (SIMP) and Optimality Criteria (OC) methods. Here, the trained super-resolution images are added to the inner loops to reconstruct the topology and used to obtain high-resolution (HR) images from the LR images at the end of each iteration. After finishing the reconstruction process, the main topology optimization method recovers the original size images from the HR images for the next iteration. Several examples are presented to demonstrate the effectiveness of the proposed method. The final topologies provide noticeably improvement over those of typical SIMP method and create a much sharper and higher contrast images. Moreover, the proposed strategy using the super-resolution image reconstruction methods can give valuable innovation for conventional topology optimization process.

Funding

This research was supported by a grant (NRF-2021R1A2B5B0300- 2410) from NRF (National Research Foundation of Korea) funded by MEST (Ministry of Education and Science Technology) of Korean government

History

Publication Date

2023-03-01

Journal

Advances in Engineering Software

Volume

177

Article Number

103413

Pagination

20p.

Publisher

Elsevier B.V.

ISSN

0965-9978

Rights Statement

© 2023. This manuscript version is made available under the CC-BY-NC-ND 4.0 license: https://creativecommons.org/licenses/by-nc-nd/4.0/

Usage metrics

    Journal Articles

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC