Real-time generative design of diverse, "truly" optimized structures with controllable structural complexities
Fuente:
arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866909078184263680 |
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| author | Du, Zongliang Ma, Xinyu Hao, Wenyu Liang, Yuan Zhang, Xiaoyu Luo, Hongzhi Guo, Xu |
| author_facet | Du, Zongliang Ma, Xinyu Hao, Wenyu Liang, Yuan Zhang, Xiaoyu Luo, Hongzhi Guo, Xu |
| contents | Compared with traditional design methods, generative design significantly attracts engineers in various disciplines. In thiswork, howto achieve the real-time generative design of optimized structures with various diversities and controllable structural complexities is investigated. To this end, a modified Moving Morphable Component (MMC) method together with novel strategies are adopted to generate high-quality dataset. The complexity level of optimized structures is categorized by the topological invariant. By improving the cost function, the WGAN is trained to produce optimized designs with the input of loading position and complexity level in real time. It is found that, diverse designs with a clear load transmission path and crisp boundary, even not requiring further optimization and different from any reference in the dataset, can be generated by the proposed model. This method holds great potential for future applications of machine learning enhanced intelligent design. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_08981 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Real-time generative design of diverse, "truly" optimized structures with controllable structural complexities Du, Zongliang Ma, Xinyu Hao, Wenyu Liang, Yuan Zhang, Xiaoyu Luo, Hongzhi Guo, Xu Computational Engineering, Finance, and Science Compared with traditional design methods, generative design significantly attracts engineers in various disciplines. In thiswork, howto achieve the real-time generative design of optimized structures with various diversities and controllable structural complexities is investigated. To this end, a modified Moving Morphable Component (MMC) method together with novel strategies are adopted to generate high-quality dataset. The complexity level of optimized structures is categorized by the topological invariant. By improving the cost function, the WGAN is trained to produce optimized designs with the input of loading position and complexity level in real time. It is found that, diverse designs with a clear load transmission path and crisp boundary, even not requiring further optimization and different from any reference in the dataset, can be generated by the proposed model. This method holds great potential for future applications of machine learning enhanced intelligent design. |
| title | Real-time generative design of diverse, "truly" optimized structures with controllable structural complexities |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2401.08981 |