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arXiv · 2401.08981

Real-time generative design of diverse, "truly" optimized structures with controllable structural complexities

Zongliang Du·Xinyu Ma·Wenyu Hao·Yuan Liang·Xiaoyu Zhang·Hongzhi Luo·Xu Guo
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Abstract

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.

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Zongliang Du, Xinyu Ma, Wenyu Hao, Yuan Liang, Xiaoyu Zhang, Hongzhi Luo, Xu Guo. 2024-01-17. Real-time generative design of diverse, "truly" optimized structures with controllable structural complexities. https://arxiv.org/abs/2401.08981

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