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.

翻译：与传统设计方法相比，生成式设计显著吸引了各学科领域工程师的关注。本文研究了如何实现具备多样化特征及可控结构复杂度的优化结构实时生成式设计。为此，采用改进型移动可变形组件（MMC）方法并结合创新策略，以生成高质量数据集。通过拓扑不变量对优化结构的复杂度水平进行分级。通过改进代价函数，训练WGAN模型，使其能够根据载荷位置与复杂度水平的输入实时生成优化设计方案。研究发现，所提出的模型能够生成具有清晰传力路径与锐利边界的多样化设计方案，这些方案不仅无需进一步优化，且与数据集中的任何参照结构均不相同，呈现出显著差异性。该方法为未来机器学习增强型智能设计应用展现了巨大潜力。