Topology optimization (TO) is employed in engineering to optimize structural performance while maximizing material efficiency. However, traditional TO methods incur significant computational and time costs. Although research has leveraged generative AI to predict TO outcomes and validated feasibility and accuracy, existing approaches still suffer from limited customizability and impose a high cognitive load on users. Furthermore, balancing structural performance with aesthetic attributes remains a persistent challenge. We developed Sketch2Topo, which augments a diffusion-based TO model with image-to-image generation and image editing capabilities. With Sketch2Topo, users can use sketching to customize geometries and specify physical constraints. The tool also supports mask input, enabling users to perform TO on selected regions only, thereby supporting higher levels of customization. We summarize the workflow and details of the tool and conduct a brief quantitative evaluation. Finally, we explore application scenarios and discuss how hand-drawn input improves usability while balancing functionality and aesthetics.

翻译：拓扑优化（Topology Optimization, TO）在工程中用于优化结构性能，同时最大化材料效率。然而，传统拓扑优化方法计算成本高、耗时久。尽管已有研究利用生成式AI预测拓扑优化结果，并验证了其可行性与准确性，但现有方法仍存在可定制性有限、用户认知负荷高的问题。此外，在结构性能与美学属性之间取得平衡始终是一项持续性挑战。我们开发了Sketch2Topo，该模型通过图像到图像生成和图像编辑功能增强了基于扩散的拓扑优化模型。借助Sketch2Topo，用户可通过手绘自定义几何形状并指定物理约束。该工具还支持掩码输入，使用户仅对选定区域进行拓扑优化，从而实现更高层次的自定义。我们总结了该工具的工作流程与细节，并进行了简要的定量评估。最后，我们探讨了应用场景，并讨论了手绘输入如何在平衡功能性与美学的同时提升可用性。