让拓扑优化结构可快速编辑,改完还能保持强度。
TopoEdit: Fast Post-Optimization Editing of Topology Optimized Structures
- 用预训练模型的潜空间做编辑接口,保持物理合理性。
- 修改后结构性能下降少于10%,生成速度低于1秒/次。
- 适合需要快速迭代设计的工程师或工业场景。
尽管拓扑优化能生成高性能结构,但后期局部修改仍易导致失效:直接在密度空间操作(如扭曲像素、打孔、替换填充)可能破坏传力路径,显著增加柔度;而重新优化耗时且可能产生质变设计。我们提出TopoEdit,一种快速后优化编辑工具,利用预训练拓扑基础模型(OAT)的结构化潜空间作为物理感知的工程编辑接口。给定已优化结构,TopoEdit将其编码至OAT的时空潜空间,通过部分加噪保留实例身份的同时提升可编辑性,并借助‘编辑-去噪’扩散流程注入用户意图。我们实现三种编辑算子:基于拖动的拓扑变形(边界条件一致更新)、使用晶格锚定参考潜空间的壳-填料替换(体积分数条件更新),以及通过掩码潜空间覆盖与扩散恢复实现晚期无设计区强制。采用一致性保持的引导DDIM过程,局部调整同时允许全局结构适应;可生成多候选并基于柔度感知标准选择,必要时辅以短周期SIMP精修。在多种案例与大规模编辑测试中,相比直接密度空间编辑,TopoEdit生成的修改更符合意图,性能下降更小,避免灾难性失效,且每样本生成时间小于1秒。
原文摘要 · Abstract (English)
Despite topology optimization producing high-performance structures, late-stage localized revisions remain brittle: direct density-space edits (e.g., warping pixels, inserting holes, swapping infill) can sever load paths and sharply degrade compliance, while re-running optimization is slow and may drift toward a qualitatively different design. We present TopoEdit, a fast post-optimization editor that demonstrates how structured latent embeddings from a pre-trained topology foundation model (OAT) can be repurposed as an interface for physics-aware engineering edits. Given an optimized topology, TopoEdit encodes it into OAT's spatial latent, applies partial noising to preserve instance identity while increasing editability, and injects user intent through an edit-then-denoise diffusion pipeline. We instantiate three edit operators: drag-based topology warping with boundary-condition-consistent conditioning updates, shell-infill lattice replacement using a lattice-anchored reference latent with updated volume-fraction conditioning, and late-stage no-design region enforcement via masked latent overwrite followed by diffusion-based recovery. A consistency-preserving guided DDIM procedure localizes changes while allowing global structural adaptation; multiple candidates can be sampled and selected using a compliance-aware criterion, with optional short SIMP refinement for warps. Across diverse case studies and large edit sweeps, TopoEdit produces intention-aligned modifications that better preserve mechanical performance and avoid catastrophic failure modes compared to direct density-space edits, while generating edited candidates in sub-second diffusion time per sample.
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