AI 中文总结
本研究提出一种基于代理模型性能估计的两阶段检索-生成流水线，用于解决汽车引擎盖内板满足KPI要求的逆向设计问题，该流水线基于公开资源构建并作为交互式工具部署，揭示了代理模型误差与类内信号比值对流水线有效性的关键影响。
AI 中文摘要
引擎盖内板必须满足挠度目标、低于应力极限并达到质量目标。机器学习代理模型已使从几何到性能的正向计算变得快速且常规。对于设计空间组织为离散拓扑族而非连续参数化的工业零件，从给定要求生成几何的逆向问题仍未得到解决。本研究针对该逆向问题提出一种两阶段流水线：可达性阶段确定哪些拓扑族可满足给定要求向量；随后，条件变分自编码器在选定族内生成点云几何，神经算子代理模型估计每个候选方案的性能。该流水线完全基于公开数据和免费可用的计算资源构建，并作为交互式工具部署。流水线有效运行，相关限定条件作为主要发现而非警告报告。代理模型整体准确，但其误差与需区分的性能差异相当，这限制了对单个生成设计的可断言性。代理模型误差与类内信号的比值被认为是决定此类流水线能否正常工作的关键量。
英文摘要
An inner hood panel must meet a deflection target, stay below a stress limit, and hit a mass target. Machine-learned surrogates have made the forward direction, geometry to performance, fast and routine. The inverse direction, producing geometry from a stated requirement, remains largely unaddressed for industrial parts whose design space is organized into discrete topology families rather than a continuous parameterization. This work presents a two-stage pipeline for that inverse problem. A reachability stage determines which topology families can satisfy a given requirement vector. A conditional variational autoencoder then generates point-cloud geometry within a selected family, and a neural-operator surrogate estimates the performance of each candidate. The pipeline is built entirely from public data and freely available compute, and is deployed as an interactive tool. The pipeline works, with qualifications that are reported as primary findings rather than caveats. The surrogate is accurate in aggregate, but its error is comparable to the performance differences it is asked to discriminate, which bounds what can be claimed for any individual generated design. That ratio of surrogate error to within-class signal is argued to be the quantity that determines whether a pipeline of this kind can work at all.
Comments20 pages, 8 figures, 9 tables
