AI-driven engineering workflows face particular challenges in crash safety design: unlike aerodynamics, crash events involve highly nonlinear contact dynamics, material nonlinearity, and discrete state transitions that are difficult to capture with data-driven surrogate models. To the best of our knowledge, we present the first foundation model--orchestrated workflow for crash safety design that enables surrogate-assisted exploration for pedestrian protection, reducing evaluation time from hours per CAE simulation to seconds. The workflow integrates four components: (1) a surrogate trained on CAE crash simulations to predict pedestrian leg injury metrics from design parameters, achieving an average $R^2=0.87$ and providing distribution-free conformal prediction intervals; (2) multiobjective evolutionary search (NSGA-II) to discover diverse feasible parameter sets under user-specified constraints; (3) a morphing-based geometry generator that maps parameters to topology-preserving 3D shapes; and (4) a natural-language interface in which an LLM orchestrates the workflow and a vision--language model supports semantic comparison of generated designs. In an automotive front-bumper case study, the workflow produces 35 distinct safety-compliant alternatives from a single exploration, a process that would require weeks with conventional CAE iteration. These results suggest that foundation models can serve as integration layers between ML surrogates and physics-based simulation, helping bring AI capabilities to safety-critical engineering domains.

翻译：AI驱动的工程工作流程在碰撞安全设计中面临特殊挑战：与空气动力学不同，碰撞事件涉及高度非线性的接触动力学、材料非线性和离散状态转变，这些难以通过数据驱动的代理模型捕捉。据我们所知，我们提出了首个面向碰撞安全设计的基于基础模型编排的工作流程，实现了代理辅助的行人保护探索，将评估时间从每次CAE模拟数小时缩短至数秒。该工作流程整合了四个组件：（1）基于CAE碰撞模拟训练的代理模型，用于从设计参数预测行人腿部损伤指标，平均$R^2=0.87$，并提供无分布假设的共形预测区间；（2）多目标进化搜索（NSGA-II），用于在用户指定约束下发现多样化的可行参数集；（3）基于形变的几何生成器，将参数映射为保持拓扑的三维形状；（4）自然语言接口，其中大语言模型编排工作流程，而视觉-语言模型支持对生成设计进行语义比较。在一项汽车前保险杠案例研究中，该工作流程通过单次探索生成35种不同的合规安全备选方案，而传统CAE迭代过程需要数周。这些结果表明，基础模型可作为机器学习代理与物理模拟之间的集成层，助力将AI能力引入安全关键工程领域。