🤖 AI Summary
This study addresses the high computational cost of traditional CAE simulations in pedestrian protection crash safety design, which hinders efficient exploration of high-dimensional, nonlinear design spaces. The authors propose an AI-driven engineering workflow coordinated by a foundation model, integrating large language models and vision-language models as a unified interface to combine data-driven surrogate modeling, NSGA-II multi-objective optimization, and topology-preserving geometric generation—all while supporting natural language interaction. Demonstrated on an automotive front bumper case study, the framework generates 35 diverse, safety-compliant designs within seconds, achieving a dramatic efficiency gain over conventional simulation workflows that require several hours. The approach maintains strong interpretability and enhances design space exploration capabilities.
📝 Abstract
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.