Explainable artificial intelligence for enhancing system understanding and interpretability of numerical crash simulations
提出一个分析框架，利用可解释人工智能（SHAP）处理数值碰撞仿真数据，帮助工程师快速理解复杂系统行为，并通过三个工业案例验证其效果。
During the development of new vehicles, engineering efforts focus on minimizing injury risks for vulnerable road users and occupants in crash scenarios while maintaining structural integrity. To meet diverse requirements, the nonlinear behavior of passive vehicle safety systems is virtually designed and optimized using numerical Finite-Element (FE) crash simulations. However, due to the complexity of these systems, it is challenging and time-consuming for the engineers involved to understand their behavior. To reduce the time required for assessing crash simulations, we introduce a novel analysis framework that provides flexible data processing and incorporates explainable Artificial Intelligence (AI). The framework allows for examining arbitrary dependencies within parameter-, sensor-, and FE-mesh data by fitting a supervised Machine Learning (ML) model, which is then analyzed using SHapley Additive exPlanations (SHAP). To extend the SHAP methodology to the engineering domain, we introduce System and Difference SHAP values, which facilitate the aggregation of features that describe a system and enable comparisons between two simulations based on input feature contributions in the output space. This allows engineers to intuitively understand contributions to the overall system behavior and generate a data-driven understanding that can be rapidly established. Three industry use-cases from the structural and occupant vehicle safety domain are used to evaluate the framework. The observations achieved demonstrate enhanced and previously unseen insights into the behavior of the crash loaded systems by the effective use of AI within virtual engineering. Comprehensive ablation studies show reproducibility and consistency of the results obtained when using alternative ML models or sensitivity analysis methods.