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The application of hybrid material systems composed of advanced composites and metals in automotive crash structural applications holds significant potential in terms of lightweighting and functional improvements. This paper proposes a comprehensive methodology to assess the suitability of vehicle body components for the application of hybrid material systems by analysing superimposed numerical crash simulation data of conventional steel bodies-in-white. The loading anisotropy and the global deformation are presented as two suitability criteria including an evaluation methodology to eventually select suitable hybrid material systems in the transition from Suitability assessments for advanced composite-metal hybrid 15 conventional to enhanced material employment and vehicle structure design. Additionally, the methodology provides novel insights into the global structural loading by simultaneously considering multiple crash load cases.

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The ‘Grey-Box-Processing’ method, presented in this article, allows for the integration of simulated and experimental data sets with the overall objective of a comprehensive validation of simulation methods and models. This integration leads to so-called hybrid data sets. They allow for a spatially and temporally resolved identification and quantitative assessment of deviations between experimental observations and results of corresponding finite element simulations in the field of vehicle safety. This is achieved by the iterative generation of a synthetic, dynamic solution corridor in the finite element domain, which is deduced from experimental observations and restricts the freedom of movement of a virtually analyzed structure. The hybrid data sets thus contain physically based information about the interaction (e.g. acting forces) between the solution corridor and the virtually analyzed structure. An additional result of the ‘Grey-Box-Processing’ is the complemented three-dimensional reconstruction of the incomplete experimental observations (e.g. two-dimensional X-ray movies). The extensive data sets can be used not only for the assessment of the similarity between experiment and simulation, but also for the efficient derivation of improvement measures in order to increase the predictive power of the used model or method if necessary. In this study, the approach is presented in detail. Simulation-based investigations are conducted using generic test setups as well as realistic pedestrian safety test cases. These investigations show the general applicability of the method as well as the significant informative value and interpretability of generated hybrid data sets.