Efficient failure information propagation under complex stress states in fiber reinforced polymers: From micro- to meso-scale using machine learning - Gerritzen, Johannes; Hornig, Andreas; Gude, Maik; Meschut, G.; Bobbert, M.; Duflou, J.; Fratini, L.; Hagenah, H.; Martins, P.; Merklein, M.; Micari, F.; TRR 285: Methodenentwicklung zur mechanischen Fügbarkeit in wandlungsfähigen Prozessketten; et al. (Sheet Metal 2025, 2025)
The failure behavior of fiber reinforced polymers (FRP) is strongly influenced by their microstructure, i.e. fiber arrangement or local fiber volume content. However, this information cannot be directly used for structural analyses, since it requires a discretization on micrometer level. Therefore, current failure theories do not directly account for such effects, but describe the behavior averaged over an entire specimen. This foundation in experimentally accessible loading conditions leads to purely theory based extension to more complex stress states without direct validation possibilities. This work aims at leveraging micro-scale simulations to obtain failure information under arbitrary loading conditions. The results are propagated to the meso-scale, enabling efficient structural analyses, by means of machine learning (ML). It is shown that the ML model is capable of correctly assessing previously unseen stress states and therefore poses an efficient tool of exploiting information from the micro-scale in larger simulations.
Keywords: conference paper; Failure -- Fiber Reinforced Plastic -- Machine Learning