Modeling approaches for the decomposition behavior of preconsolidated rovings throughout local deformation processes - Gröger, Benjamin; Gerritzen, Johannes; Hornig, Andreas; Gude, Maik (Sheet Metal 2025)
This paper investigates two modeling approaches for the simulation of the deformation and decomposition behavior of preconsolidated rovings above the thermoplastic matrix{’} melting temperature. This is crucial for capturing the local material structure after processes introducing highly localized deformation such as mechanical joining processes between metal and fiber reinforced thermoplastics (FRTP). A generic finite element (FE) model is developed, incorporating interfaces discretized through either cohesive zone (CZ) elements or Coulomb friction-based contacts. The material parameters for the FE elements are derived from the initial stiffness of a statistical volume element (SVE) at micro scale modelled with an Arbitrary-Lagrange-Eulerian method for three load cases. The CZ properties calculated are based on the shear viscosity of the composite. The CZ and contact modelling approaches are evaluated using three load cases of the SVE, comparing force-displacement curves. Under simple loading conditions, such as normal pressure tension and bending, both methods produce similar results; however, in complex load cases, the CZ approach shows clear advantages in handling interface interactions and shows robust simulations. The CZ approach thus presents a promising method for simulating roving decomposition in FRTP-metal joining applications above the matrix{’} melting temperature.
Keywords: Finite Element Method (FEM); Process; Thermoplastic Fiber Reinforced Plastic

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 (Sheet Metal 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: Failure; Fiber Reinforced Plastic; Machine Learning