In this paper, a set of failure criteria for transverse failure in Non Crimp Fabric (NCF) reinforced composites are presented. The proposed failure criteria are physically based and take into account the orthotropic character of NCF composites addressing the observed lack of transverse isotropy. Experimental data for transverse loading out of plane in combination with inplane loads are scarce. Therefore, to validate the developed criteria, experimental data are complemented with numerical data from a representative volume element (RVE) model using a meso-micromechanical approach. The RVE model also provides a deeper understanding of how failure occurs in NCF composites. Strength predictions from the developed set of failure criteria show good agreement with the experimental and numerical data.

In this paper a set of failure criteria for Non-Crimp Fabric (NCF) reinforced composites is implemented in a Finite Element (FE) software. The criteria, implemented at the ply level, predict transverse failure of NCF reinforced composites, in particular accounting for their inherent orthotropic properties. Numerical simulations are compared with tests on specimens with a generic design feature found in automotive structures. The current implementation enables correct prediction of failure mode and location.

In this paper, failure initiation in composite structures due to high out-of-plane load components is predicted. The predictions are based on finite element models built with shell elements, intended for global models. The full 3D stress state is estimated through stress recovery by the extended 2D FEM approach. Failure initiation is predicted with state of the art failure criteria for transversely isotropic composite materials. The approach is validated for a range of geometries with different modelling resolutions. Finally, the methodology is verified on a complex composite structure. With the proposed approach, using shell elements, efficient modelling strategies of large structures can be pursued using hot spot analyses to identify critical locations.