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Numerical Evaluation of Low-Speed Impact Behaviour of a Fabric Layered Composite Plate in an Industrial Context

The use of Layered Fabric Reinforced Polymers (LFabRP) in the automotive industry is growing significantly. In order to ensure the safety and design imperatives, a new material model was developed for the LFabRP to improve the predictability of low-speed impact simulations by means of Finite Element Analysis (FEA). The delamination prediction with FEA requires costly computation time methods such as the use of cohesive elements at ply interfaces. The proposed method includes the delamination evaluation directly within the material model and operates as a plugin for the constitutive intralaminar model. It allows to describe in an accurate manner the behaviour of a layered material by using only one shell element through-the-thickness. The material model recomputes a realistic strain field by means of a high-order zigzag theory. It takes into account the delamination effects on the continuity of the strain field but remains based on five degrees of freedom. By ensuring the internal energy equivalence between both element and material model theories, a realistic strain field for layered material is provided to the intralaminar material model. The intralaminar material model is based on a pre-existing continuum damage model (Onera Damage Model). To improve the efficiency and the precision for the modelling of LFabRP, friction mechanisms, a rheological viscoelastic model and a smeared crack approach for the fibre failure were introduced. The validation of the present model was carried out by means of controlled impact tests on a hydraulic high-speed jack. The LFabRP taken as reference is made up with three different fabric preforms. The material parameters are exclusively determined thanks to standard tests on the preforms taken individually on order to evaluate the model ability to predict low-speed impact behaviour. Moreover, experimental 3D damage reconstruction by means of ultrasonic inspection is compared to simulation predictions.