Automatic generation of high performance material models for long fiber reinforced plastics in crash simulations

Long fiber reinforced plastics (LFRPs) offer excellent mechanical properties and are widely used in automotive and aerospace industries. Accurately modeling the behavior of LFRPs under crash conditions is crucial for designing lightweight and safe structures. However, creating reliable material models for LFRPs is challenging due to their complex microstructure and anisotropic nature. This study presents an automatic method to generate highly accurate material models for LFRPs, specifically tailored for crash simulations. The proposed approach employs virtual testing on representative volume elements (RVEs) that model the LFRP material with fibers and matrix separately. To ensure accuracy, a micro material model is applied, which is calibrated using real micro material experiments combined with computed tomography (CT) scans to obtain the actual fiber orientation distribution. An anisotropic material card is then calibrated using the generated data. The influence of various factors, including strain rate, fiber orientation, fiber concentration, and stress state, is accurately described within the material model. The model's predictive capabilities are validated against a range of experimental tests, including tension, shear, and biaxial loading conditions. The main advantage of this method is its efficiency and reduced experimental effort compared to established techniques. By leveraging virtual testing on RVEs and incorporating real micro material data, the proposed approach significantly reduces the time and resources required for material characterization. This enables quicker development and optimization of LFRP structures for crash applications, leading to improved safety and reduced time-to-market. The presented automatic method for generating highly accurate material models for LFRPs offers a valuable tool for engineers and researchers involved in crash simulation and design optimization. Its ability to capture the intricate behavior of LFRPs under various loading conditions paves the way for enhanced structural analysis and lightweight design in numerous industries.for generating highly accurate material models for LFRPs offers a valuable tool for engineers and researchers involved in crash simulation and design optimization. Its ability to capture the intricate behavior of LFRPs under various loading conditions paves the way for enhanced structural analysis and lightweight design in numerous industries.