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Description of the fatigue behaviour of fabric-reinforced fibre-plastic composites under combined interlaminar shear and out-of-plane compressive stress

Subject Area Polymeric and Biogenic Materials and Derived Composites
Lightweight Construction, Textile Technology
Term from 2020 to 2024
Project identifier Deutsche Forschungsgemeinschaft (DFG) - Project number 450147819
 
Final Report Year 2025

Final Report Abstract

The project aimed to experimentally investigate the fatigue behavior of fabric-reinforced fiber-reinforced polymers (FRP) with a thermoset matrix under combined interlaminar shear and out-of-plane compressive loading. Based on these findings, models were developed to enable damage-tolerant design of FRP components, considering the fabric-specific layer architecture in highly loaded primary structures (e.g., fan blades, drive shafts, aircraft fuselages). The focus was on interlaminar crack growth under constant out-of-plane pressure and its dependence on fabric architecture. For this, a jointly developed biaxial test method was further optimized. Experimental procedures were established to determine fracture mechanical parameters such as delamination length and stress intensity factors. Image processing algorithms (machine learning (ML)) and digital twin methods were also applied. Assuming linear-elastic fracture mechanics, cyclic energy release rates were derived from experimental data depending on the fabric reinforcement and load combination. In addition to the newly developed biaxial test method (Instron 8800), standard tests such as Double-Cantilever-Beam and End-Notched-Flexture were used to characterize delamination growth without compressive influence. Improved CT analyses enabled a detailed characterization of the fabric architecture of three selected plain weave fabrics. These data were used in multiscale simulations to model the influence of fabric structure on delamination under pressure. Comparison with experimental results allowed the identification and separation of influencing factors, such as crack jumps, using correlation analysis. To integrate the results, ML methods were employed—both supervised and unsupervised approaches, including classification, regression, and clustering models. The goal was to derive predictive crack growth laws (e.g., Paris-Erdogan) and S-N curves for the damage-tolerant design of fabric-reinforced FRP structures.

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