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

该项目的目标是在层间剪切和面外压缩载荷的联合作用下，对热固性基体的纤维增强聚合物（FRP）复合材料的疲劳行为进行实验研究。在此基础上，考虑到高应力主要结构（如喷气发动机低压段风扇叶片、异型轴或飞机机身）的特殊层结构和布置，建立了今后FRP构件损伤容限设计的基础模型。为了提高认识，科学研究将集中在层间裂纹扩展依赖于一个叠加的恒定面外压缩载荷和特定的织物结构在应用期间。因此，将加强与申请人合作开发和测试的双轴测试方法。断裂力学参数，如分层长度和应力强度因子的实验测定方法的发展。图像识别算法（机器学习-计算机视觉）和实验与模拟相结合的方法（虚拟孪生）适用于此目的。假设线弹性断裂力学，循环能量释放率，断裂阻力曲线，裂缝闭合函数等将根据织物增强和应力组合从循环试验的实验数据中导出。沿着在Instron 8800伺服液压试验机上进行的新开发的双轴循环试验方法，DCB和ENF等标准化试验方法用于循环分层增长的基本表征，而不受面外压缩载荷的影响。此外，改进的CT分析方法生成关于三种预选平纹织物的织物结构/布置的详细知识。在开发的多尺度模拟方法中，这些数据用于描述织物对平面外压缩应力下分层行为的影响。多尺度方法的结果与实验确定的数据进行比较，使影响分层增长和现象学现象，如在织物增强复合材料中的裂纹跳跃的因素可以确定和分离的相关性分析。机器学习领域的新分析方法用于联合收割机组合所有结果。使用来自监督和无监督学习的子部分的方法，例如数学分类，回归或分组模型。其目标是生成和提供预测裂纹扩展规律（例如Paris-Erdogan）和Wöhler曲线，用于织物增强复合材料的损伤容限工程设计。

The goal of the project is the experimental investigation of the fatigue behaviour of fabric-reinforced polymer (FRP) composites with thermoset matrix under combined interlaminar shear and out-of-plane compressive loading. On this basis, models are to be developed which will from the founda-tion for the damage-tolerant design of FRP-components in future, taking into account the specific layer architecture and arrangement in highly stresses primary structures such as fan blades in the low-pressure section of a jet engine, profiled shafts or aircraft fuselages. In order to improve understanding, scientific research will focus on the interlaminar crack growth in dependence of a superimposed constant out-of-plane compression loading and the specific fabric architecture in the appli-cation period. Therefore, a bi-axial test method will be enhanced which was developed and tested in cooperation of the applicants. Methods for the experimental determination of fracture mechanical parameters such as delamination length and stress intensity factors are developed. Algorithms of image recognition (machine learning - computer vision) and the method of linking experiment and simulation (virtual twin) are suitable for this purpose. Assuming linear-elastic fracture mechanics, cyclic energy release rates, fracture resistance curves, crack closure functions etc. will be derived from the experimental data of the cyclic tests depending on the fabric reinforcement and the stress combination. Along with the newly developed bi-axial cyclic test method, which is performed on an Instron 8800 servo-hydraulic testing machine, standardised test methods such as DCB and ENF are used for fundamental characterization of cyclic delamination growth without the influence of out-of-plane compressive loading. In addition, improved methods of CT-analysis generate detailed knowledge about the fabric architecture/arrangement of the three pre-selected plain weave fabrics. In the developed methods of multi-scale simulation this data is used to describe the fabric-specific influence on the delamination behaviour under out-of-plane compressive stress. The results of the multi-scale approach are compared with the experimentally determined data so that factors influencing delamination growth and phenomenological phenomena such as cracks jumps in a fabric-reinforced composites can be determined and separated by correlation analysis. New analysis methods from the field of machine learning are used to combine all results. Methods from the subsections of supervised and unsupervised learning such as mathematical classification, regression or grouping models are used. The goal is to generate and provide predictive crack growth laws (e.g. Paris-Erdogan) and Wöhler curves for the damage-tolerant engineering design of fabric-reinforced composites.