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

该项目的目标是对热固性纤维增强聚合物（FRP）复合材料在层间剪切和面外压缩载荷联合作用下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的风扇叶片、异形轴或飞机机身）的特定层结构和布置，将开发模型，为未来frp部件的损伤容限设计奠定基础。为了提高认识，科学研究将集中在应用期间依赖于叠加恒定面外压缩载荷和特定织物结构的层间裂纹扩展。因此，将加强与申请人合作开发和测试的双轴测试方法。提出了分层长度和应力强度因子等断裂力学参数的实验测定方法。图像识别算法（机器学习-计算机视觉）和实验与仿真相结合的方法（虚拟孪生）适合于此目的。假设线弹性断裂力学，根据织物的配筋和应力组合，由循环试验的实验数据推导出循环能量释放率、断裂阻力曲线、裂纹闭合函数等。结合在Instron 8800伺服液压试验机上新开发的双轴循环试验方法，采用了DCB和ENF等标准化试验方法，对不受面外压缩载荷影响的循环分层生长进行了基本表征。此外，改进的ct分析方法生成了关于三种预选平纹织物的织物结构/排列的详细知识。在发展的多尺度模拟方法中，这些数据被用来描述织物在面外压应力下对分层行为的特定影响。将多尺度方法的结果与实验数据进行了比较，从而可以通过相关分析确定和分离织物增强复合材料中分层生长和裂纹跳跃等现象的影响因素。来自机器学习领域的新分析方法被用于组合所有结果。从监督和非监督学习的子部分的方法，如数学分类，回归或分组模型被使用。目标是生成并提供可预测的裂纹扩展规律（例如Paris-Erdogan）和Wöhler曲线，用于织物增强复合材料的损伤容忍工程设计。

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.