In this study a modeling approach for short fiber-reinforced composites is presented which allows one to consider information from the microstructure of the compound while modeling on the component level. The proposed technique is based on the determination of correlation functions by the moving window method. Using these correlation functions random fields are generated by the Karhunen–Loève expansion. Linear elastic numerical simulations are conducted on the mesoscale and component level based on the probabilistic characteristics of the microstructure derived from a two-dimensional micrograph. The experimental validation by nanoindentation on the mesoscale shows good conformity with the numerical simulations. For the numerical modeling on the component level the comparison of experimentally obtained Young’s modulus by tensile tests with numerical simulations indicate that the presented approach requires three-dimensional information of the probabilistic characteristics of the microstructure. Using this information not only the overall material properties are approximated sufficiently, but also the local distribution of the material properties shows the same trend as the results of conducted tensile tests.

在这项研究中，提出了一种短纤维增强复合材料的建模方法，该方法可以在组分级建模时考虑化合物微观结构的信息。所提出的技术基于通过移动窗口方法确定相关函数。使用这些相关函数，Karhunen-Loève展开会生成随机字段。基于从二维显微照片得出的微观结构的概率特征，在中尺度和组分水平上进行了线性弹性数值模拟。通过在中尺度上的纳米压痕进行的实验验证与数值模拟显示出良好的一致性。对于组件级的数值建模，通过拉伸试验将实验获得的杨氏模量与数值模拟进行比较，表明所提出的方法需要有关微观结构概率特征的三维信息。使用该信息，不仅可以充分估计总体材料性能，而且材料性能的局部分布也显示出与进行拉伸测试的结果相同的趋势。