Abstract
Generation of realistic finite element method (FEM) geometry of a textile composite material at tow scale remains a challenging stage of material modeling. In this paper, a FE model generation approach is introduced, based on micro-Computed Tomography (µ-CT) images of a carbon fiber reinforced textile composite with multiple layer orientations. For the specified laminate layup made of plain weave fabric layers, a tow instance segmentation method based on deep learning was proposed. The fiber local orientation field was extracted from the tow centerlines approximation. For the image-based finite element meshing, a material interface reconstruction algorithm was proposed. The algorithm leads to a conformal and smooth finite element (FE) mesh with high-quality elements. FE models with voxel and tetrahedral meshes considering different approximations of the material orientation field were generated and used for numerical homogenization of the composite material's elastic properties.
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