The aim of this paper is to develop a comprehensive modeling strategy for creating a realistic representative volume element (RVE) of 2.5D woven composites. The strategy consists of two main parts: the extraction of geometric feature parameters and the establishment of a parametric voxel-mesh full-cell model (VFM). Firstly, a neural network model is constructed to achieve an accurate segmentation of yarn cross-sections from X-ray computed tomography (XCT) images. Secondly, geometric feature parameters are then extracted from the segmentation results using image algorithms. Finally, a parametric modeling method is proposed to establish the VFM of the material. To evaluate the performance of the VFM, its structural sizes, overall fiber volume fraction (FVF), and stiffness prediction accuracy are assessed. The comparison results indicate that the VFM achieves a fine mesoscale characterization and a high stiffness prediction accuracy.

中文翻译：

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基于计算机视觉特征提取的2.5D编织复合材料参数化建模

本文的目的是开发一种综合建模策略，用于创建 2.5D 编织复合材料的真实代表性体积单元 (RVE)。该策略由两个主要部分组成：几何特征参数的提取和参数化体素网格全细胞模型（VFM）的建立。首先，构建神经网络模型以实现 X 射线计算机断层扫描 (XCT) 图像中纱线横截面的精确分割。其次，使用图像算法从分割结果中提取几何特征参数。最后，提出了一种参数化建模方法来建立材料的VFM。为了评估 VFM 的性能，需要评估其结构尺寸、整体纤维体积分数 (FVF) 和刚度预测精度。比较结果表明，VFM 实现了精细的介观表征和较高的刚度预测精度。