基于拓扑材料特征的大规模三维纺织增强材料纱线追踪
Yarn tracking of large-scale 3D textile reinforcements using topological material features
- ENS Paris-Saclay（巴黎萨克雷高等师范学院）
- CNRS（法国国家科学研究中心）
- Inria（法国国家信息与自动化研究所）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出一种基于统计建模和变分优化的半自动纱线追踪方法，利用粗分辨率CT图像在正交切片上追踪大规模纺织增强材料，实现超过90%的追踪成功率。
AI中文摘要：
CT图像的自动分割对于通过生成高保真数值模型来增强模拟的可靠性变得越来越重要。本研究针对使用粗分辨率（即高于140 $\mu$m）X射线CT图像对风扇叶片干态预成型件中的纺织增强材料进行半自动追踪这一挑战性任务。我们的方法提供了一种可扩展的、基于切片的分析，在与主纱线方向正交的平面上进行，并应用于大规模真实工业部件。这使得在需要最少训练的情况下，能够准确识别和追踪纱线路径。该方法统计性地建模了纱线的三个关键属性：其典型横截面形状、其在三维空间中的连续性和运动，以及其与相邻纱线的空间相对排列。这些统计属性通过变分公式集成到追踪框架中，该公式优化了连续横截面平面中的所有纱线中心位置。该方法在1,500个切片中追踪了超过3,000根经纱，并实现了超过90%的追踪成功率。总体而言，这项工作展示了一种迈向大规模、自动化纺织增强材料标注的有前景的方法，为复杂复合材料结构中更高效的材料表征铺平了道路。
英文摘要：
Automated segmentation of CT images has become increasingly important to enhance the reliability of simulations through the generation of high fidelity numerical models. This study addresses the challenging task of semi-automatically tracking textile reinforcements in fan blade dry preforms using X-ray CT images captured at coarse resolutions (i.e., above 140 $μ$m). Our approach offers a scalable, slice-based analysis conducted on planes orthogonal to the main yarn directions, applied to a large-scale real industrial component. This enables accurate identification and tracking of yarn paths while requiring minimal training. The method models three key yarn properties statistically: their typical cross-section shape, their continuity and movement in the 3D space, and their spatial relative arrangement with respect to neighboring yarns. These statistical properties are integrated into a tracking framework via a variational formulation that optimizes all yarn center positions in successive cross-section planes. The method tracks more than 3,000 warp yarns across 1,500 slices and achieves a tracking success rate above 90%. Overall, this work demonstrates a promising approach toward large-scale, automated textile reinforcement annotation, paving the way for more efficient material characterization in complex composite structures.
