周期性与图像配准用于大型三维纺织品中纱线路径提取
Periodicity and image registration for yarn path extraction in large 3D textiles
- Universit\'e Paris-Saclay, CentraleSup\'elec, ENS Paris-Saclay, CNRS, LMPS, 4, Avenue des Sciences, 91192 Gif-sur-Yvette, France
- Universit\'e Paris-Saclay, CentraleSup\'elec, Inria, CVN, 9 Rue Joliot Curie, 91190 Gif-sur-Yvette, France
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出一种利用机织周期性，通过提取单胞分割并结合数字体积相关配准，实现大型三维纺织品纱线路径高效准确提取的方法。
AI中文摘要：
在X射线计算机断层扫描体数据中准确识别纱线，仍然是生成机织复合材料高保真数值模型的关键且复杂的步骤。本研究引入了一个跟踪框架，利用机织结构的固有周期性，将复杂的大规模分割问题转化为对单个代表性单胞的标注。该方法首先利用织物的周期性，从体数据中提取代表性单胞并对其进行分割，以提供纱线几何形状的参考描述。然后，在通过复制单胞获得的理想化周期体积与真实复合材料体积之间进行数字体积相关（DVC），产生一个三维位移场，该位移场捕捉了与理想周期性的几何偏差。通过利用参考体积的周期性，将单胞纱线分割传播到整个体积，并使用DVC导出的位移场将标注传输到实际体积。在真实复合材料数据集上获得的结果表明，该方法能够以最少的输入准确恢复经纱和纬纱的纱线结构，为高效、可扩展的纺织复合材料表征开辟了新的前景。
英文摘要：
Accurate identification of yarns in X-ray computed tomography volumes remains a critical and complex step in generating high-fidelity numerical models of woven composites. This work introduces a tracking framework that leverages the intrinsic periodicity of woven architectures to transform a complex, large-scale segmentation problem into the annotation of a single representative unit cell. The approach first exploits the periodic nature of the weave to extract a representative unit cell from the volumetric data and segment it to provide a reference description of the yarn geometry. Digital Volume Correlation (DVC) is then performed between an idealised periodic volume obtained by replication of the unit cell and the real composite volume, yielding a three-dimensional displacement field that captures geometric deviations from ideal periodicity. The unit-cell yarn segmentation is propagated to the full volume by exploiting the periodicity of the reference volume, and the annotations are transported to the actual volume using the DVC-derived displacement field. Results obtained on real composite datasets demonstrate the ability of the method to accurately recover warp and weft yarn architectures with minimal input, opening new perspectives for efficient and scalable textile composite characterisation.
