Yarn tracking of large-scale 3D textile reinforcements using topological material features

2026-09-15

AI Summary

本文通过统计建模和变分优化方法，解决大尺度3D纺织增强材料中粗分辨率CT图像的纱线路径半自动跟踪问题。

Abstract

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.

Problem

• Yarn tracking
• CT images
• Textile reinforcements
• Semi-automatic

Innovation

• yarn tracking
• topological material features
• automated segmentation
• large-scale 3D textile reinforcements
• variational formulation