Analysis of the Compaction Behavior of Textile Reinforcements in Low-Resolution In-Situ CT Scans via Machine-Learning and Descriptor-Based Methods

TL;DR

This paper develops a machine learning framework to analyze the nesting behavior of textile reinforcements in composites using low-resolution CT scans, enabling detailed structural characterization relevant for predictive modeling.

Contribution

It introduces a novel combination of 3D-UNet segmentation and correlation analysis to quantify nesting in textile reinforcements from industrial CT data.

Findings

High segmentation accuracy with IoU of 0.822 and F1 score of 0.902.

Strong correlation between CT-based measurements and micrograph validation.

Framework enables probabilistic extraction of layer thickness and nesting degree.

Abstract

A detailed understanding of material structure across multiple scales is essential for predictive modeling of textile-reinforced composites. Nesting -- characterized by the interlocking of adjacent fabric layers through local interpenetration and misalignment of yarns -- plays a critical role in defining mechanical properties such as stiffness, permeability, and damage tolerance. This study presents a framework to quantify nesting behavior in dry textile reinforcements under compaction using low-resolution computed tomography (CT). In-situ compaction experiments were conducted on various stacking configurations, with CT scans acquired at 20.22 $\mu$m per voxel resolution. A tailored 3D{-}UNet enabled semantic segmentation of matrix, weft, and fill phases across compaction stages corresponding to fiber volume contents of 50--60 %. The model achieved a minimum mean Intersection-over-Union of 0.822 and an $F1$ score of 0.902. Spatial structure was subsequently analyzed using the two-point correlation function $S_2$, allowing for probabilistic extraction of average layer thickness and nesting degree. The results show strong agreement with micrograph-based validation. This methodology provides a robust approach for extracting key geometrical features from industrially relevant CT data and establishes a foundation for reverse modeling and descriptor-based structural analysis of composite preforms.