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

2025-08-13

AI Summary

This study addresses the challenge of quantifying inter-layer nesting in dry textile-reinforced preforms during compression and elucidating its influence on composite stiffness, permeability, and damage tolerance. To overcome limitations in resolving microstructural details from low-resolution industrial in-situ X-ray computed tomography (CT) data, we propose a multiscale nesting quantification framework: (i) a customized 3D U-Net enables robust semantic segmentation (mean IoU = 0.822, F1-score = 0.902); (ii) two-point correlation functions (S₂) provide probabilistic structural characterization; and (iii) fiber volume fraction is inverted from S₂-derived statistics. Validated against high-resolution microscopy, the framework yields layer thickness and nesting degree with high fidelity. This work presents the first non-destructive, quantitative microstructural characterization of textile preforms from low-quality CT data, delivering high-fidelity geometric parameters essential for multiphysics modeling of composites.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Computer Vision: Segmentation
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.

Application Category

• Web Mining and Content Analysis: Bridging structured and unstructured data
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

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 $μ$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.

Problem

• Quantify nesting behavior in textile reinforcements under compaction
• Analyze material structure using low-resolution CT scans and machine learning
• Extract key geometrical features for composite preforms modeling

Innovation

• Low-resolution CT scans for textile analysis
• 3D-UNet for semantic segmentation of phases
• Two-point correlation function for structure analysis