Segmentation of a CFRP weave structure for material characterization and simulation
Phase contrast X-Ray imaging is state-of-art for micro-structural elucidation for carbon fiber reinforced composites (CFRP). Understanding the micro-structure of CFRP is important because it significantly influences its processing as well as its application properties on component length scale. Tw...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | deu |
| Published: |
NDT.net
2025-02-01
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| Series: | e-Journal of Nondestructive Testing |
| Online Access: | https://www.ndt.net/search/docs.php3?id=30714 |
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| Summary: | Phase contrast X-Ray imaging is state-of-art for micro-structural elucidation for carbon fiber reinforced composites (CFRP). Understanding the micro-structure of CFRP is important because it significantly influences its processing as well as its application properties on component length scale. Two prominent examples for this multiscale relationship are the permeability of fiber woven fabrics as semi-finished parts, affecting its processing behaviour or the fatigue behaviour of the final composite, determining the usability and life-time of a composite product. CT provides a valuable tool for microstructure analysis, but is inherently limited to a certain length scale, while the important features of a CFRP specimen range from μm to several mm. The results presented in this article constitute the first steps in the development of a multiscale approach to bridge the gap over those 5 orders of magnitude on the length scale, to provide a digital representation of an actual CFRP sample, to be able to derive the aforementioned parameters. In a first step, a high-resolution μCT scans was performed on a woven carbon fiber specimen with a Zeiss Versa 520. The scan data were exported into three image stacks. These stacks were segmented into 0°-fibers, 90°-fibers and matrix. A structure-tensor-based approach, which used the 3D volume and an AI-based approach, which used the cross sections, where one fiber direction pierces the image surface, were chosen to perform the segmentation.
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| ISSN: | 1435-4934 |