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X-ray computed tomography (X-ray CT) is widely used to examine the internal structure of carbon fiber-reinforced plastics (CFRPs), yet challenges remain due to the trade-off between field-of-view and spatial resolution. This study presents a framework based on topological data analysis (TDA) using persistent homology to identify carbon fiber positions and cracks in CFRPs from X-ray CT images. Degree-1 persistence diagrams (PD1), obtained via both superlevel-set and sublevel-set filtrations of grayscale X-ray CT images, enable the identification of fiber positions while avoiding artifacts in the resin matrix. The framework is also applied to the detection of particle positions in a scanning electron microscopy (SEM) image. Comparative evaluation with watershed and local thickness algorithms demonstrates the advantages of our method. Segmentation of crack regions is also achieved through a refined application of PD1 using superlevel-set filtration. This framework is applicable to a variety of grayscale images beyond X-ray CT.

Rights:

• Creative Commons BY Attribution 4.0 International
  

Keyword: CFRP, fiber positions, crack, persistent homology, TDA, X-ray CT

Date published: 2025-12-31

Publisher: Taylor & Francis

Journal:

• Science and Technology of Advanced Materials (ISSN: 27660400) vol. 5 2572288

Funding:

Manuscript type: Author's version (Accepted manuscript)

MDR DOI: https://doi.org/10.48505/nims.5854

First published URL: https://doi.org/10.1080/27660400.2025.2572288

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Updated at: 2025-11-06 12:30:15 +0900

Published on MDR: 2025-11-06 12:24:58 +0900