Presentation Information
[24p-P07-5]Detecting and Quantifying Structural Information in X-CT Images Using Topological Data Analysis
〇Xichan Gao1, Kazuto Akagi1, Daiki Kido2,3, Masao Kimura2,3 (1.AIMR, Tohoku Univ., 2.High Energy Accelerator Research Organization (KEK), 3.SOKENDAI (The Graduate University for Advanced Studies))
Keywords:
TDA,persistent homology,CFRP
X-ray computed tomography (X-CT) is a non-destructive 3D imaging technique extensively used to examine the internal structure of various objects. It has also been applicable for analyzing the alignment of fibers in carbon-fiber reinforced plastics (CFRP), as it aids in pinpointing areas prone to destruction and refining manufacturing parameters. Despite its widespread application in studying structural materials, challenges persist due to the constraints of obtaining large field-of-views with fine spatial resolution. This paper introduces an innovative approach that effectively identifies fiber positions beyond the limitations of X-CT's spatial resolution and quantitatively analyzes fiber alignment using topological data analysis (TDA).