2017
DOI: 10.1109/tvcg.2016.2582158
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Interactive Exploration and Visualization Using MetaTracts extracted from Carbon Fiber Reinforced Composites

Abstract: This work introduces a tool for interactive exploration and visualization using MetaTracts. MetaTracts is a novel method for extraction and visualization of individual fiber bundles and weaving patterns from X-ray computed tomography (XCT) scans of endless carbon fiber reinforced polymers (CFRPs). It is designed specifically to handle XCT scans of low resolutions where the individual fibers are barely visible, which makes extraction of fiber bundles a challenging problem. The proposed workflow is used to analy… Show more

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Cited by 5 publications
(2 citation statements)
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“…• Dynamic Volume Lines (Weissenböck, Fröhler, Gröller, Kastner, & Heinzl, 2019) facilitate the comparison of multiple slightly varying volumetric datasets, by mapping them to 1D and applying a nonlinear scaling to highlight regions with large differences. • With MetaTracts (Bhattacharya, Heinzl, Amirkhanov, Kastner, & Wenger, 2015, Bhattacharya et al (2017) one can characterize and analyze fiber bundles as well as weaving patterns in fiber-reinforced polymers.…”
Section: Discussionmentioning
confidence: 99%
“…• Dynamic Volume Lines (Weissenböck, Fröhler, Gröller, Kastner, & Heinzl, 2019) facilitate the comparison of multiple slightly varying volumetric datasets, by mapping them to 1D and applying a nonlinear scaling to highlight regions with large differences. • With MetaTracts (Bhattacharya, Heinzl, Amirkhanov, Kastner, & Wenger, 2015, Bhattacharya et al (2017) one can characterize and analyze fiber bundles as well as weaving patterns in fiber-reinforced polymers.…”
Section: Discussionmentioning
confidence: 99%
“…Quan and Vo [QV15] propose a measure based on the center of mass as well as the start and end point of each fiber. Bhattacharya et al [BWW*17] employ distance measures for the clustering of fiber tracts of industrial fiber‐reinforced materials into fiber bundles. While these measures are designed for clustering similar fibers, we are interested in determining matches of the same underlying fiber in different datasets.…”
Section: Background and Related Workmentioning
confidence: 99%