| People | Locations | Statistics |
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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Dahl, Anders Bjorholm
Technical University of Denmark
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (18/18 displayed)
- 2023Elucidating the Bulk Morphology of Cellulose-Based Conducting Aerogels with X-Ray Microtomography
- 2023Elucidating the Bulk Morphology of Cellulose-Based Conducting Aerogels with X-Ray Microtomography
- 2022SparseMeshCNN with Self-Attention for Segmentation of Large Meshescitations
- 2021Quantifying effects of manufacturing methods on fiber orientation in unidirectional composites using structure tensor analysiscitations
- 2020Characterization of the fiber orientations in non-crimp glass fiber reinforced composites using structure tensorcitations
- 2019Process characterization for molding of paper bottles using computed tomography and structure tensor analysis
- 2017Individual fibre segmentation from 3D X-ray computed tomography for characterising the fibre orientation in unidirectional composite materialscitations
- 2017Graphite nodules in fatigue-tested cast iron characterized in 2D and 3Dcitations
- 2015Dictionary Based Segmentation in Volumescitations
- 2015Characterization of boundary roughness of two cube grains in partly recrystallized coppercitations
- 2015Boundary Fractal Analysis of Two Cube-oriented Grains in Partly Recrystallized Coppercitations
- 2014Surface Detection using Round Cutcitations
- 2014Pattern recognition approach to quantify the atomic structure of graphenecitations
- 2014Structure Identification in High-Resolution Transmission Electron Microscopic Imagescitations
- 2014Characterization of graphite nodules in thick-walled ductile cast iron
- 2014Quantification Tools for Analyzing Tomograms of Energy Materials
- 2013Automated Structure Detection in HRTEM Images: An Example with Graphene
- 2012Large scale tracking of stem cells using sparse coding and coupled graphs
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article
Characterization of the fiber orientations in non-crimp glass fiber reinforced composites using structure tensor
Abstract
The mechanical properties of composite fiber materials are highly dependent on the orientation of the fibers. Micro-CT enables acquisition of high-resolution 3D images, where individual fibers are visible. However, manually extracting orientation information from the samples is impractical due to the size of the 3D images. In this paper, we use a Structure Tensor to extract orientation information from a large 3D image of non-crimp glass fiber fabric. We go through the process of segmenting the image and extracting the orientation distribution step-by-step using structure tensor and show the results of the analysis of the studied non-crimp fabric. The Jupyter notebooks and Python code used for the data-analysis are publicly available, detailing the process and allowing the reader to use the method on their own data. The results show that structure tensor analysis works well for determining fiber orientations, which has many useful applications.