| 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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Sinchuk, Yuriy
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (8/8 displayed)
- 2024A numerical multi-scale method for analyzing the rate-dependent and inelastic response of short fiber reinforced polymers : modeling framework and experimental validationcitations
- 2024Study of self-heating and local strain rate in polyamide-6 and short fibre glass/polyamide-6 under tension through synchronised full-field strain and temperature measurementscitations
- 2022A computationally efficient multi-scale strategy for predicting the elasto-plastic behaviour of short fiber composites
- 2022Sinchuk et al. Variational and Deep Learning Segmentation of Very-Low-Contrast X-ray Computed Tomography Images of Carbon/Epoxy Woven Composites
- 2022X-ray CT based multi-layer unit cell modeling of carbon fiber-reinforced textile composites: Segmentation, meshing and elastic property homogenizationcitations
- 2022A hierarchical multi-scale analytical approach for predicting the elastic behavior of short fiber reinforced polymers under triaxial and flexural loading conditionscitations
- 2021Geometrical and deep learning approaches for instance segmentation of CFRP fiber bundles in textile compositescitations
- 2020Variational and Deep Learning Segmentation of Very-Low-Contrast X-ray Computed Tomography Images of Carbon/Epoxy Woven Compositescitations
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article
X-ray CT based multi-layer unit cell modeling of carbon fiber-reinforced textile composites: Segmentation, meshing and elastic property homogenization
Abstract
International audience ; Generation of realistic finite element method (FEM) geometry of a textile composite material at tow scale remains a challenging stage of material modeling. In this paper, a FE model generation approach is introduced, based on micro-Computed Tomography (µ-CT) images of a carbon fiber reinforced textile composite with multiple layer orientations. For the specified laminate layup made of plain weave fabric layers, a tow instance segmentation method based on deep learning was proposed. The fiber local orientation field was extracted from the tow centerlines approximation. For the image-based finite element meshing, a material interface reconstruction algorithm was proposed. The algorithm leads to a conformal and smooth finite element (FE) mesh with high-quality elements. FE models with voxel and tetrahedral meshes considering different approximations of the material orientation field were generated and used for numerical homogenization of the composite material's elastic properties.