| 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
Places of action
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conferencepaper
A computationally efficient multi-scale strategy for predicting the elasto-plastic behaviour of short fiber composites
Abstract
Predicting the elasto-plastic response of short fiber reinforced polymers (SFRPs) is a challenging task due to the important effects of microstructural details (e.g. fiber interactions, orientations, volume fraction distribution, etc). The main goal of this study is to provide a straightforward framework for estimating the nonlinear response of SFRPs having complex microstructures using intrinsic physical properties of the matrix phase without using any reverse engineering. To do so, simplified 3D unit cells considering the effects of fiber interactions, are selected in order to predict the elasto-plastic response of SFRPs with aligned fibers (see Fig. 1). The effective mechanical responses of such 3D unit cells under different loading conditions are then used to calibrate the Hill plasticity model [1] to estimate anisotropic responses of SFRPs at microscopic levels. By coupling the obtained plasticity model with Pseudo-grain decomposition techniques [2, 3] as well as different orientation averaging approaches, the effects of fiber misalignments are taken into account. The numerical accuracy and computational efficiency of the employed unit cells are first studied by comparing the obtained results with those of multi-fiber RVEs with aligned fibers. Second, the validity and efficiency of the orientation averaging strategy are investigated using RVEs with randomly distributed fibers. The obtained results reveal that the proposed anisotropic Hill’s model calibrated with simple FEM unit cells largely reduces the number of required calibration tests and provides a computationally efficient framework to predict the nonlinear response of SFRPs while the effects of microstructural details are taken into account.