| 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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Ali, Muhammad A.
Heriot-Watt University
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (7/7 displayed)
- 2023Co-cured GNP films with liquid thermoplastic/glass fiber composites for superior EMI shielding and impact properties for space applicationscitations
- 2023MXene and graphene coated multifunctional fiber reinforced aerospace composites with sensing and EMI shielding abilitiescitations
- 2023Monitoring the thermomechanical response of aerospace composites under dynamic loading via embedded rGO coated fabric sensorscitations
- 2022In-situ monitoring of reinforcement compaction response via MXene-coated glass fabric sensorscitations
- 2022Deep learning based prediction of fibrous microstructure permeability
- 2022Deep learning accelerated prediction of the permeability of fibrous microstructurescitations
- 2020A virtual permeability measurement framework for fiber reinforcements using micro CT generated digital twinscitations
Places of action
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article
Deep learning accelerated prediction of the permeability of fibrous microstructures
Abstract
International audience ; Permeability of fibrous microstructures is a key material property for predicting the mold fill times and resin flow path during composite manufacturing. In this work, we report an efficient approach to predict the permeability of 3D microstructures from deep learning based permeability predictions of 2D cross-sections combined via a circuit analogy. After validating the network’s predictions in 2D and extending it to 3D, we investigate its capabilities for handling images of various sizes obtained from virtual and real microstructures. More than 90% of 2D predictions is within ± 30% of their counterparts obtained via flow simulations, similarly for 3D transverse permeability predictions, while in 3D case computational time is reduced from several thousands of seconds to less than 10 s. This work provides a robust and efficient framework for characterizing the permeability of fibrous microstructures and paves the way for extending this capability to estimate the permeability of fabric mesostructures.