| 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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Kallel, Achraf
Institut de Recherche Technologique SystemX
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (17/17 displayed)
- 2024Investigation of manufacturing process effects on microstructure and fatigue prediction in composite automotive tailgate designcitations
- 2024Investigation of manufacturing process effects on microstructure and fatigue prediction in composite automotive tailgate designcitations
- 2024Viscoelastic-damageable behavior of sheet molding compound (SMC) composites
- 2024Study of composite polymer degradation for high pressure hydrogen vessel by machine learning approachcitations
- 2024Investigation of manufacturing process effects on microstructure and fatigue prediction in composite automotive tailgate design ; Int J Adv Manuf Technolcitations
- 2023Manufacturing Process Effect on the Mechanical Properties of Glass Fiber/Polypropylene Composite Under High Strain Rate Loading: Woven (W-GF-PP) and Compressed GF50-PPcitations
- 2022Modeling of viscoelastic behavior of a shape memory polymer blendcitations
- 2021Molecular weight influence on shape memory effect of shape memory polymer blend (poly(caprolactone)/ styrene‐butadiene‐styrene )citations
- 2021Molecular weight influence on shape memory effect of shape memory polymer blend (poly(caprolactone)/<scp>styrene‐butadiene‐styrene</scp>)citations
- 2021Modeling of viscoelastic behavior of a shape memory polymer blendcitations
- 2020Molecular weight influence on shape memory effect of shape memory polymer blend (poly(caprolactone)/ styrene‐butadiene‐styrene )citations
- 2019Enzymatic Hydrolysis of Poly (Caprolactone) and its Blend with Styrene–Butadiene–Styrene (40% PCL/60% SBS)citations
- 2019Introduction of the Diffusion Stage into the Bubble Growth Model
- 2019Study of Bonding Formation between the Filaments of PLA in FFF Processcitations
- 2015Stability analysis of a polymer film casting problemcitations
- 2014Stability analysis of a polymer coating process
- 2013Hot pressing of thermoelectric materials for high temperature energy harvesting ; Compaction à chaud de nanopoudres SiGe : du process aux propriétés thermoélectriques
Places of action
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article
Study of composite polymer degradation for high pressure hydrogen vessel by machine learning approach
Abstract
International audience ; Abstract The aim of this article is to study the degradation of a composite material under static pressure. The high pressure condition is similar to the one encountered inside hydrogen tanks. Damage modeling was used to evaluate the behavior of hydrogen tanks to high pressure. A practical approach, coupling a finite element method (FEM) simulation and machine learning (ML) algorithm, is suggested. The representative volume element (RVE) was used in association with a choice of a behavior law and a damage law as an input data. Algorithms for ML classification such as K‐nearest neighbors (k‐NN) and a special k‐NN with a dynamic time warping metric were used. The hierarchical clustering through dendrograms visualizations allowed to exhibit the impact of composite parameters in relation to fiber, matrix properties and fiber volume fraction on the strain degradation under external static pressure. Continuing this, the optimum RVE which shows a low degradation value will be exhibited.