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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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Siddique, Shafaqat
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (5/5 displayed)
- 2023Exploring the elastic properties of woven fabric composites: a machine learning approach for improved analysis and designcitations
- 2018Simulation of cyclic deformation behavior of selective laser melted and hybrid-manufactured aluminum alloys using the phase-field method
- 2017Comparison of microstructure and mechanical properties of Scalmalloy® produced by selective laser melting and laser metal deposition
- 2015Fatigue Performance of Laser Additive Manufactured Ti–6al–4V in Very High Cycle Fatigue Regime up to 1E9 Cycles
- 2015Fatigue Performance of Laser Additive Manufactured Ti–6al–4V in Very High Cycle Fatigue Regime up to 1E9 Cycles
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document
Exploring the elastic properties of woven fabric composites: a machine learning approach for improved analysis and design
Abstract
Woven fabric reinforced plastic composites are highly favoured in the aerospace and automotive industries for their exceptional impact resistance and ease of manufacture. To design and analyse these structures, it is crucial to determine their elastic properties of woven fabric composites, which can be estimated through analytical, numerical, or experimental means. In this study, we propose a novel approach that combines machine learning techniques with finite-element methods based multi-scaling analysis methodology to predict the elastic behaviour of woven composites. The method leverages datasets generated from finite element methods based numerical simulations and literature to train and validate models, providing a cost-effective and computationally efficient alternative to conventional homogenization-based finite element method. The approach offers a promising solution to accurately predicting the elastic behaviour of woven fabric composites.<br/><br/>