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A multiscale deep l...
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Ghane, EhsanGothenburg University,Göteborgs universitet,Institutionen för fysik (GU),Department of Physics (GU),University of Gothenburg
(author)
A multiscale deep learning model for elastic properties of woven composites
- Article/chapterEnglish2023
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LIBRIS-ID:oai:gup.ub.gu.se/331031
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https://gup.ub.gu.se/publication/331031URI
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https://doi.org/10.1016/j.ijsolstr.2023.112452DOI
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https://research.chalmers.se/publication/537465URI
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Subject category:ref swepub-contenttype
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Subject category:art swepub-publicationtype
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Time-consuming and costly computational analysis expresses the need for new methods for generalizing multiscale analysis of composite materials. Combining neural networks and multiscale modeling is favorable for bypassing expensive lower-scale material modeling, and accelerating coupled multi-scale analyses (FE2). In this work, neural networks are used to replace the time-consuming micromechanical finite element analysis of unidirectional composites, representing the local material properties of yarns in woven fabric composites in a multiscale framework. Leveraging the fast multiscale data generation procedure, we presented a second neural networks model to estimate the elastic engineering coefficients of a particular weave architecture based on a broad range of dry resin and fiber properties and yarn fiber volume fraction. As an outcome, this paper provides the user with a generalized, neural network-based approach to tackle the balance of computational efficiency and accuracy in the multiscale analysis of elastic woven composites.
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Fagerström, Martin,1979Chalmers tekniska högskola,Chalmers University of Technology(Swepub:cth)fagmar
(author)
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Mirkhalaf, S. Mohsen,1982Gothenburg University,Göteborgs universitet,Institutionen för fysik (GU),Department of Physics (GU),University of Gothenburg(Swepub:gu)xmirmo
(author)
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Göteborgs universitetInstitutionen för fysik (GU)
(creator_code:org_t)
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In:International Journal of Solids and Structures2820020-7683
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