The influence of fabric architecture on impregnation behavior and void formation: Artificial neural network and statistical-based analysis

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Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorAalto University-
Autor(es): dc.contributorQueen's University Belfast-
Autor(es): dc.contributorFederal University of Rio Grande do Sul-
Autor(es): dc.contributorFederal University for Latin American Integration (UNILA) Foz do Iguaçu-
Autor(es): dc.contributorPolytechnique Montréal-
Autor(es): dc.creatorMonticeli, Francisco M.-
Autor(es): dc.creatorAlmeida, José Humberto S.-
Autor(es): dc.creatorNeves, Roberta M.-
Autor(es): dc.creatorOrnaghi, Heitor L.-
Autor(es): dc.creatorTrochu, François-
Data de aceite: dc.date.accessioned2025-08-21T22:35:11Z-
Data de disponibilização: dc.date.available2025-08-21T22:35:11Z-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2021-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1002/pc.26578-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/223552-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/223552-
Descrição: dc.descriptionThis work proposes an approach combining artificial neural networks (ANN) with statistical models to predict injection processing conditions for four reinforcement architectures: plain weave, bidirectional noncrimp fabrics, unidirectional fabrics (Uni) and random fiber mats (Random). Key results allow evaluating the velocity of the flow front by combining processing parameters and creating a three-dimensional response surface based on a properly trained ANN. This investigation is based on a large number of experimental results. The key role played by some physical parameters was associated with predicting the impregnation behavior (velocity of the flow front) during resin injection. The main outcome aims to provide a better control of void content in terms of size and position to the four fibrous reinforcements considered.-
Descrição: dc.descriptionDepartment of Materials and Technology São Paulo State University, São paulo-
Descrição: dc.descriptionDepartment of Mechanical Engineering Aalto University-
Descrição: dc.descriptionAdvanced Composites Research Group School of Mechanical and Aerospace Engineering Queen's University Belfast-
Descrição: dc.descriptionPPGE3M Federal University of Rio Grande do Sul-
Descrição: dc.descriptionDepartment of Material Engineering Federal University for Latin American Integration (UNILA) Foz do Iguaçu-
Descrição: dc.descriptionDepartment of Mechanical Engineering Research Center for High Performance Polymer and Composite Systems Polytechnique Montréal-
Descrição: dc.descriptionDepartment of Materials and Technology São Paulo State University, São paulo-
Idioma: dc.languageen-
Relação: dc.relationPolymer Composites-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectartificial neural network-
Palavras-chave: dc.subjectpermeability-
Palavras-chave: dc.subjectresin transfer molding process-
Palavras-chave: dc.subjectvoid formation-
Título: dc.titleThe influence of fabric architecture on impregnation behavior and void formation: Artificial neural network and statistical-based analysis-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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