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

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dc.contributor.authorMonticeli, Francisco M. [UNESP]
dc.contributor.authorAlmeida, José Humberto S.
dc.contributor.authorNeves, Roberta M.
dc.contributor.authorOrnaghi, Heitor L.
dc.contributor.authorTrochu, François
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.contributor.institutionAalto University
dc.contributor.institutionQueen's University Belfast
dc.contributor.institutionFederal University of Rio Grande do Sul
dc.contributor.institutionFederal University for Latin American Integration (UNILA) Foz do Iguaçu
dc.contributor.institutionPolytechnique Montréal
dc.date.accessioned2022-04-28T19:51:23Z
dc.date.available2022-04-28T19:51:23Z
dc.date.issued2022-01-01
dc.description.abstractThis 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.en
dc.description.affiliationDepartment of Materials and Technology São Paulo State University, São paulo
dc.description.affiliationDepartment of Mechanical Engineering Aalto University
dc.description.affiliationAdvanced Composites Research Group School of Mechanical and Aerospace Engineering Queen's University Belfast
dc.description.affiliationPPGE3M Federal University of Rio Grande do Sul
dc.description.affiliationDepartment of Material Engineering Federal University for Latin American Integration (UNILA) Foz do Iguaçu
dc.description.affiliationDepartment of Mechanical Engineering Research Center for High Performance Polymer and Composite Systems Polytechnique Montréal
dc.description.affiliationUnespDepartment of Materials and Technology São Paulo State University, São paulo
dc.identifierhttp://dx.doi.org/10.1002/pc.26578
dc.identifier.citationPolymer Composites.
dc.identifier.doi10.1002/pc.26578
dc.identifier.issn1548-0569
dc.identifier.issn0272-8397
dc.identifier.scopus2-s2.0-85125595013
dc.identifier.urihttp://hdl.handle.net/11449/223552
dc.language.isoeng
dc.relation.ispartofPolymer Composites
dc.sourceScopus
dc.subjectartificial neural network
dc.subjectpermeability
dc.subjectresin transfer molding process
dc.subjectvoid formation
dc.titleThe influence of fabric architecture on impregnation behavior and void formation: Artificial neural network and statistical-based analysisen
dc.typeArtigo
unesp.author.orcid0000-0002-0814-8160[1]
unesp.author.orcid0000-0002-9408-7674[2]
unesp.author.orcid0000-0002-7017-0852[3]
unesp.author.orcid0000-0002-0005-9534[4]
unesp.author.orcid0000-0003-3644-8408[5]

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