Experimental and artificial neural network approach for prediction of dynamic mechanical behavior of sisal/glass hybrid composites

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorFederal University for Latin American Integration (UNILA)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorFederal University of Rio Grande do Sul (UFRGS)-
Autor(es): dc.contributorUniversity of Caxias do Sul (UCS)-
Autor(es): dc.creatorOrnaghi, Heitor Luiz-
Autor(es): dc.creatorMonticeli, Francisco M [UNESP]-
Autor(es): dc.creatorNeves, Roberta Motta-
Autor(es): dc.creatorZattera, Ademir José-
Autor(es): dc.creatorAmico, Sandro Campos-
Data de aceite: dc.date.accessioned2022-08-04T22:11:45Z-
Data de disponibilização: dc.date.available2022-08-04T22:11:45Z-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2020-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1177/09673911211037829-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/222195-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/222195-
Descrição: dc.descriptionThe dynamic mechanical behavior (storage modulus, loss modulus, and tan δ) of hybrid sisal/glass composites was investigated in the temperature range of 30–210 °C, for two different volume percentages of reinforcement along with the different ratios of sisal and glass fibers. Based on the experimental outcome, an artificial neural network (ANN) approach was used to predict the dynamic mechanical properties followed by a surface response methodology (SRM). The ANN analysis showed an excellent fit with the storage modulus, loss modulus, and tan δ experimental data. In addition, the fitted curves with the ANN approach were used to propose equations based on SRM. The simulation result has shown that the ANN is a potential mathematical tool for the structure–property correlation for polymer composites and may help researchers in the development and application of their data, reducing the need for long experimental campaigns.-
Descrição: dc.descriptionFederal University for Latin American Integration (UNILA)-
Descrição: dc.descriptionDepartment of Materials and Technology School of Engineering São Paulo State University (Unesp)-
Descrição: dc.descriptionPostgraduate Program in Mining Metallurgical and Materials Engineering (PPGE3M) Federal University of Rio Grande do Sul (UFRGS)-
Descrição: dc.descriptionPostgraduate Program in Engineering of Processes and Technologies (PGEPROTEC) University of Caxias do Sul (UCS)-
Descrição: dc.descriptionDepartment of Materials and Technology School of Engineering São Paulo State University (Unesp)-
Idioma: dc.languageen-
Relação: dc.relationPolymers and Polymer Composites-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectartificial neural network-
Palavras-chave: dc.subjectHybrid composite-
Palavras-chave: dc.subjectstatistical properties/methods-
Palavras-chave: dc.subjectthermo-mechanical properties-
Palavras-chave: dc.subjectthermosetting resin-
Título: dc.titleExperimental and artificial neural network approach for prediction of dynamic mechanical behavior of sisal/glass hybrid composites-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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