Predicting the mechanical behavior of carbon fiber-reinforced polymer using machine learning methods: a systematic review

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorTechnological Institute of Aeronautics (ITA)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorInstitute for Technological Research of the State of São Paulo (IPT)-
Autor(es): dc.creatorMonticeli, Francisco Maciel-
Autor(es): dc.creatorAlves, Fillip Cortat-
Autor(es): dc.creatorSantos, Luis Felipe de Paula-
Autor(es): dc.creatorCosta, Michelle Leali-
Autor(es): dc.creatorBotelho, Edson Cocchiere-
Data de aceite: dc.date.accessioned2025-08-21T15:18:51Z-
Data de disponibilização: dc.date.available2025-08-21T15:18:51Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2023-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/B978-0-443-18644-8.00012-5-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/308045-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/308045-
Descrição: dc.descriptionConsidering the complexity of the mechanic analysis in advanced composite materials, studies in the literature have demonstrated the use of machine learning (ML) methods, aiming to predict the mechanical properties in high-reliability levels. ML models have been also used in medical applications, biological sciences, and data control systems, presenting prospects in analyzing and modeling mechanical/thermal behavior for engineering applications. For this purpose, this chapter aims to conduct a systematic review of ML methods on the mechanical properties of structural composites. The analysis of the ML approach parameters and efficiency are also highlighted. A systematic review was performed using the PRISMA methodology to identify the main discoveries in recent studies. A total of 490 studies were initially identified from 2013 to 2022. Then, each article was selected and described by specific inclusion/exclusion criteria. The main findings were presented and discussed, and the gaps are identified to open up further investigations yet to be understood and exploited.-
Descrição: dc.descriptionDepartment of Aeronautical Engineering Technological Institute of Aeronautics (ITA), São Paulo-
Descrição: dc.descriptionDepartment of Materials and Technology São Paulo State University (UNESP), São Paulo-
Descrição: dc.descriptionLightweight Structures Laboratory (LEL) Institute for Technological Research of the State of São Paulo (IPT)-
Descrição: dc.descriptionDepartment of Materials and Technology São Paulo State University (UNESP), São Paulo-
Formato: dc.format193-233-
Idioma: dc.languageen-
Relação: dc.relationMachine Intelligence in Mechanical Engineering-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectCFRP composite-
Palavras-chave: dc.subjectmachine learning-
Palavras-chave: dc.subjectmechanical behavior-
Palavras-chave: dc.subjectprediction models-
Título: dc.titlePredicting the mechanical behavior of carbon fiber-reinforced polymer using machine learning methods: a systematic review-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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