Structural damage classification in composite materials using the Wigner-Ville distribution and convolutional neural networks

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Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorMonson, Paulo Monteiro de Carvalho-
Autor(es): dc.creatorConceição Junior, Pedro de Oliveira-
Autor(es): dc.creatorLofrano Dotto, Fabio Romano-
Autor(es): dc.creatorAguiar, Paulo Roberto de-
Autor(es): dc.creatorRodrigues, Alessandro Roger-
Autor(es): dc.creatorDavid, Gabriel Augusto-
Data de aceite: dc.date.accessioned2025-08-21T22:50:25Z-
Data de disponibilização: dc.date.available2025-08-21T22:50:25Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-08-15-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.matlet.2024.136734-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/307036-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/307036-
Descrição: dc.descriptionDetecting damage in composite materials is crucial and has received considerable attention in the field of machine learning. However, challenges remain in addressing backbone issues and classifying specific types of damage. This paper presents a novel deep-learning approach for automatically distinguishing delamination and microcracks in Carbon Fiber-Reinforced Polymer (CFRP). The methodology utilizes signals from piezoelectric transducers transformed into time–frequency representations based on the Wigner-Ville Distribution (WVD). Backbone issues were successfully addressed by transitioning the time series classification problem into a computer vision (CV) context through Convolutional Neural Networks (CNN). The analysis included a thorough examination of delamination and microcracks datasets produced experimentally by the National Aeronautics and Space Administration (NASA), focusing on these two types of damage. The proposed methodology achieved a precision range of 98.3 % to 100 % in damage classification, demonstrating its effectiveness for structural health monitoring in composite materials.-
Descrição: dc.descriptionDepartment of Electrical and Computer Engineering USP, Av. Trabalhador São Carlense 400, SP-
Descrição: dc.descriptionDepartment of Electrical Engineering UNESP, Av. Eng. Luiz E. C. Coube 14-01, SP-
Descrição: dc.descriptionDepartment of Mechanical Engineering USP, Av. Trabalhador São Carlense 400, SP-
Descrição: dc.descriptionDepartment of Electrical Engineering UNESP, Av. Eng. Luiz E. C. Coube 14-01, SP-
Idioma: dc.languageen-
Relação: dc.relationMaterials Letters-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectComposite-
Palavras-chave: dc.subjectDeep Learning-
Palavras-chave: dc.subjectFailure-
Palavras-chave: dc.subjectFatigue-
Título: dc.titleStructural damage classification in composite materials using the Wigner-Ville distribution and convolutional neural networks-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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