Discontinuity detection in the shield metal arc welding process.

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MetadadosDescriçãoIdioma
Autor(es): dc.creatorCocota Júnior, José Alberto Naves-
Autor(es): dc.creatorGarcia, Gabriel Carvalho-
Autor(es): dc.creatorCosta, Adilson Rodrigues da-
Autor(es): dc.creatorLima, Milton Sérgio Fernandes de-
Autor(es): dc.creatorRocha, Filipe Augusto Santos-
Autor(es): dc.creatorFreitas, Gustavo Medeiros-
Data de aceite: dc.date.accessioned2025-08-21T15:20:46Z-
Data de disponibilização: dc.date.available2025-08-21T15:20:46Z-
Data de envio: dc.date.issued2017-11-08-
Data de envio: dc.date.issued2017-11-08-
Data de envio: dc.date.issued2017-
Fonte completa do material: dc.identifierhttp://www.repositorio.ufop.br/handle/123456789/9117-
Fonte completa do material: dc.identifierhttps://doi.org/10.3390/s17051082-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1011278-
Descrição: dc.descriptionThis work proposes a new methodology for the detection of discontinuities in the weld bead applied in Shielded Metal ArcWelding (SMAW) processes. The detection system is based on two sensors—a microphone and piezoelectric—that acquire acoustic emissions generated during the welding. The feature vectors extracted from the sensor dataset are used to construct classifier models. The approaches based on Artificial Neural Network (ANN) and Support Vector Machine (SVM) classifiers are able to identify with a high accuracy the three proposed weld bead classes: desirable weld bead, shrinkage cavity and burn through discontinuities. Experimental results illustrate the system’s high accuracy, greater than 90% for each class. A novel Hierarchical Support Vector Machine (HSVM) structure is proposed to make feasible the use of this system in industrial environments. This approach presented 96.6% overall accuracy. Given the simplicity of the equipment involved, this system can be applied in the metal transformation industries.-
Formato: dc.formatapplication/pdf-
Idioma: dc.languageen-
Direitos: dc.rightsaberto-
Direitos: dc.rightsThis article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Fonte: o próprio artigo.-
Palavras-chave: dc.subjectSupport vector machine-
Palavras-chave: dc.subjectArtificial neural network-
Palavras-chave: dc.subjectShielded metal arc welding-
Título: dc.titleDiscontinuity detection in the shield metal arc welding process.-
Aparece nas coleções:Repositório Institucional - UFOP

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