Please use this identifier to cite or link to this item: http://www.repositorio.ufop.br/handle/123456789/9117
Title: Discontinuity detection in the shield metal arc welding process.
Authors: Cocota Júnior, José Alberto Naves
Garcia, Gabriel Carvalho
Costa, Adilson Rodrigues da
Lima, Milton Sérgio Fernandes de
Rocha, Filipe Augusto Santos
Freitas, Gustavo Medeiros
Keywords: Support vector machine
Artificial neural network
Shielded metal arc welding
Issue Date: 2017
Citation: COCOTA JÚNIOR, J. A. N. et al. Discontinuity detection in the shield metal arc welding process. Sensors, v. 17, p. 1082, 2017. Disponível em: <http://www.mdpi.com/1424-8220/17/5/1082>. Acesso em: 29 set. 2017.
Abstract: This 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.
URI: http://www.repositorio.ufop.br/handle/123456789/9117
ISSN: 14248220
metadata.dc.rights.license: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license. Fonte: O próprio artigo.
Appears in Collections:DEMET - Artigos publicados em periódicos

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