Sensor fusion to estimate the depth and width of the weld bead in real time in GMAW processes

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.creatorAlvarez Bestard, Guillermo-
Autor(es): dc.creatorSampaio, Renato Coral-
Autor(es): dc.creatorVargas, José A. R.-
Autor(es): dc.creatorAlfaro, Sadek Crisóstomo Absi-
Data de aceite: dc.date.accessioned2021-10-14T18:07:49Z-
Data de disponibilização: dc.date.available2021-10-14T18:07:49Z-
Data de envio: dc.date.issued2020-12-09-
Data de envio: dc.date.issued2020-12-09-
Data de envio: dc.date.issued2018-03-23-
Fonte completa do material: dc.identifierhttps://repositorio.unb.br/handle/10482/39714-
Fonte completa do material: dc.identifierhttps://doi.org/10.3390/s18040962-
Fonte completa do material: dc.identifierhttps://orcid.org/0000-0001-6659-441X-
Fonte completa do material: dc.identifierhttps://orcid.org/0000-0002-0361-0555-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/624193-
Descrição: dc.descriptionThe arc welding process is widely used in industry but its automatic control is limited by the difficulty in measuring the weld bead geometry and closing the control loop on the arc, which has adverse environmental conditions. To address this problem, this work proposes a system to capture the welding variables and send stimuli to the Gas Metal Arc Welding (GMAW) conventional process with a constant voltage power source, which allows weld bead geometry estimation with an open-loop control. Dynamic models of depth and width estimators of the weld bead are implemented based on the fusion of thermographic data, welding current and welding voltage in a multilayer perceptron neural network. The estimators were trained and validated off-line with data from a novel algorithm developed to extract the features of the infrared image, a laser profilometer was implemented to measure the bead dimensions and an image processing algorithm that measures depth by making a longitudinal cut in the weld bead. These estimators are optimized for embedded devices and real-time processing and were implemented on a Field-Programmable Gate Array (FPGA) device. Experiments to collect data, train and validate the estimators are presented and discussed. The results show that the proposed method is useful in industrial and research environments.-
Formato: dc.formatapplication/pdf-
Publicador: dc.publisherMDPI-
Direitos: dc.rightsAcesso Aberto-
Direitos: dc.rights© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This 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/).-
Palavras-chave: dc.subjectSoldagem-
Palavras-chave: dc.subjectFPGA-
Palavras-chave: dc.subjectSoldagem GMAW-
Palavras-chave: dc.subjectRedes neurais (Computação)-
Palavras-chave: dc.subjectFusão de sensores-
Título: dc.titleSensor fusion to estimate the depth and width of the weld bead in real time in GMAW processes-
Tipo de arquivo: dc.typelivro digital-
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