The use of virtual sensors for bead size measurements in wire-arc directed energy deposition

Having garnered significant attention in the scientific community over the past decade, wire-arc directed energy deposition (arc-DED) technology is at the heart of this investigation into additive manufacturing parameters. Singularly focused on Invar as the selected material, the primary objective r...

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Autores: Fernández Zabalza, Aitor, Veiga Suárez, Fernando, Suárez, Alfredo, Alfaro López, José Ramón
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:España
Institución:Universidad Pública de Navarra
Repositorio:Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
OAI Identifier:oai:academica-e.unavarra.es:2454/51453
Acceso en línea:https://hdl.handle.net/2454/51453
Access Level:acceso abierto
Palabra clave:Wire-arc additive manufacturing
Invar
Wall geometry
Additive manufacturing monitoring
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spelling The use of virtual sensors for bead size measurements in wire-arc directed energy depositionFernández Zabalza, AitorVeiga Suárez, FernandoSuárez, AlfredoAlfaro López, José RamónWire-arc additive manufacturingInvarWall geometryAdditive manufacturing monitoringHaving garnered significant attention in the scientific community over the past decade, wire-arc directed energy deposition (arc-DED) technology is at the heart of this investigation into additive manufacturing parameters. Singularly focused on Invar as the selected material, the primary objective revolves around devising a virtual sensor for the indirect size measurement of the bead. This innovative methodology involves the seamless integration of internal signals and sensors, enabling the derivation of crucial measurements sans the requirement for direct physical interaction or conventional measurement methodologies. The internal signals recorded, the comprising voltage, the current, the energy from the welding heat source generator, the wire feed speed from the feeding system, the traverse speed from the machine axes, and the temperature from a pyrometer located in the head were all captured through the control of the machine specially dedicated to the arc-DED process during a phase of optimizing and modeling the bead geometry. Finally, a feedforward neural network (FNN), also known as a multi-layer perceptron (MLP), is designed, with the internal signals serving as the input and the height and width of the bead constituting the output. Remarkably cost-effective, this solution circumvents the need for intricate measurements and significantly contributes to the proper layer-by-layer growth process. Furthermore, a neural network model is implemented with a test loss of 0.144 and a test accuracy of 1.0 in order to predict weld bead geometry based on process parameters, thus offering a promising approach for real-time monitoring and defect detection.This study was supported as part of the ADDILANZA project by the Euroregion Nouvelle-Aquitaine Euskadi Navarra under the "CEuroregional Innovation" program.MDPIIngenieríaIngeniaritza2024info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/2454/51453reponame:Academica-e. Repositorio Institucional de la Universidad Pública de Navarrainstname:Universidad Pública de NavarraInglés© 2024 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:academica-e.unavarra.es:2454/514532026-06-17T12:41:47Z
dc.title.none.fl_str_mv The use of virtual sensors for bead size measurements in wire-arc directed energy deposition
title The use of virtual sensors for bead size measurements in wire-arc directed energy deposition
spellingShingle The use of virtual sensors for bead size measurements in wire-arc directed energy deposition
Fernández Zabalza, Aitor
Wire-arc additive manufacturing
Invar
Wall geometry
Additive manufacturing monitoring
title_short The use of virtual sensors for bead size measurements in wire-arc directed energy deposition
title_full The use of virtual sensors for bead size measurements in wire-arc directed energy deposition
title_fullStr The use of virtual sensors for bead size measurements in wire-arc directed energy deposition
title_full_unstemmed The use of virtual sensors for bead size measurements in wire-arc directed energy deposition
title_sort The use of virtual sensors for bead size measurements in wire-arc directed energy deposition
dc.creator.none.fl_str_mv Fernández Zabalza, Aitor
Veiga Suárez, Fernando
Suárez, Alfredo
Alfaro López, José Ramón
author Fernández Zabalza, Aitor
author_facet Fernández Zabalza, Aitor
Veiga Suárez, Fernando
Suárez, Alfredo
Alfaro López, José Ramón
author_role author
author2 Veiga Suárez, Fernando
Suárez, Alfredo
Alfaro López, José Ramón
author2_role author
author
author
dc.contributor.none.fl_str_mv Ingeniería
Ingeniaritza
dc.subject.none.fl_str_mv Wire-arc additive manufacturing
Invar
Wall geometry
Additive manufacturing monitoring
topic Wire-arc additive manufacturing
Invar
Wall geometry
Additive manufacturing monitoring
description Having garnered significant attention in the scientific community over the past decade, wire-arc directed energy deposition (arc-DED) technology is at the heart of this investigation into additive manufacturing parameters. Singularly focused on Invar as the selected material, the primary objective revolves around devising a virtual sensor for the indirect size measurement of the bead. This innovative methodology involves the seamless integration of internal signals and sensors, enabling the derivation of crucial measurements sans the requirement for direct physical interaction or conventional measurement methodologies. The internal signals recorded, the comprising voltage, the current, the energy from the welding heat source generator, the wire feed speed from the feeding system, the traverse speed from the machine axes, and the temperature from a pyrometer located in the head were all captured through the control of the machine specially dedicated to the arc-DED process during a phase of optimizing and modeling the bead geometry. Finally, a feedforward neural network (FNN), also known as a multi-layer perceptron (MLP), is designed, with the internal signals serving as the input and the height and width of the bead constituting the output. Remarkably cost-effective, this solution circumvents the need for intricate measurements and significantly contributes to the proper layer-by-layer growth process. Furthermore, a neural network model is implemented with a test loss of 0.144 and a test accuracy of 1.0 in order to predict weld bead geometry based on process parameters, thus offering a promising approach for real-time monitoring and defect detection.
publishDate 2024
dc.date.none.fl_str_mv 2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2454/51453
url https://hdl.handle.net/2454/51453
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv https://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
instname:Universidad Pública de Navarra
instname_str Universidad Pública de Navarra
reponame_str Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
collection Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
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