Global Fault Detection in Adhesively Bonded Joints Using Artificial Intelligence

Autores
Zapico, Adriana Maria; Molisani Yolitti, Leonardo; Del Real, J. C.; Ballesteros, Yamila
Año de publicación
2011
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In general, non-destructive evaluation is applied to detect and localize structural faults using a signal with a wavelength smaller than the detected fault. But the method requires analyzing the object in numerous small sections to detect the damage. Non-invasive diagnosis methods for fault detection are used in different industrial sectors. In this work, the main focus is on global fault detection for structural mechanical components such as a bonded beam using artificial intelligence, i.e., neural nets. Therefore, the fault detection procedure requires only a global measurement in the structural component in operational conditions. An experimental setup using two aluminum beams bonded with an adhesive was used to simulate a bonded joint. Different sizes of adhesive surface simulate faults in the original adhesive joint. Thereafter, resonance frequency shifts in the Frequency Response Functions (FRFs) were used to detect structural faults. Damage in structures causes small changes in the structural resonances. Then, the FRFs were used as an input into an artificial supervised neural network. This work considers global non-destructive tests focused only on the soundness estimation of the system. The neural network involved is a supervised feed-forward network with Levenberg-Marquardt backpropagation algorithm, which classifies the beams in four clusters. The classification consists in beam damaged or not damaged. If the beam is damaged the intensity of the fault is established.
Fil: Zapico, Adriana Maria. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina
Fil: Molisani Yolitti, Leonardo. Universidad Nacional de Río Cuarto. Facultad de Ingeniería. Departamento de Mecánica. Grupo de Acústica y Vibraciones; Argentina
Fil: Del Real, J. C.. Universidad Pontificia Comillas de Madrid; España
Fil: Ballesteros, Yamila. Universidad Pontificia Comillas de Madrid; España
Materia
BONDED JOINTS
FAULT DIAGNOSIS
FREQUENCY RESPONSE FUNCTIONS (FRFS)
NEURAL NETWORKS
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/193492
Acceder
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Global Fault Detection in Adhesively Bonded Joints Using Artificial IntelligenceZapico, Adriana MariaMolisani Yolitti, LeonardoDel Real, J. C.Ballesteros, YamilaBONDED JOINTSFAULT DIAGNOSISFREQUENCY RESPONSE FUNCTIONS (FRFS)NEURAL NETWORKShttps://purl.org/becyt/ford/2.3https://purl.org/becyt/ford/2In general, non-destructive evaluation is applied to detect and localize structural faults using a signal with a wavelength smaller than the detected fault. But the method requires analyzing the object in numerous small sections to detect the damage. Non-invasive diagnosis methods for fault detection are used in different industrial sectors. In this work, the main focus is on global fault detection for structural mechanical components such as a bonded beam using artificial intelligence, i.e., neural nets. Therefore, the fault detection procedure requires only a global measurement in the structural component in operational conditions. An experimental setup using two aluminum beams bonded with an adhesive was used to simulate a bonded joint. Different sizes of adhesive surface simulate faults in the original adhesive joint. Thereafter, resonance frequency shifts in the Frequency Response Functions (FRFs) were used to detect structural faults. Damage in structures causes small changes in the structural resonances. Then, the FRFs were used as an input into an artificial supervised neural network. This work considers global non-destructive tests focused only on the soundness estimation of the system. The neural network involved is a supervised feed-forward network with Levenberg-Marquardt backpropagation algorithm, which classifies the beams in four clusters. The classification consists in beam damaged or not damaged. If the beam is damaged the intensity of the fault is established.Fil: Zapico, Adriana Maria. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; ArgentinaFil: Molisani Yolitti, Leonardo. Universidad Nacional de Río Cuarto. Facultad de Ingeniería. Departamento de Mecánica. Grupo de Acústica y Vibraciones; ArgentinaFil: Del Real, J. C.. Universidad Pontificia Comillas de Madrid; EspañaFil: Ballesteros, Yamila. Universidad Pontificia Comillas de Madrid; EspañaBrill Academic Publishers2011-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/193492Zapico, Adriana Maria; Molisani Yolitti, Leonardo; Del Real, J. C.; Ballesteros, Yamila; Global Fault Detection in Adhesively Bonded Joints Using Artificial Intelligence; Brill Academic Publishers; Journal of Adhesion Science and Technology; 25; 18; 8-2011; 2435-24430169-42431568-5616CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.tandfonline.com/doi/abs/10.1163/016942411X580126info:eu-repo/semantics/altIdentifier/doi/10.1163/016942411X580126info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T12:19:54Zoai:ri.conicet.gov.ar:11336/193492instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-22 12:19:54.493CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Global Fault Detection in Adhesively Bonded Joints Using Artificial Intelligence
title Global Fault Detection in Adhesively Bonded Joints Using Artificial Intelligence
spellingShingle Global Fault Detection in Adhesively Bonded Joints Using Artificial Intelligence
Zapico, Adriana Maria
BONDED JOINTS
FAULT DIAGNOSIS
FREQUENCY RESPONSE FUNCTIONS (FRFS)
NEURAL NETWORKS
title_short Global Fault Detection in Adhesively Bonded Joints Using Artificial Intelligence
title_full Global Fault Detection in Adhesively Bonded Joints Using Artificial Intelligence
title_fullStr Global Fault Detection in Adhesively Bonded Joints Using Artificial Intelligence
title_full_unstemmed Global Fault Detection in Adhesively Bonded Joints Using Artificial Intelligence
title_sort Global Fault Detection in Adhesively Bonded Joints Using Artificial Intelligence
dc.creator.none.fl_str_mv Zapico, Adriana Maria
Molisani Yolitti, Leonardo
Del Real, J. C.
Ballesteros, Yamila
author Zapico, Adriana Maria
author_facet Zapico, Adriana Maria
Molisani Yolitti, Leonardo
Del Real, J. C.
Ballesteros, Yamila
author_role author
author2 Molisani Yolitti, Leonardo
Del Real, J. C.
Ballesteros, Yamila
author2_role author
author
author
dc.subject.none.fl_str_mv BONDED JOINTS
FAULT DIAGNOSIS
FREQUENCY RESPONSE FUNCTIONS (FRFS)
NEURAL NETWORKS
topic BONDED JOINTS
FAULT DIAGNOSIS
FREQUENCY RESPONSE FUNCTIONS (FRFS)
NEURAL NETWORKS
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.3
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv In general, non-destructive evaluation is applied to detect and localize structural faults using a signal with a wavelength smaller than the detected fault. But the method requires analyzing the object in numerous small sections to detect the damage. Non-invasive diagnosis methods for fault detection are used in different industrial sectors. In this work, the main focus is on global fault detection for structural mechanical components such as a bonded beam using artificial intelligence, i.e., neural nets. Therefore, the fault detection procedure requires only a global measurement in the structural component in operational conditions. An experimental setup using two aluminum beams bonded with an adhesive was used to simulate a bonded joint. Different sizes of adhesive surface simulate faults in the original adhesive joint. Thereafter, resonance frequency shifts in the Frequency Response Functions (FRFs) were used to detect structural faults. Damage in structures causes small changes in the structural resonances. Then, the FRFs were used as an input into an artificial supervised neural network. This work considers global non-destructive tests focused only on the soundness estimation of the system. The neural network involved is a supervised feed-forward network with Levenberg-Marquardt backpropagation algorithm, which classifies the beams in four clusters. The classification consists in beam damaged or not damaged. If the beam is damaged the intensity of the fault is established.
Fil: Zapico, Adriana Maria. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina
Fil: Molisani Yolitti, Leonardo. Universidad Nacional de Río Cuarto. Facultad de Ingeniería. Departamento de Mecánica. Grupo de Acústica y Vibraciones; Argentina
Fil: Del Real, J. C.. Universidad Pontificia Comillas de Madrid; España
Fil: Ballesteros, Yamila. Universidad Pontificia Comillas de Madrid; España
description In general, non-destructive evaluation is applied to detect and localize structural faults using a signal with a wavelength smaller than the detected fault. But the method requires analyzing the object in numerous small sections to detect the damage. Non-invasive diagnosis methods for fault detection are used in different industrial sectors. In this work, the main focus is on global fault detection for structural mechanical components such as a bonded beam using artificial intelligence, i.e., neural nets. Therefore, the fault detection procedure requires only a global measurement in the structural component in operational conditions. An experimental setup using two aluminum beams bonded with an adhesive was used to simulate a bonded joint. Different sizes of adhesive surface simulate faults in the original adhesive joint. Thereafter, resonance frequency shifts in the Frequency Response Functions (FRFs) were used to detect structural faults. Damage in structures causes small changes in the structural resonances. Then, the FRFs were used as an input into an artificial supervised neural network. This work considers global non-destructive tests focused only on the soundness estimation of the system. The neural network involved is a supervised feed-forward network with Levenberg-Marquardt backpropagation algorithm, which classifies the beams in four clusters. The classification consists in beam damaged or not damaged. If the beam is damaged the intensity of the fault is established.
publishDate 2011
dc.date.none.fl_str_mv 2011-08
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/193492
Zapico, Adriana Maria; Molisani Yolitti, Leonardo; Del Real, J. C.; Ballesteros, Yamila; Global Fault Detection in Adhesively Bonded Joints Using Artificial Intelligence; Brill Academic Publishers; Journal of Adhesion Science and Technology; 25; 18; 8-2011; 2435-2443
0169-4243
1568-5616
CONICET Digital
CONICET
url http://hdl.handle.net/11336/193492
identifier_str_mv Zapico, Adriana Maria; Molisani Yolitti, Leonardo; Del Real, J. C.; Ballesteros, Yamila; Global Fault Detection in Adhesively Bonded Joints Using Artificial Intelligence; Brill Academic Publishers; Journal of Adhesion Science and Technology; 25; 18; 8-2011; 2435-2443
0169-4243
1568-5616
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.tandfonline.com/doi/abs/10.1163/016942411X580126
info:eu-repo/semantics/altIdentifier/doi/10.1163/016942411X580126
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Brill Academic Publishers
publisher.none.fl_str_mv Brill Academic Publishers
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 12.982451

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