Investigations into the use of the finite element method and artificial neural networks in the non-destructive analysis of metallic tubes
Autor(a) principal: | |
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Data de Publicação: | 2002 |
Outros Autores: | , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/IJCNN.2002.1007730 http://hdl.handle.net/11449/66770 |
Resumo: | This work presents an investigation into the use of the finite element method and artificial neural networks in the identification of defects in industrial plants metallic tubes, due to the aggressive actions of the fluids contained by them, and/or atmospheric agents. The methodology used in this study consists of simulating a very large number of defects in a metallic tube, using the finite element method. Both variations in width and height of the defects are considered. Then, the obtained results are used to generate a set of vectors for the training of a perceptron multilayer artificial neural network. Finally, the obtained neural network is used to classify a group of new defects, simulated by the finite element method, but that do not belong to the original dataset. The reached results demonstrate the efficiency of the proposed approach, and encourage future works on this subject. |
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Repositório Institucional da UNESP |
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Investigations into the use of the finite element method and artificial neural networks in the non-destructive analysis of metallic tubesAgentsComputer simulationFinite element methodLeakage (fluid)Nondestructive examinationTubes (components)Atmospheric tubesMetallic tubesMultilayer neural networksThis work presents an investigation into the use of the finite element method and artificial neural networks in the identification of defects in industrial plants metallic tubes, due to the aggressive actions of the fluids contained by them, and/or atmospheric agents. The methodology used in this study consists of simulating a very large number of defects in a metallic tube, using the finite element method. Both variations in width and height of the defects are considered. Then, the obtained results are used to generate a set of vectors for the training of a perceptron multilayer artificial neural network. Finally, the obtained neural network is used to classify a group of new defects, simulated by the finite element method, but that do not belong to the original dataset. The reached results demonstrate the efficiency of the proposed approach, and encourage future works on this subject.Electrical Engineering Department São Paulo State Univ.-UNESP, 17033-360 - Bauru - SPElectrical Engineering Department São Paulo State Univ.-UNESP, 17033-360 - Bauru - SPUniversidade Estadual Paulista (Unesp)De Alcantara Jr., Naasson P. [UNESP]De Carvalho, Alexandre M. [UNESP]Ulson, Jose Alfredo Covolan [UNESP]2014-05-27T11:20:23Z2014-05-27T11:20:23Z2002-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject1450-1454http://dx.doi.org/10.1109/IJCNN.2002.1007730Proceedings of the International Joint Conference on Neural Networks, v. 2, p. 1450-1454.http://hdl.handle.net/11449/6677010.1109/IJCNN.2002.1007730WOS:0001774028002592-s2.0-00360887524517057121462258Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProceedings of the International Joint Conference on Neural Networksinfo:eu-repo/semantics/openAccess2024-06-28T13:34:43Zoai:repositorio.unesp.br:11449/66770Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T22:41:49.187452Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Investigations into the use of the finite element method and artificial neural networks in the non-destructive analysis of metallic tubes |
title |
Investigations into the use of the finite element method and artificial neural networks in the non-destructive analysis of metallic tubes |
spellingShingle |
Investigations into the use of the finite element method and artificial neural networks in the non-destructive analysis of metallic tubes De Alcantara Jr., Naasson P. [UNESP] Agents Computer simulation Finite element method Leakage (fluid) Nondestructive examination Tubes (components) Atmospheric tubes Metallic tubes Multilayer neural networks |
title_short |
Investigations into the use of the finite element method and artificial neural networks in the non-destructive analysis of metallic tubes |
title_full |
Investigations into the use of the finite element method and artificial neural networks in the non-destructive analysis of metallic tubes |
title_fullStr |
Investigations into the use of the finite element method and artificial neural networks in the non-destructive analysis of metallic tubes |
title_full_unstemmed |
Investigations into the use of the finite element method and artificial neural networks in the non-destructive analysis of metallic tubes |
title_sort |
Investigations into the use of the finite element method and artificial neural networks in the non-destructive analysis of metallic tubes |
author |
De Alcantara Jr., Naasson P. [UNESP] |
author_facet |
De Alcantara Jr., Naasson P. [UNESP] De Carvalho, Alexandre M. [UNESP] Ulson, Jose Alfredo Covolan [UNESP] |
author_role |
author |
author2 |
De Carvalho, Alexandre M. [UNESP] Ulson, Jose Alfredo Covolan [UNESP] |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
De Alcantara Jr., Naasson P. [UNESP] De Carvalho, Alexandre M. [UNESP] Ulson, Jose Alfredo Covolan [UNESP] |
dc.subject.por.fl_str_mv |
Agents Computer simulation Finite element method Leakage (fluid) Nondestructive examination Tubes (components) Atmospheric tubes Metallic tubes Multilayer neural networks |
topic |
Agents Computer simulation Finite element method Leakage (fluid) Nondestructive examination Tubes (components) Atmospheric tubes Metallic tubes Multilayer neural networks |
description |
This work presents an investigation into the use of the finite element method and artificial neural networks in the identification of defects in industrial plants metallic tubes, due to the aggressive actions of the fluids contained by them, and/or atmospheric agents. The methodology used in this study consists of simulating a very large number of defects in a metallic tube, using the finite element method. Both variations in width and height of the defects are considered. Then, the obtained results are used to generate a set of vectors for the training of a perceptron multilayer artificial neural network. Finally, the obtained neural network is used to classify a group of new defects, simulated by the finite element method, but that do not belong to the original dataset. The reached results demonstrate the efficiency of the proposed approach, and encourage future works on this subject. |
publishDate |
2002 |
dc.date.none.fl_str_mv |
2002-01-01 2014-05-27T11:20:23Z 2014-05-27T11:20:23Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1109/IJCNN.2002.1007730 Proceedings of the International Joint Conference on Neural Networks, v. 2, p. 1450-1454. http://hdl.handle.net/11449/66770 10.1109/IJCNN.2002.1007730 WOS:000177402800259 2-s2.0-0036088752 4517057121462258 |
url |
http://dx.doi.org/10.1109/IJCNN.2002.1007730 http://hdl.handle.net/11449/66770 |
identifier_str_mv |
Proceedings of the International Joint Conference on Neural Networks, v. 2, p. 1450-1454. 10.1109/IJCNN.2002.1007730 WOS:000177402800259 2-s2.0-0036088752 4517057121462258 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Proceedings of the International Joint Conference on Neural Networks |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
1450-1454 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808129452399394816 |