Automatic segmentation of the secondary austenite-phase island precipitates in a superduplex stainless steel weld metal
Autor(a) principal: | |
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Data de Publicação: | 2011 |
Outros Autores: | , , , |
Tipo de documento: | Livro |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/10216/56793 |
Resumo: | Duplex and superduplex stainless steels are class of materials of a high importance for engineering purposes, since they have good mechanical properties combination and also are very resistant to corrosion. It is known as well that the chemical composition of such steels is very important to maintain some desired properties. In the past years, some works have reported that gama 2 precipitation improves the toughness of such steels, and its quantification may reveals some important information about steel quality. Thus, we propose in this work the automatic segmentation of gama 2 precipitation using two pattern recognition techniques: Optimum-Path Forest (OPF) and a Bayesian classifier. To the best of our knowledge, this if the first time that machine learning techniques are applied into this area. The experimental results showed that both techniques achieved similar and good recognition rates. |
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7160 |
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Automatic segmentation of the secondary austenite-phase island precipitates in a superduplex stainless steel weld metalCiências Tecnológicas, Outras ciências da engenharia e tecnologiasTechnological sciences, Other engineering and technologiesDuplex and superduplex stainless steels are class of materials of a high importance for engineering purposes, since they have good mechanical properties combination and also are very resistant to corrosion. It is known as well that the chemical composition of such steels is very important to maintain some desired properties. In the past years, some works have reported that gama 2 precipitation improves the toughness of such steels, and its quantification may reveals some important information about steel quality. Thus, we propose in this work the automatic segmentation of gama 2 precipitation using two pattern recognition techniques: Optimum-Path Forest (OPF) and a Bayesian classifier. To the best of our knowledge, this if the first time that machine learning techniques are applied into this area. The experimental results showed that both techniques achieved similar and good recognition rates.20112011-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/bookapplication/pdfhttps://hdl.handle.net/10216/56793engVictor H. C. AlbuquerqueRodrigo Y. M. NakamuraJoão P. PapaCleiton C. SilvaJoão Manuel R. S.Tavaresinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-29T15:34:33Zoai:repositorio-aberto.up.pt:10216/56793Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:27:02.329073Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Automatic segmentation of the secondary austenite-phase island precipitates in a superduplex stainless steel weld metal |
title |
Automatic segmentation of the secondary austenite-phase island precipitates in a superduplex stainless steel weld metal |
spellingShingle |
Automatic segmentation of the secondary austenite-phase island precipitates in a superduplex stainless steel weld metal Victor H. C. Albuquerque Ciências Tecnológicas, Outras ciências da engenharia e tecnologias Technological sciences, Other engineering and technologies |
title_short |
Automatic segmentation of the secondary austenite-phase island precipitates in a superduplex stainless steel weld metal |
title_full |
Automatic segmentation of the secondary austenite-phase island precipitates in a superduplex stainless steel weld metal |
title_fullStr |
Automatic segmentation of the secondary austenite-phase island precipitates in a superduplex stainless steel weld metal |
title_full_unstemmed |
Automatic segmentation of the secondary austenite-phase island precipitates in a superduplex stainless steel weld metal |
title_sort |
Automatic segmentation of the secondary austenite-phase island precipitates in a superduplex stainless steel weld metal |
author |
Victor H. C. Albuquerque |
author_facet |
Victor H. C. Albuquerque Rodrigo Y. M. Nakamura João P. Papa Cleiton C. Silva João Manuel R. S.Tavares |
author_role |
author |
author2 |
Rodrigo Y. M. Nakamura João P. Papa Cleiton C. Silva João Manuel R. S.Tavares |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Victor H. C. Albuquerque Rodrigo Y. M. Nakamura João P. Papa Cleiton C. Silva João Manuel R. S.Tavares |
dc.subject.por.fl_str_mv |
Ciências Tecnológicas, Outras ciências da engenharia e tecnologias Technological sciences, Other engineering and technologies |
topic |
Ciências Tecnológicas, Outras ciências da engenharia e tecnologias Technological sciences, Other engineering and technologies |
description |
Duplex and superduplex stainless steels are class of materials of a high importance for engineering purposes, since they have good mechanical properties combination and also are very resistant to corrosion. It is known as well that the chemical composition of such steels is very important to maintain some desired properties. In the past years, some works have reported that gama 2 precipitation improves the toughness of such steels, and its quantification may reveals some important information about steel quality. Thus, we propose in this work the automatic segmentation of gama 2 precipitation using two pattern recognition techniques: Optimum-Path Forest (OPF) and a Bayesian classifier. To the best of our knowledge, this if the first time that machine learning techniques are applied into this area. The experimental results showed that both techniques achieved similar and good recognition rates. |
publishDate |
2011 |
dc.date.none.fl_str_mv |
2011 2011-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/book |
format |
book |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://hdl.handle.net/10216/56793 |
url |
https://hdl.handle.net/10216/56793 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799136183467376640 |