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: | 2012 |
Outros Autores: | , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://www.crcpress.com/product/isbn/9780415683951 http://hdl.handle.net/11449/73190 |
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 γ 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 γ 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. © 2012 Taylor & Francis Group. |
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Automatic segmentation of the secondary austenite-phase island precipitates in a superduplex stainless steel weld metalAutomatic segmentationsBayesian classifierChemical compositionsMachine learning techniquesPattern recognition techniquesRecognition ratesSteel qualitySuperduplex stainless steelsImage processingMechanical propertiesMedical image processingPattern recognitionStainless steelDuplex 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 γ 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 γ 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. © 2012 Taylor & Francis Group.Universidade de Fortal̀eza Centro de Ciências Tecnológicas, FortalezaDepartamento de Computação UNESP-Universidade Estadual Paulista, BauruDepartamento de Engenharia Metalúrgica e Materiais Universidade Federal do Ceará, FortalezaUniversidade do Porto Faculdade de Engenharia, PortoDepartamento de Computação UNESP-Universidade Estadual Paulista, BauruUniversidade de Fortaleza (UNIFOR)Universidade Estadual Paulista (Unesp)Universidade Federal do Ceará (UFC)Albuquerque, Victor H. C.Nakamura, Rodrigo Y. M. [UNESP]Papa, João Paulo [UNESP]Silva, Cleiton C.Tavares, João Manuel R. S.2014-05-27T11:26:23Z2014-05-27T11:26:23Z2012-02-13info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject161-166http://www.crcpress.com/product/isbn/9780415683951Computational Vision and Medical Image Processing, Proceedings of VipIMAGE 2011 - 3rd ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing, p. 161-166.http://hdl.handle.net/11449/731902-s2.0-848567315189039182932747194Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengComputational Vision and Medical Image Processing, Proceedings of VipIMAGE 2011 - 3rd ECCOMAS Thematic Conference on Computational Vision and Medical Image Processinginfo:eu-repo/semantics/openAccess2024-04-23T16:11:19Zoai:repositorio.unesp.br:11449/73190Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T15:48:51.059702Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
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 Albuquerque, Victor H. C. Automatic segmentations Bayesian classifier Chemical compositions Machine learning techniques Pattern recognition techniques Recognition rates Steel quality Superduplex stainless steels Image processing Mechanical properties Medical image processing Pattern recognition Stainless steel |
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 |
Albuquerque, Victor H. C. |
author_facet |
Albuquerque, Victor H. C. Nakamura, Rodrigo Y. M. [UNESP] Papa, João Paulo [UNESP] Silva, Cleiton C. Tavares, João Manuel R. S. |
author_role |
author |
author2 |
Nakamura, Rodrigo Y. M. [UNESP] Papa, João Paulo [UNESP] Silva, Cleiton C. Tavares, João Manuel R. S. |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade de Fortaleza (UNIFOR) Universidade Estadual Paulista (Unesp) Universidade Federal do Ceará (UFC) |
dc.contributor.author.fl_str_mv |
Albuquerque, Victor H. C. Nakamura, Rodrigo Y. M. [UNESP] Papa, João Paulo [UNESP] Silva, Cleiton C. Tavares, João Manuel R. S. |
dc.subject.por.fl_str_mv |
Automatic segmentations Bayesian classifier Chemical compositions Machine learning techniques Pattern recognition techniques Recognition rates Steel quality Superduplex stainless steels Image processing Mechanical properties Medical image processing Pattern recognition Stainless steel |
topic |
Automatic segmentations Bayesian classifier Chemical compositions Machine learning techniques Pattern recognition techniques Recognition rates Steel quality Superduplex stainless steels Image processing Mechanical properties Medical image processing Pattern recognition Stainless steel |
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 γ 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 γ 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. © 2012 Taylor & Francis Group. |
publishDate |
2012 |
dc.date.none.fl_str_mv |
2012-02-13 2014-05-27T11:26:23Z 2014-05-27T11:26:23Z |
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://www.crcpress.com/product/isbn/9780415683951 Computational Vision and Medical Image Processing, Proceedings of VipIMAGE 2011 - 3rd ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing, p. 161-166. http://hdl.handle.net/11449/73190 2-s2.0-84856731518 9039182932747194 |
url |
http://www.crcpress.com/product/isbn/9780415683951 http://hdl.handle.net/11449/73190 |
identifier_str_mv |
Computational Vision and Medical Image Processing, Proceedings of VipIMAGE 2011 - 3rd ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing, p. 161-166. 2-s2.0-84856731518 9039182932747194 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Computational Vision and Medical Image Processing, Proceedings of VipIMAGE 2011 - 3rd ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
161-166 |
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_ |
1808128566419783680 |