Determining the Effect of the Main Alloying Elements on Localized Corrosion in Nickel Alloys Using Artificial Neural Networks

Autores
Sosa Haudet, Santiago; Rodríguez, Martín Alejandro; Carranza, Ricardo Mario
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Nickel base alloys are considered among candidate materials for engineered barriers of nuclear repositories. The localized corrosion resistance is a determining factor in materials selection for this application. This work compares the crevice corrosion resistance of several commercial nickel base alloys using artificial neural networks. The crevice corrosion repassivation potential of the tested alloys was determined by the potentiodynamic-galvanostatic-potentiodynamic (PD-GS-PD) method. The testing temperature was 60ªC and the chloride concentrations used were 0,1M, 1M and 10M. The results indicate that the repassivation potential increases linearly with the PREN (Pitting Resistant Equivalent Number) at high chloride concentrations. We also found a linear relationship between the repassivation potential and the logarithm of the concentration of chloride. Analysis from artificial neural networks presents distinctive patterns between the mayor alloying components and the chloride concentration and the repassivation potential. Predictions from artificial neural networks fit with successive tested commercial nickel alloys.
Fil: Sosa Haudet, Santiago. Comisión Nacional de Energía Atómica. Centro Atómico Constituyentes; Argentina. Universidad Nacional de San Martín. Instituto Sabato; Argentina
Fil: Rodríguez, Martín Alejandro. Comisión Nacional de Energía Atómica. Centro Atómico Constituyentes; Argentina. Universidad Nacional de San Martín. Instituto Sabato; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Carranza, Ricardo Mario. Comisión Nacional de Energía Atómica. Centro Atómico Constituyentes; Argentina. Universidad Nacional de San Martín. Instituto Sabato; Argentina
Materia
ARTIFICIAL NEURAL NETWORKS
CREVICE CORROSION
REPASSIVATION POTENTIAL
CHLORIDES
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:11336/111954
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Determining the Effect of the Main Alloying Elements on Localized Corrosion in Nickel Alloys Using Artificial Neural NetworksSosa Haudet, SantiagoRodríguez, Martín AlejandroCarranza, Ricardo MarioARTIFICIAL NEURAL NETWORKSCREVICE CORROSIONREPASSIVATION POTENTIALCHLORIDEShttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1https://purl.org/becyt/ford/2.5https://purl.org/becyt/ford/2Nickel base alloys are considered among candidate materials for engineered barriers of nuclear repositories. The localized corrosion resistance is a determining factor in materials selection for this application. This work compares the crevice corrosion resistance of several commercial nickel base alloys using artificial neural networks. The crevice corrosion repassivation potential of the tested alloys was determined by the potentiodynamic-galvanostatic-potentiodynamic (PD-GS-PD) method. The testing temperature was 60ªC and the chloride concentrations used were 0,1M, 1M and 10M. The results indicate that the repassivation potential increases linearly with the PREN (Pitting Resistant Equivalent Number) at high chloride concentrations. We also found a linear relationship between the repassivation potential and the logarithm of the concentration of chloride. Analysis from artificial neural networks presents distinctive patterns between the mayor alloying components and the chloride concentration and the repassivation potential. Predictions from artificial neural networks fit with successive tested commercial nickel alloys.Fil: Sosa Haudet, Santiago. Comisión Nacional de Energía Atómica. Centro Atómico Constituyentes; Argentina. Universidad Nacional de San Martín. Instituto Sabato; ArgentinaFil: Rodríguez, Martín Alejandro. Comisión Nacional de Energía Atómica. Centro Atómico Constituyentes; Argentina. Universidad Nacional de San Martín. Instituto Sabato; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Carranza, Ricardo Mario. Comisión Nacional de Energía Atómica. Centro Atómico Constituyentes; Argentina. Universidad Nacional de San Martín. Instituto Sabato; ArgentinaElsevier2015-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/111954Sosa Haudet, Santiago; Rodríguez, Martín Alejandro; Carranza, Ricardo Mario; Determining the Effect of the Main Alloying Elements on Localized Corrosion in Nickel Alloys Using Artificial Neural Networks; Elsevier; Procedia Materials Science; 8; 6-2015; 21-282211-8128CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S2211812815000450info:eu-repo/semantics/altIdentifier/doi/10.1016/j.mspro.2015.04.044info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:59:17Zoai:ri.conicet.gov.ar:11336/111954instacron: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:34982025-09-03 09:59:17.441CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Determining the Effect of the Main Alloying Elements on Localized Corrosion in Nickel Alloys Using Artificial Neural Networks
title Determining the Effect of the Main Alloying Elements on Localized Corrosion in Nickel Alloys Using Artificial Neural Networks
spellingShingle Determining the Effect of the Main Alloying Elements on Localized Corrosion in Nickel Alloys Using Artificial Neural Networks
Sosa Haudet, Santiago
ARTIFICIAL NEURAL NETWORKS
CREVICE CORROSION
REPASSIVATION POTENTIAL
CHLORIDES
title_short Determining the Effect of the Main Alloying Elements on Localized Corrosion in Nickel Alloys Using Artificial Neural Networks
title_full Determining the Effect of the Main Alloying Elements on Localized Corrosion in Nickel Alloys Using Artificial Neural Networks
title_fullStr Determining the Effect of the Main Alloying Elements on Localized Corrosion in Nickel Alloys Using Artificial Neural Networks
title_full_unstemmed Determining the Effect of the Main Alloying Elements on Localized Corrosion in Nickel Alloys Using Artificial Neural Networks
title_sort Determining the Effect of the Main Alloying Elements on Localized Corrosion in Nickel Alloys Using Artificial Neural Networks
dc.creator.none.fl_str_mv Sosa Haudet, Santiago
Rodríguez, Martín Alejandro
Carranza, Ricardo Mario
author Sosa Haudet, Santiago
author_facet Sosa Haudet, Santiago
Rodríguez, Martín Alejandro
Carranza, Ricardo Mario
author_role author
author2 Rodríguez, Martín Alejandro
Carranza, Ricardo Mario
author2_role author
author
dc.subject.none.fl_str_mv ARTIFICIAL NEURAL NETWORKS
CREVICE CORROSION
REPASSIVATION POTENTIAL
CHLORIDES
topic ARTIFICIAL NEURAL NETWORKS
CREVICE CORROSION
REPASSIVATION POTENTIAL
CHLORIDES
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
https://purl.org/becyt/ford/2.5
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Nickel base alloys are considered among candidate materials for engineered barriers of nuclear repositories. The localized corrosion resistance is a determining factor in materials selection for this application. This work compares the crevice corrosion resistance of several commercial nickel base alloys using artificial neural networks. The crevice corrosion repassivation potential of the tested alloys was determined by the potentiodynamic-galvanostatic-potentiodynamic (PD-GS-PD) method. The testing temperature was 60ªC and the chloride concentrations used were 0,1M, 1M and 10M. The results indicate that the repassivation potential increases linearly with the PREN (Pitting Resistant Equivalent Number) at high chloride concentrations. We also found a linear relationship between the repassivation potential and the logarithm of the concentration of chloride. Analysis from artificial neural networks presents distinctive patterns between the mayor alloying components and the chloride concentration and the repassivation potential. Predictions from artificial neural networks fit with successive tested commercial nickel alloys.
Fil: Sosa Haudet, Santiago. Comisión Nacional de Energía Atómica. Centro Atómico Constituyentes; Argentina. Universidad Nacional de San Martín. Instituto Sabato; Argentina
Fil: Rodríguez, Martín Alejandro. Comisión Nacional de Energía Atómica. Centro Atómico Constituyentes; Argentina. Universidad Nacional de San Martín. Instituto Sabato; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Carranza, Ricardo Mario. Comisión Nacional de Energía Atómica. Centro Atómico Constituyentes; Argentina. Universidad Nacional de San Martín. Instituto Sabato; Argentina
description Nickel base alloys are considered among candidate materials for engineered barriers of nuclear repositories. The localized corrosion resistance is a determining factor in materials selection for this application. This work compares the crevice corrosion resistance of several commercial nickel base alloys using artificial neural networks. The crevice corrosion repassivation potential of the tested alloys was determined by the potentiodynamic-galvanostatic-potentiodynamic (PD-GS-PD) method. The testing temperature was 60ªC and the chloride concentrations used were 0,1M, 1M and 10M. The results indicate that the repassivation potential increases linearly with the PREN (Pitting Resistant Equivalent Number) at high chloride concentrations. We also found a linear relationship between the repassivation potential and the logarithm of the concentration of chloride. Analysis from artificial neural networks presents distinctive patterns between the mayor alloying components and the chloride concentration and the repassivation potential. Predictions from artificial neural networks fit with successive tested commercial nickel alloys.
publishDate 2015
dc.date.none.fl_str_mv 2015-06
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/11336/111954
Sosa Haudet, Santiago; Rodríguez, Martín Alejandro; Carranza, Ricardo Mario; Determining the Effect of the Main Alloying Elements on Localized Corrosion in Nickel Alloys Using Artificial Neural Networks; Elsevier; Procedia Materials Science; 8; 6-2015; 21-28
2211-8128
CONICET Digital
CONICET
url http://hdl.handle.net/11336/111954
identifier_str_mv Sosa Haudet, Santiago; Rodríguez, Martín Alejandro; Carranza, Ricardo Mario; Determining the Effect of the Main Alloying Elements on Localized Corrosion in Nickel Alloys Using Artificial Neural Networks; Elsevier; Procedia Materials Science; 8; 6-2015; 21-28
2211-8128
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S2211812815000450
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.mspro.2015.04.044
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
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
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 13.13397

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