An artificial neural network (ANN) model for predicting water absorption of nanoclay-epoxy composites

Autores
Capiel, Guillermina; Arrosio, Florencia; Alvarez, Vera Alejandra; Montemartini, Pablo Ezequiel; Morán, Juan
Año de publicación
2019
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Glass fiber reinforced epoxy (GFRE) composite materials are prone to suffer from water absorption due to their heterogeneous structure. The main process governing water absorption is diffusion of water molecules through the epoxy matrix. However, hydrolytic degradation may also take place during components service life specially due high temperatures. In order to mitigate the effects of the water diffusive processes in the deterioration of in-service behavior of epoxy matrix composites, the use of chemically modified nanoclays as an additive has been proposed and studied in previous works [1]. In this work, an Artificial Neural Network (ANN) model was developed for better understanding and predicting the influence of modified and unmodified bentonite addition on the water absorption behavior of epoxy-anhydride systems. An excellent correlation between model and experimental data was found. The ANN model allowed the identification of critical points like the precise temperature at which a particular system?s water uptake goes beyond a predefined threshold, or which system will resist an immersion longer than a particular time.
Fil: Capiel, Guillermina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones en Ciencia y Tecnología de Materiales. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones en Ciencia y Tecnología de Materiales; Argentina
Fil: Arrosio, Florencia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones en Ciencia y Tecnología de Materiales. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones en Ciencia y Tecnología de Materiales; Argentina
Fil: Alvarez, Vera Alejandra. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones en Ciencia y Tecnología de Materiales. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones en Ciencia y Tecnología de Materiales; Argentina
Fil: Montemartini, Pablo Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones en Ciencia y Tecnología de Materiales. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones en Ciencia y Tecnología de Materiales; Argentina
Fil: Morán, Juan. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones en Ciencia y Tecnología de Materiales. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones en Ciencia y Tecnología de Materiales; Argentina
Materia
Artificial Neural Networks
Epoxy-Anhydride
Clay Nanocomposites
Water Absorption
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/121284
Acceder
id CONICETDig_bf3780487edba36eb4214400c059b2d1
oai_identifier_str oai:ri.conicet.gov.ar:11336/121284
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling An artificial neural network (ANN) model for predicting water absorption of nanoclay-epoxy compositesCapiel, GuillerminaArrosio, FlorenciaAlvarez, Vera AlejandraMontemartini, Pablo EzequielMorán, JuanArtificial Neural NetworksEpoxy-AnhydrideClay NanocompositesWater Absorptionhttps://purl.org/becyt/ford/2.5https://purl.org/becyt/ford/2Glass fiber reinforced epoxy (GFRE) composite materials are prone to suffer from water absorption due to their heterogeneous structure. The main process governing water absorption is diffusion of water molecules through the epoxy matrix. However, hydrolytic degradation may also take place during components service life specially due high temperatures. In order to mitigate the effects of the water diffusive processes in the deterioration of in-service behavior of epoxy matrix composites, the use of chemically modified nanoclays as an additive has been proposed and studied in previous works [1]. In this work, an Artificial Neural Network (ANN) model was developed for better understanding and predicting the influence of modified and unmodified bentonite addition on the water absorption behavior of epoxy-anhydride systems. An excellent correlation between model and experimental data was found. The ANN model allowed the identification of critical points like the precise temperature at which a particular system?s water uptake goes beyond a predefined threshold, or which system will resist an immersion longer than a particular time.Fil: Capiel, Guillermina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones en Ciencia y Tecnología de Materiales. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones en Ciencia y Tecnología de Materiales; ArgentinaFil: Arrosio, Florencia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones en Ciencia y Tecnología de Materiales. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones en Ciencia y Tecnología de Materiales; ArgentinaFil: Alvarez, Vera Alejandra. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones en Ciencia y Tecnología de Materiales. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones en Ciencia y Tecnología de Materiales; ArgentinaFil: Montemartini, Pablo Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones en Ciencia y Tecnología de Materiales. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones en Ciencia y Tecnología de Materiales; ArgentinaFil: Morán, Juan. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones en Ciencia y Tecnología de Materiales. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones en Ciencia y Tecnología de Materiales; ArgentinaScientific Research Publishing Inc.2019-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/121284Capiel, Guillermina; Arrosio, Florencia; Alvarez, Vera Alejandra; Montemartini, Pablo Ezequiel; Morán, Juan; An artificial neural network (ANN) model for predicting water absorption of nanoclay-epoxy composites; Scientific Research Publishing Inc.; Journal of Materials Science and Chemical Engineering; 07; 8; 7-2019; 87-972327-6053CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.scirp.org/journal/doi.aspx?DOI=10.4236/msce.2019.78010info:eu-repo/semantics/altIdentifier/doi/10.4236/msce.2019.78010info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:59:28Zoai:ri.conicet.gov.ar:11336/121284instacron: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 09:59:28.512CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv An artificial neural network (ANN) model for predicting water absorption of nanoclay-epoxy composites
title An artificial neural network (ANN) model for predicting water absorption of nanoclay-epoxy composites
spellingShingle An artificial neural network (ANN) model for predicting water absorption of nanoclay-epoxy composites
Capiel, Guillermina
Artificial Neural Networks
Epoxy-Anhydride
Clay Nanocomposites
Water Absorption
title_short An artificial neural network (ANN) model for predicting water absorption of nanoclay-epoxy composites
title_full An artificial neural network (ANN) model for predicting water absorption of nanoclay-epoxy composites
title_fullStr An artificial neural network (ANN) model for predicting water absorption of nanoclay-epoxy composites
title_full_unstemmed An artificial neural network (ANN) model for predicting water absorption of nanoclay-epoxy composites
title_sort An artificial neural network (ANN) model for predicting water absorption of nanoclay-epoxy composites
dc.creator.none.fl_str_mv Capiel, Guillermina
Arrosio, Florencia
Alvarez, Vera Alejandra
Montemartini, Pablo Ezequiel
Morán, Juan
author Capiel, Guillermina
author_facet Capiel, Guillermina
Arrosio, Florencia
Alvarez, Vera Alejandra
Montemartini, Pablo Ezequiel
Morán, Juan
author_role author
author2 Arrosio, Florencia
Alvarez, Vera Alejandra
Montemartini, Pablo Ezequiel
Morán, Juan
author2_role author
author
author
author
dc.subject.none.fl_str_mv Artificial Neural Networks
Epoxy-Anhydride
Clay Nanocomposites
Water Absorption
topic Artificial Neural Networks
Epoxy-Anhydride
Clay Nanocomposites
Water Absorption
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.5
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Glass fiber reinforced epoxy (GFRE) composite materials are prone to suffer from water absorption due to their heterogeneous structure. The main process governing water absorption is diffusion of water molecules through the epoxy matrix. However, hydrolytic degradation may also take place during components service life specially due high temperatures. In order to mitigate the effects of the water diffusive processes in the deterioration of in-service behavior of epoxy matrix composites, the use of chemically modified nanoclays as an additive has been proposed and studied in previous works [1]. In this work, an Artificial Neural Network (ANN) model was developed for better understanding and predicting the influence of modified and unmodified bentonite addition on the water absorption behavior of epoxy-anhydride systems. An excellent correlation between model and experimental data was found. The ANN model allowed the identification of critical points like the precise temperature at which a particular system?s water uptake goes beyond a predefined threshold, or which system will resist an immersion longer than a particular time.
Fil: Capiel, Guillermina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones en Ciencia y Tecnología de Materiales. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones en Ciencia y Tecnología de Materiales; Argentina
Fil: Arrosio, Florencia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones en Ciencia y Tecnología de Materiales. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones en Ciencia y Tecnología de Materiales; Argentina
Fil: Alvarez, Vera Alejandra. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones en Ciencia y Tecnología de Materiales. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones en Ciencia y Tecnología de Materiales; Argentina
Fil: Montemartini, Pablo Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones en Ciencia y Tecnología de Materiales. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones en Ciencia y Tecnología de Materiales; Argentina
Fil: Morán, Juan. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones en Ciencia y Tecnología de Materiales. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones en Ciencia y Tecnología de Materiales; Argentina
description Glass fiber reinforced epoxy (GFRE) composite materials are prone to suffer from water absorption due to their heterogeneous structure. The main process governing water absorption is diffusion of water molecules through the epoxy matrix. However, hydrolytic degradation may also take place during components service life specially due high temperatures. In order to mitigate the effects of the water diffusive processes in the deterioration of in-service behavior of epoxy matrix composites, the use of chemically modified nanoclays as an additive has been proposed and studied in previous works [1]. In this work, an Artificial Neural Network (ANN) model was developed for better understanding and predicting the influence of modified and unmodified bentonite addition on the water absorption behavior of epoxy-anhydride systems. An excellent correlation between model and experimental data was found. The ANN model allowed the identification of critical points like the precise temperature at which a particular system?s water uptake goes beyond a predefined threshold, or which system will resist an immersion longer than a particular time.
publishDate 2019
dc.date.none.fl_str_mv 2019-07
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/11336/121284
Capiel, Guillermina; Arrosio, Florencia; Alvarez, Vera Alejandra; Montemartini, Pablo Ezequiel; Morán, Juan; An artificial neural network (ANN) model for predicting water absorption of nanoclay-epoxy composites; Scientific Research Publishing Inc.; Journal of Materials Science and Chemical Engineering; 07; 8; 7-2019; 87-97
2327-6053
CONICET Digital
CONICET
url http://hdl.handle.net/11336/121284
identifier_str_mv Capiel, Guillermina; Arrosio, Florencia; Alvarez, Vera Alejandra; Montemartini, Pablo Ezequiel; Morán, Juan; An artificial neural network (ANN) model for predicting water absorption of nanoclay-epoxy composites; Scientific Research Publishing Inc.; Journal of Materials Science and Chemical Engineering; 07; 8; 7-2019; 87-97
2327-6053
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.scirp.org/journal/doi.aspx?DOI=10.4236/msce.2019.78010
info:eu-repo/semantics/altIdentifier/doi/10.4236/msce.2019.78010
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Scientific Research Publishing Inc.
publisher.none.fl_str_mv Scientific Research Publishing Inc.
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
_version_ 1842269582032633856
score 13.13397

Publicaciones similares