Metamodelling the hot deformation behaviour of titanium alloys using a mean-field approach
- Autores
- Miller Branco Ferraz, Franz; Sztangret, Lukasz; Carazo, Fernando Diego; Buzolin, Ricardo Henrique; Wang, Peng; Szeliga, Danuta; dos Santos Effertz, Pedro; Macio, Piotr; Krumphals, Alfred; Poletti, Maria Cecilia
- Año de publicación
- 2023
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- During the thermomechanical processing of titanium alloys in the β-domain, the β-phase undergoes restoration phenomena. This work describes them by a mean-field physical model that correlates the flow stress with the microstructural evolution. To reduce the computational time of process simulations, metamodels are developed for specific outputs of the mean-field physical model using Artificial Neural Network (ANN) and Decision Tree Regression (DTR). The performance of the obtained metamodels is evaluated in terms of the coefficient of determination (R²), the root-mean-square error (RMSE), and the mean relative error (MRE). No significant difference was observed between R2training and R2testing, meaning that all the metamodels correctly generalise the overall behaviour of the outputs for a wide range of inputs. The evolution of the metamodel outputs is compared with the model predictions in two different situations: 1) at a constant strain rate and temperature, and 2) during Finite Element (FE) simulations of the hot deformation of a hat-shaped sample, where temperature and effective strain rate vary at each element during deformation. The evolution of the outputs at constant and non-constant strain rates and temperature demonstrated the robustness of the metamodels in predicting the heterogeneous deformation within a workpiece. The computational time required by the metamodels to calculate selected outputs can be more than 100 times less than that of the model itself at a constant strain rate using MATLAB® and up to 19% less when coupled with FE simulations. The simulation results combined with microstructural analysis are used to visualise the different restoration mechanisms occurring in different regions of the hat-shaped sample as a function of the local thermomechanical history. The changes in strain rate and temperature during deformation influence the evolution of the wall dislocation density and the immobilisation rate of mobile dislocations at subgrain boundaries, leading to different kinetics of microstructure evolution.
Fil: Miller Branco Ferraz, Franz. Graz University Of Technology.; Austria
Fil: Sztangret, Lukasz. AGH University of Science and Technology; Polonia
Fil: Carazo, Fernando Diego. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Mecanica Aplicada; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Buzolin, Ricardo Henrique. Graz University Of Technology.; Austria
Fil: Wang, Peng. Graz University Of Technology.; Austria
Fil: Szeliga, Danuta. AGH University of Science and Technology; Polonia
Fil: dos Santos Effertz, Pedro. No especifíca;
Fil: Macio, Piotr. AGH University of Science and Technology; Polonia
Fil: Krumphals, Alfred. No especifíca;
Fil: Poletti, Maria Cecilia. Graz University Of Technology.; Austria - Materia
-
ARTIFICIAL NEURAL NETWORK
DECISION-TREE REGRESSION
HOT DEFORMATION
MEAN-FIELD MODEL
METAMODEL
TITANIUM ALLOYS - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by/2.5/ar/
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/223398
Ver los metadatos del registro completo
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CONICET Digital (CONICET) |
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Metamodelling the hot deformation behaviour of titanium alloys using a mean-field approachMiller Branco Ferraz, FranzSztangret, LukaszCarazo, Fernando DiegoBuzolin, Ricardo HenriqueWang, PengSzeliga, Danutados Santos Effertz, PedroMacio, PiotrKrumphals, AlfredPoletti, Maria CeciliaARTIFICIAL NEURAL NETWORKDECISION-TREE REGRESSIONHOT DEFORMATIONMEAN-FIELD MODELMETAMODELTITANIUM ALLOYShttps://purl.org/becyt/ford/2.5https://purl.org/becyt/ford/2During the thermomechanical processing of titanium alloys in the β-domain, the β-phase undergoes restoration phenomena. This work describes them by a mean-field physical model that correlates the flow stress with the microstructural evolution. To reduce the computational time of process simulations, metamodels are developed for specific outputs of the mean-field physical model using Artificial Neural Network (ANN) and Decision Tree Regression (DTR). The performance of the obtained metamodels is evaluated in terms of the coefficient of determination (R²), the root-mean-square error (RMSE), and the mean relative error (MRE). No significant difference was observed between R2training and R2testing, meaning that all the metamodels correctly generalise the overall behaviour of the outputs for a wide range of inputs. The evolution of the metamodel outputs is compared with the model predictions in two different situations: 1) at a constant strain rate and temperature, and 2) during Finite Element (FE) simulations of the hot deformation of a hat-shaped sample, where temperature and effective strain rate vary at each element during deformation. The evolution of the outputs at constant and non-constant strain rates and temperature demonstrated the robustness of the metamodels in predicting the heterogeneous deformation within a workpiece. The computational time required by the metamodels to calculate selected outputs can be more than 100 times less than that of the model itself at a constant strain rate using MATLAB® and up to 19% less when coupled with FE simulations. The simulation results combined with microstructural analysis are used to visualise the different restoration mechanisms occurring in different regions of the hat-shaped sample as a function of the local thermomechanical history. The changes in strain rate and temperature during deformation influence the evolution of the wall dislocation density and the immobilisation rate of mobile dislocations at subgrain boundaries, leading to different kinetics of microstructure evolution.Fil: Miller Branco Ferraz, Franz. Graz University Of Technology.; AustriaFil: Sztangret, Lukasz. AGH University of Science and Technology; PoloniaFil: Carazo, Fernando Diego. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Mecanica Aplicada; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Buzolin, Ricardo Henrique. Graz University Of Technology.; AustriaFil: Wang, Peng. Graz University Of Technology.; AustriaFil: Szeliga, Danuta. AGH University of Science and Technology; PoloniaFil: dos Santos Effertz, Pedro. No especifíca;Fil: Macio, Piotr. AGH University of Science and Technology; PoloniaFil: Krumphals, Alfred. No especifíca;Fil: Poletti, Maria Cecilia. Graz University Of Technology.; AustriaElsevier2023-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/223398Miller Branco Ferraz, Franz; Sztangret, Lukasz; Carazo, Fernando Diego; Buzolin, Ricardo Henrique; Wang, Peng; et al.; Metamodelling the hot deformation behaviour of titanium alloys using a mean-field approach; Elsevier; Materials Today Communications; 35; 6-2023; 1-162352-4928CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S2352492823008395info:eu-repo/semantics/altIdentifier/doi/10.1016/j.mtcomm.2023.106148info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:20:59Zoai:ri.conicet.gov.ar:11336/223398instacron: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:34982025-09-29 10:20:59.665CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
Metamodelling the hot deformation behaviour of titanium alloys using a mean-field approach |
title |
Metamodelling the hot deformation behaviour of titanium alloys using a mean-field approach |
spellingShingle |
Metamodelling the hot deformation behaviour of titanium alloys using a mean-field approach Miller Branco Ferraz, Franz ARTIFICIAL NEURAL NETWORK DECISION-TREE REGRESSION HOT DEFORMATION MEAN-FIELD MODEL METAMODEL TITANIUM ALLOYS |
title_short |
Metamodelling the hot deformation behaviour of titanium alloys using a mean-field approach |
title_full |
Metamodelling the hot deformation behaviour of titanium alloys using a mean-field approach |
title_fullStr |
Metamodelling the hot deformation behaviour of titanium alloys using a mean-field approach |
title_full_unstemmed |
Metamodelling the hot deformation behaviour of titanium alloys using a mean-field approach |
title_sort |
Metamodelling the hot deformation behaviour of titanium alloys using a mean-field approach |
dc.creator.none.fl_str_mv |
Miller Branco Ferraz, Franz Sztangret, Lukasz Carazo, Fernando Diego Buzolin, Ricardo Henrique Wang, Peng Szeliga, Danuta dos Santos Effertz, Pedro Macio, Piotr Krumphals, Alfred Poletti, Maria Cecilia |
author |
Miller Branco Ferraz, Franz |
author_facet |
Miller Branco Ferraz, Franz Sztangret, Lukasz Carazo, Fernando Diego Buzolin, Ricardo Henrique Wang, Peng Szeliga, Danuta dos Santos Effertz, Pedro Macio, Piotr Krumphals, Alfred Poletti, Maria Cecilia |
author_role |
author |
author2 |
Sztangret, Lukasz Carazo, Fernando Diego Buzolin, Ricardo Henrique Wang, Peng Szeliga, Danuta dos Santos Effertz, Pedro Macio, Piotr Krumphals, Alfred Poletti, Maria Cecilia |
author2_role |
author author author author author author author author author |
dc.subject.none.fl_str_mv |
ARTIFICIAL NEURAL NETWORK DECISION-TREE REGRESSION HOT DEFORMATION MEAN-FIELD MODEL METAMODEL TITANIUM ALLOYS |
topic |
ARTIFICIAL NEURAL NETWORK DECISION-TREE REGRESSION HOT DEFORMATION MEAN-FIELD MODEL METAMODEL TITANIUM ALLOYS |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/2.5 https://purl.org/becyt/ford/2 |
dc.description.none.fl_txt_mv |
During the thermomechanical processing of titanium alloys in the β-domain, the β-phase undergoes restoration phenomena. This work describes them by a mean-field physical model that correlates the flow stress with the microstructural evolution. To reduce the computational time of process simulations, metamodels are developed for specific outputs of the mean-field physical model using Artificial Neural Network (ANN) and Decision Tree Regression (DTR). The performance of the obtained metamodels is evaluated in terms of the coefficient of determination (R²), the root-mean-square error (RMSE), and the mean relative error (MRE). No significant difference was observed between R2training and R2testing, meaning that all the metamodels correctly generalise the overall behaviour of the outputs for a wide range of inputs. The evolution of the metamodel outputs is compared with the model predictions in two different situations: 1) at a constant strain rate and temperature, and 2) during Finite Element (FE) simulations of the hot deformation of a hat-shaped sample, where temperature and effective strain rate vary at each element during deformation. The evolution of the outputs at constant and non-constant strain rates and temperature demonstrated the robustness of the metamodels in predicting the heterogeneous deformation within a workpiece. The computational time required by the metamodels to calculate selected outputs can be more than 100 times less than that of the model itself at a constant strain rate using MATLAB® and up to 19% less when coupled with FE simulations. The simulation results combined with microstructural analysis are used to visualise the different restoration mechanisms occurring in different regions of the hat-shaped sample as a function of the local thermomechanical history. The changes in strain rate and temperature during deformation influence the evolution of the wall dislocation density and the immobilisation rate of mobile dislocations at subgrain boundaries, leading to different kinetics of microstructure evolution. Fil: Miller Branco Ferraz, Franz. Graz University Of Technology.; Austria Fil: Sztangret, Lukasz. AGH University of Science and Technology; Polonia Fil: Carazo, Fernando Diego. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Mecanica Aplicada; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Buzolin, Ricardo Henrique. Graz University Of Technology.; Austria Fil: Wang, Peng. Graz University Of Technology.; Austria Fil: Szeliga, Danuta. AGH University of Science and Technology; Polonia Fil: dos Santos Effertz, Pedro. No especifíca; Fil: Macio, Piotr. AGH University of Science and Technology; Polonia Fil: Krumphals, Alfred. No especifíca; Fil: Poletti, Maria Cecilia. Graz University Of Technology.; Austria |
description |
During the thermomechanical processing of titanium alloys in the β-domain, the β-phase undergoes restoration phenomena. This work describes them by a mean-field physical model that correlates the flow stress with the microstructural evolution. To reduce the computational time of process simulations, metamodels are developed for specific outputs of the mean-field physical model using Artificial Neural Network (ANN) and Decision Tree Regression (DTR). The performance of the obtained metamodels is evaluated in terms of the coefficient of determination (R²), the root-mean-square error (RMSE), and the mean relative error (MRE). No significant difference was observed between R2training and R2testing, meaning that all the metamodels correctly generalise the overall behaviour of the outputs for a wide range of inputs. The evolution of the metamodel outputs is compared with the model predictions in two different situations: 1) at a constant strain rate and temperature, and 2) during Finite Element (FE) simulations of the hot deformation of a hat-shaped sample, where temperature and effective strain rate vary at each element during deformation. The evolution of the outputs at constant and non-constant strain rates and temperature demonstrated the robustness of the metamodels in predicting the heterogeneous deformation within a workpiece. The computational time required by the metamodels to calculate selected outputs can be more than 100 times less than that of the model itself at a constant strain rate using MATLAB® and up to 19% less when coupled with FE simulations. The simulation results combined with microstructural analysis are used to visualise the different restoration mechanisms occurring in different regions of the hat-shaped sample as a function of the local thermomechanical history. The changes in strain rate and temperature during deformation influence the evolution of the wall dislocation density and the immobilisation rate of mobile dislocations at subgrain boundaries, leading to different kinetics of microstructure evolution. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-06 |
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/11336/223398 Miller Branco Ferraz, Franz; Sztangret, Lukasz; Carazo, Fernando Diego; Buzolin, Ricardo Henrique; Wang, Peng; et al.; Metamodelling the hot deformation behaviour of titanium alloys using a mean-field approach; Elsevier; Materials Today Communications; 35; 6-2023; 1-16 2352-4928 CONICET Digital CONICET |
url |
http://hdl.handle.net/11336/223398 |
identifier_str_mv |
Miller Branco Ferraz, Franz; Sztangret, Lukasz; Carazo, Fernando Diego; Buzolin, Ricardo Henrique; Wang, Peng; et al.; Metamodelling the hot deformation behaviour of titanium alloys using a mean-field approach; Elsevier; Materials Today Communications; 35; 6-2023; 1-16 2352-4928 CONICET Digital CONICET |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S2352492823008395 info:eu-repo/semantics/altIdentifier/doi/10.1016/j.mtcomm.2023.106148 |
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 |
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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1844614195377602560 |
score |
13.070432 |