Predicting grain size-dependent superplastic properties in friction stir processed ZK30 magnesium alloy with machine learning methods

The aim of this work is to predict, for the first time, the high temperature flow stress dependency with the grain size and the underlaid deformation mechanism using two machine learning models, random forest (RF) and artificial neural network (ANN). With that purpose, a ZK30 magnesium alloy was fri...

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Authors: Bahari-Sambran, F., Carreño, Fernando, Cepeda-Jiménez, C.M., Orozco-Caballero, A.
Format: article
Status:Published version
Publication Date:2024
Country:España
Institution:Consejo Superior de Investigaciones Científicas (CSIC)
Repository:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/380717
Online Access:http://hdl.handle.net/10261/380717
Access Level:Open access
Keyword:Machine learning
Artificial intelligence
Magnesium alloys
Superplasticity
Friction stir processing
Grain coarsening
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spelling Predicting grain size-dependent superplastic properties in friction stir processed ZK30 magnesium alloy with machine learning methodsBahari-Sambran, F.Carreño, FernandoCepeda-Jiménez, C.M.Orozco-Caballero, A.Machine learningArtificial intelligenceMagnesium alloysSuperplasticityFriction stir processingGrain coarseningThe aim of this work is to predict, for the first time, the high temperature flow stress dependency with the grain size and the underlaid deformation mechanism using two machine learning models, random forest (RF) and artificial neural network (ANN). With that purpose, a ZK30 magnesium alloy was friction stir processed (FSP) using three different severe conditions to obtain fine grain microstructures (with average grain sizes between 2 and 3 µm) prone to extensive superplastic response. The three friction stir processed samples clearly deformed by grain boundary sliding (GBS) deformation mechanism at high temperatures. The maximum elongations to failure, well over 400% at high strain rate of 10 s, were reached at 400 °C in the material with coarsest grain size of 2.8 µm, and at 300 °C for the finest grain size of 2 µm. Nevertheless, the superplastic response decreased at 350 °C and 400 °C due to thermal instabilities and grain coarsening, which makes it difficult to assess the operative deformation mechanism at such temperatures. This work highlights that the machine learning models considered, especially the ANN model with higher accuracy in predicting flow stress values, allow determining adequately the superplastic creep behavior including other possible grain size scenarios.Financial support was obtained from Comunidad de Madrid through the Universidad Politécnica de Madrid in the line of Action for Encouraging Research from Young Doctors (project CdM ref: APOYO-JOVENES- 779NQU-57-LSWH0F , UPM ref M190020074AOC , CAREDEL), as well as MINECO (Spain) Project MAT2015-68919-C3-1-R (MINECO/FEDER) and project PID2020-118626RB-I00 (RAPIDAL) awarded by MCIN/AEI/10.13039/501100011033 . Pilar Rey (AIMEN) and Marta Álvarez-Leal (CENIM) are gratefully acknowl- edged for FSP assistance. FBS also thanks Project CAREDEL and Project RAPIDAL for research contracts and MCIN/AEI for a FPI contract number PRE2021-096977 .Peer reviewedElsevier BVComunidad de MadridMinisterio de Economía y Empresa (España)Ministerio de Ciencia, Innovación y Universidades (España)Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]2025202520242025info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/380717reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/MINECO//MAT2015-68919-C3-1-Rinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-118626RB-I00info:eu-repo/grantAgreement/MINECO//MCINhttps://doi.org/10.1016/j.jma.2024.05.019Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3807172026-05-22T06:33:51Z
dc.title.none.fl_str_mv Predicting grain size-dependent superplastic properties in friction stir processed ZK30 magnesium alloy with machine learning methods
title Predicting grain size-dependent superplastic properties in friction stir processed ZK30 magnesium alloy with machine learning methods
spellingShingle Predicting grain size-dependent superplastic properties in friction stir processed ZK30 magnesium alloy with machine learning methods
Bahari-Sambran, F.
Machine learning
Artificial intelligence
Magnesium alloys
Superplasticity
Friction stir processing
Grain coarsening
title_short Predicting grain size-dependent superplastic properties in friction stir processed ZK30 magnesium alloy with machine learning methods
title_full Predicting grain size-dependent superplastic properties in friction stir processed ZK30 magnesium alloy with machine learning methods
title_fullStr Predicting grain size-dependent superplastic properties in friction stir processed ZK30 magnesium alloy with machine learning methods
title_full_unstemmed Predicting grain size-dependent superplastic properties in friction stir processed ZK30 magnesium alloy with machine learning methods
title_sort Predicting grain size-dependent superplastic properties in friction stir processed ZK30 magnesium alloy with machine learning methods
dc.creator.none.fl_str_mv Bahari-Sambran, F.
Carreño, Fernando
Cepeda-Jiménez, C.M.
Orozco-Caballero, A.
author Bahari-Sambran, F.
author_facet Bahari-Sambran, F.
Carreño, Fernando
Cepeda-Jiménez, C.M.
Orozco-Caballero, A.
author_role author
author2 Carreño, Fernando
Cepeda-Jiménez, C.M.
Orozco-Caballero, A.
author2_role author
author
author
dc.contributor.none.fl_str_mv Comunidad de Madrid
Ministerio de Economía y Empresa (España)
Ministerio de Ciencia, Innovación y Universidades (España)
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Machine learning
Artificial intelligence
Magnesium alloys
Superplasticity
Friction stir processing
Grain coarsening
topic Machine learning
Artificial intelligence
Magnesium alloys
Superplasticity
Friction stir processing
Grain coarsening
description The aim of this work is to predict, for the first time, the high temperature flow stress dependency with the grain size and the underlaid deformation mechanism using two machine learning models, random forest (RF) and artificial neural network (ANN). With that purpose, a ZK30 magnesium alloy was friction stir processed (FSP) using three different severe conditions to obtain fine grain microstructures (with average grain sizes between 2 and 3 µm) prone to extensive superplastic response. The three friction stir processed samples clearly deformed by grain boundary sliding (GBS) deformation mechanism at high temperatures. The maximum elongations to failure, well over 400% at high strain rate of 10 s, were reached at 400 °C in the material with coarsest grain size of 2.8 µm, and at 300 °C for the finest grain size of 2 µm. Nevertheless, the superplastic response decreased at 350 °C and 400 °C due to thermal instabilities and grain coarsening, which makes it difficult to assess the operative deformation mechanism at such temperatures. This work highlights that the machine learning models considered, especially the ANN model with higher accuracy in predicting flow stress values, allow determining adequately the superplastic creep behavior including other possible grain size scenarios.
publishDate 2024
dc.date.none.fl_str_mv 2024
2025
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/380717
url http://hdl.handle.net/10261/380717
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/MINECO//MAT2015-68919-C3-1-R
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-118626RB-I00
info:eu-repo/grantAgreement/MINECO//MCIN
https://doi.org/10.1016/j.jma.2024.05.019
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Elsevier BV
publisher.none.fl_str_mv Elsevier BV
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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