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

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外文摘要：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 de-formation 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 μm 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 10-2 s-1,were reached at 400 ℃ in the material with coarsest grain size of 2.8 μm,and at 300 ℃ for the finest grain size of 2 μm.Neverthe-less,the superplastic response decreased at 350 ℃ and 400 ℃ 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.

外文关键词：

Machine learningArtificial intelligenceMagnesium alloysSuperplasticityFriction stir processingGrain coarsening

作者：

Farid Bahari-Sambran、Fernando Carre?o、C.M.Cepeda-Jiménez、Alberto Orozco-Caballero

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作者单位：

Physical Metallurgy Department,CENIM-CSIC,Av.Gregorio del Amo 8,28040 Madrid,Spain

Department of Mechanical Engineering,Chemistry and Industrial Design,Polytechnic University of Madrid,Ronda de Valencia 3,28012,Madrid,Spain

基金：

Comunidad de Madrid through the Universidad Politécnica de Madrid in the line of Action for Encouraging Research from Young DoctComunidad de Madrid through the Universidad Politécnica de Madrid in the line of Action for Encouraging Research from Young DoctComunidad de Madrid through the Universidad Politécnica de Madrid in the line of Action for Encouraging Research from Young DoctMINECO(Spain)Project (MINECO/FEDER)MCIN/AEI/10.13039/501100011033Project CAREDELPro-ject RAPIDAL

项目编号：

APOYO-JOVENES-779NQU-57-LSWH0FUPM ref M190020074AOCCAREDELMAT2015-68919-C3-1-RPID2020-118626RB-I00RAPIDALPRE2021-096977

出版年：

2024

DOI：

10.1016/j.jma.2024.05.019

镁合金学报(英文)

ISSN：2213-9567

年,卷(期)：2024.12(5)