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 arti...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 3μm)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 2.8μm,and at 300℃ for the finest grain size of 2μm.Nevertheless,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.展开更多
基金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-JOVENES779NQU-57-LSWH0F,UPM ref M190020074AOC,CAREDEL)MINECO(Spain)Project MAT2015-68919-C3-1-R(MINECO/FEDER)+4 种基金project PID2020-118626RB-I00(RAPIDAL)awarded by MCIN/AEI/10.13039/501100011033FSP assistanceProject CAREDELProject RAPIDAL for research contractsMCIN/AEI for a FPI contract number PRE2021-096977。
文摘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 3μm)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 2.8μm,and at 300℃ for the finest grain size of 2μm.Nevertheless,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.
