Optimization of stabilized annealing of Al-Mg alloys utilizing machine learning algorithms - Details

Author：

Indexed by：

EI Scopus SCIE

Abstract：

The　corrosion　properties　of　the　alloy　are　influenced　by　the　physical　parameters　involved　in　the　preparation　process.　Experiments　to　explore　the　preparation　process　of　Al-Mg　alloys　are　very　complex　and　time-consuming,　and　the　amount　of　data　is　very　limited.　In　this　work,　the　analysis　of　the　corrosion　mechanism　of　Al-Mg　alloy　identified　the　alloy　magnesium　content,　deformation,　annealing　temperature　and　time　as　important　factors　affecting　the　corrosion　resistance　of　the　alloy.　Based　on　the　existing　experimental　data,　a　machine　learning　framework　that　effectively　promotes　smart　manufacturing　is　proposed.　The　results　show　that　the　machine　learning　framework　constructed　based　on　the　existing　experimental　results　can　reliably　predict　the　NAMLT　values　of　the　alloy.　As　more　data　is　acquired,　the　method　is　expected　to　be　used　to　adjust　production　processes　for　efficient　and　intelligent　machining.　©　2023　Elsevier　Ltd

Keyword：

Microstructural Corrosion Machine learning Al–Mg alloy Annealing

Author Community：

[ 1 ] [Xue D.]Key Laboratory of Advanced Functional Materials, Education Ministry of China, Beijing University of Technology, Beijing, 100124, China
[ 2 ] [Wei W.]Key Laboratory of Advanced Functional Materials, Education Ministry of China, Beijing University of Technology, Beijing, 100124, China
[ 3 ] [Shi W.]Institute of Corrosion Science and Technology, Guangzhou, 510530, China
[ 4 ] [Zhou X.R.]School of Materials, The University of Manchester, Manchester, M13 9PL, United Kingdom
[ 5 ] [Qi J.T.]College of New Energy, China University of Petroleum (East China), Qingdao, 266580, China
[ 6 ] [Wen S.P.]Key Laboratory of Advanced Functional Materials, Education Ministry of China, Beijing University of Technology, Beijing, 100124, China
[ 7 ] [Wu X.L.]Key Laboratory of Advanced Functional Materials, Education Ministry of China, Beijing University of Technology, Beijing, 100124, China
[ 8 ] [Gao K.Y.]Key Laboratory of Advanced Functional Materials, Education Ministry of China, Beijing University of Technology, Beijing, 100124, China
[ 9 ] [Xiong X.Y.]Key Laboratory of Advanced Functional Materials, Education Ministry of China, Beijing University of Technology, Beijing, 100124, China
[ 10 ] [Huang H.]Key Laboratory of Advanced Functional Materials, Education Ministry of China, Beijing University of Technology, Beijing, 100124, China
[ 11 ] [Nie Z.R.]Key Laboratory of Advanced Functional Materials, Education Ministry of China, Beijing University of Technology, Beijing, 100124, China

Reprint Author's Address：

Email：

Show more details

Related Keywords：

Machine learning-driven new material discovery
2020，Nanoscale Advances
Research on the earthquake damage level prediction of mega-sub isolation system based on machine learning; [基于机器学习的巨-子结构隔震体系地震损伤等级预测研究]
2023，Journal of Vibration and Shock
Annealing behavior of a modified 5083 aluminum alloy
2010，MATERIALS & DESIGN
Effect of cold deformation and annealing on intergranular corrosion property of 5E06 aluminium alloy plates
2013，Chinese Journal of Nonferrous Metals

Source ：

Materials Today Communications

ISSN： 2352-4928

Year： 2023

Volume： 35

3 . 8 0 0

JCR@2022

ESI Discipline： MATERIALS SCIENCE;

ESI HC Threshold：26

Cited Count：

WoS CC Cited Count：

SCOPUS Cited Count： 4

ESI Highly Cited Papers on the List： 0 Unfold All

WanFang Cited Count：

Chinese Cited Count：

30 Days PV： 8

Affiliated Colleges：

Get Fulltext

DOI Library Discovery Baidu Scholar Search SCOPUS

Type
Departments

All Years Choose Year From to