A combined neural network and mechanistic approach for the prediction of corrosion rate and yield strength of magnesium-rare earth alloys

被引：95

作者：

Birbilis, N. ^{[1
,2
]}

Cavanaugh, M. K. ^{[3
]}

Sudholz, A. D. ^{[1
]}

Zhu, S. M. ^{[2
]}

Easton, M. A. ^{[2
]}

Gibson, M. A. ^{[4
]}

机构：

[1] Monash Univ, ARC Ctr Excellence Design Light Met, Clayton, Vic 3800, Australia

[2] Monash Univ, CAST Cooperat Res Ctr, Clayton, Vic 3800, Australia

[3] Ohio State Univ, Dept Mat Sci & Engn, Columbus, OH 43210 USA

[4] CSIRO, Div Proc Sci & Engn, Clayton, Vic, Australia

来源：

CORROSION SCIENCE | 2011年 / 53卷 / 01期

基金：

澳大利亚研究理事会;

关键词：

Magnesium alloys; Rare earth; Corrosion; Yield strength; Neural network modelling; CREEP-BEHAVIOR; AE42;

D O I：

10.1016/j.corsci.2010.09.013

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Additions of Ce La and Nd to Mg were made in binary ternary and quaternary combinations up to similar to 6 wt % This provided a dataset that was used in developing a neural network model for predicting corrosion rate and yield strength Whilst yield strength increased with RE additions corrosion rates also systematically increased however this depended on the type of RE element added and the combination of elements added (along with differences in intermetallic morphology) This work is permits an understanding of Mg-RE alloy performance and can be exploited in Mg alloy design for predictable combinations of strength and corrosion resistance (C) 2010 Elsevier Ltd All rights reserved

引用

页码：168 / 176

页数：9

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