Prediction of corrosion-fatigue behavior of DP steel through artificial neural network

被引：69

作者：

Haque, ME ^{[1
]}

Sudhakar, KV

机构：

[1] Texas A&M Univ, Dept Construct Sci, College Stn, TX 77843 USA

[2] Western Michigan Univ, Dept Construct Engn Mat Engn & Ind Design, Kalamazoo, MI 49008 USA

来源：

INTERNATIONAL JOURNAL OF FATIGUE | 2001年 / 23卷 / 01期

关键词：

corrosion-fatigue; artificial neural network (ANN); dual phase (DP) steel; stress intensity range; martensite;

D O I：

10.1016/S0142-1123(00)00074-8

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

Corrosion-fatigue crack growth (da/dN) of dual phase (DP) steel was analyzed using an artificial neural network (ANN) based model. The training data consisted of corrosion-fatigue crack growth rates at varying stress intensity ranges (DeltaK) for martensite contents between 32 and 76%. The ANN model exhibited excellent comparison with the experimental results. Since a large number of variables are used during training the model, it will provide a reliable and useful predictor for corrosion-fatigue crack growth (FCG) in DP steels. (C) 2001 Elsevier Science Ltd. All rights reserved.

引用

页码：1 / 4

页数：4

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