ANN based prediction model for fatigue crack growth in DP steel

被引：25

作者：

Haque, ME ^{[1
]}

Sudhakar, KV

机构：

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

[2] Cent Michigan Univ, Dept Ind & Engn Technol, Mt Pleasant, MI 48859 USA

来源：

FATIGUE & FRACTURE OF ENGINEERING MATERIALS & STRUCTURES | 2001年 / 24卷 / 01期

关键词：

artificial neural network (ANN); dual phase (DP) steel; fatigue crack growth; martensite content; stress intensity range;

D O I：

10.1046/j.1460-2695.2001.00361.x

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 [机械工程];

摘要：

An artificial neural network (ANN)-based model was developed to analyse high-cycle fatigue crack growth rates (da/dN) as a function of stress intensity ranges (DeltaK) for dual phase (DP) steel. The training data consisted of da/dN at DeltaK ranges between 5 and 16 MPa rootm for DP steel with martensite contents in the range 32 to 76%. The ANN back-propagation model with Gaussian activation function exhibited excellent agreement with the experimental results. The fatigue crack growth rate predictions were made to demonstrate its practical significance in a given real-life situation. Because of the wide range of data points used during training of the model, it will provide a useful predictor for fatigue crack growth in DP steels.

引用

页码：63 / 68

页数：6

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