ISSN:
1460-2695
Source:
Blackwell Publishing Journal Backfiles 1879-2005
Topics:
Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
Notes:
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 (ΔK ) for dual phase (DP) steel. The training data consisted of da/dN at ΔK ranges between 5 and 16 MPa √ 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.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1046/j.1460-2695.2001.00361.x
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