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An artificial neural network (ANN)‐based model was developed to analyse high‐cycle fatigue crack growth rates (d a/d N ) as a function of stress intensity ranges (Δ K ) for dual phase (DP) steel. The training data consisted of d a/d N at Δ K 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.

2008 7th World Congress on Intelligent Control and Automation Chongqing, China 2008 7th World Congress on Intelligent Control and Automation IEEE, (2008). 978-1-4244-2113-8 Binbin Dan, Kuisheng Chen, Ling Xiong, Zhijun Rong and Jiangang Yi Research on multi-BP NN-based control model for molten iron desulfurization, (2008).

6133 61, 10.1109/WCICA.20 • T. Srpčič, Fire analysis of steel frames with the use of artificial neural networks, Journal of Constructional Steel Research, 63, 10, (1396), (2007).

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