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.

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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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