ANN based prediction model for fatigue crack growth in DP steel

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.