Prediction of corrosion–fatigue behavior of DP steel through artificial neural network

Abstract Corrosion–fatigue crack growth (da/dN) of dual phase (DP) steel was analyzed using an artificial neural network (ANN) based model. The training data consisted of corrosion–fatigue crack growth rates at varying stress intensity ranges (ΔK) for martensite contents between 32 and 76%. The ANN model exhibited excellent comparison with the experimental results. Since a large number of variables are used during training the model, it will provide a reliable and useful predictor for corrosion–fatigue crack growth (FCG) in DP steels.