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

• Published in: International journal of fatigue Vol. 23; no. 1; pp. 1 - 4
• Main Authors: Haque, Mohammed E, Sudhakar, K.V
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 2001; Elsevier Science
• Subjects: Applied sciences; Artificial neural network (ANN); Condensed matter: structure, mechanical and thermal properties; Corrosion–fatigue; Dual phase (DP) steel; Exact sciences and technology; Fatigue, brittleness, fracture, and cracks; Martensite; Mechanical and acoustical properties of condensed matter; Mechanical properties of solids; Metals. Metallurgy; Physics; Stress intensity range; Stress intensity range; Artificial neural network (ANN); Corrosion–fatigue; Martensite; Dual phase (DP) steel; Fracture mechanics; Propagation velocity; Neural networks; Dual-phase steels; Theoretical study; Mechanical properties; Stress corrosion cracking; Steels; Crack propagation
• ISSN: 0142-1123; 1879-3452
• DOI: 10.1016/S0142-1123(00)00074-8

Corrosion–fatigue crack growth (d
a/d
N) 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 (Δ
K) 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.