Prediction of corrosion-fatigue behavior of DP steel through artificial neural network Academic Article

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 (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.

authors

• Haque, Mohammed

published proceedings

• INTERNATIONAL JOURNAL OF FATIGUE

author list (cited authors)

• Haque, M. E., & Sudhakar, K. V.

citation count

• 61

complete list of authors

• Haque, ME||Sudhakar, KV

publication date

• January 2001

publisher

• Elsevier  Publisher

published in

• International Journal of Fatigue  Journal

keywords

• Artificial Neural Network (ann)
• Corrosion-fatigue
• Dual Phase (dp) Steel
• Martensite
• Stress Intensity Range

Digital Object Identifier (DOI)

• 10.1016/S0142-1123(00)00074-8

start page

• 1

end page

• 4

volume

• 23

issue

• 1

URL

• http://dx.doi.org/10.1016/s0142-1123(00)00074-8