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ANN based prediction model for fatigue crack growth in DP steel
Author(s) -
Haque M. E.,
Sudhakar K. V.
Publication year - 2001
Publication title -
fatigue and fracture of engineering materials and structures
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.887
H-Index - 84
eISSN - 1460-2695
pISSN - 8756-758X
DOI - 10.1046/j.1460-2695.2001.00361.x
Subject(s) - paris' law , materials science , martensite , structural engineering , artificial neural network , range (aeronautics) , gaussian function , gaussian , fracture mechanics , metallurgy , composite material , crack closure , engineering , computer science , microstructure , artificial intelligence , physics , quantum mechanics
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.