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Prediction of residual fatigue life under interspersed mixed‐mode (I and II) overloads by Artificial Neural Network
Author(s) -
MOHANTY J. R.,
PARHI D. R. K.,
RAY P. K.,
VERMA B. B.
Publication year - 2009
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 - 10.1111/j.1460-2695.2009.01407.x
Subject(s) - paris' law , artificial neural network , residual , materials science , structural engineering , mode (computer interface) , mixed mode , residual stress , residual strength , aluminium , engineering , composite material , computer science , crack closure , fracture mechanics , algorithm , artificial intelligence , operating system
Mixed‐mode (I and II) overloads are often encountered in an engineering structure due to either alteration of the loading direction or the presence of randomly oriented defects. Prediction of fatigue life in these cases is more complex than that of mode‐I overloads. The objective of this study is to explore the use of an artificial neural network (ANN) model for the prediction of fatigue crack growth rate under interspersed mixed‐mode (I and II) overload. The crack growth rates as predicted by the ANN method on two aluminium alloys, 7020 T7 and 2024 T3 have been compared with the experimental data and an Exponential Model. It is observed that the predicted results are in good agreement and facilitate determination of residual fatigue life.