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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/1108

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contributor.authorMohanty, J R-
contributor.authorParhi, D R K-
contributor.authorRay, P K-
contributor.authorVerma, B B-
identifier.citationFatigue & Fracture of Engineering Materials & Structures, Vol 32, Iss 12, P 1020-1031en
descriptionThis is author version post-print.en
description.abstractMixed-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.en
format.extent353544 bytes-
subjectexponential modeen
subjectmulti-layer perceptronen
subjectnormalised mean square erroren
subjectretardation parametersen
titlePrediction of residual fatigue life under interspersed mixed-mode (I and II) overloads by Artificial Neural Networken
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