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dc.contributor.authorMohanty, J R-
dc.contributor.authorVerma, B B-
dc.contributor.authorParhi, D R K-
dc.contributor.authorRay, P K-
dc.identifier.citationArchives of Computational Materials Science and Surface Engineering, Vol 1, No 3, Pgs 133-138, 2009en
dc.description.abstractPurpose: In this work, fatigue crack propagation life of 7020 T7 and 2024 T3 aluminum alloys under the influence of load ratio was predicted by using artificial neural network (ANN). Design/methodology/approach: Numerous phenomenological models have been proposed for predicting fatigue life of the components under the influence of load ratio to take into account the mean load effect. Findings: In current research, an automatic prediction methodology has been adopted to estimate the constant amplitude loading fatigue life under the above condition by applying artificial neural network (ANN). Practical implications: ANNs show great potential for predicting fatigue crack growth rate especially by interpolation within the tested range. However, its benefit is lost when the model is needed to extrapolate the available experimental data. Originality/value: The predicted results are found to be in good agreement with the experimental findings when tested on two aluminum alloys 7020 T7 and 2024 T3 respectivelyen
dc.format.extent627943 bytes-
dc.publisherAssociation of Computational Materials Science and Surface Engineeringen
dc.subjectFatigue crack growth rateen
dc.subjectArtificial Neural Networken
dc.subjectConstant amplitude loadingen
dc.titleApplication of Artificial Neural Network for Predicting Fatigue Crack Propagation Life of Aluminum Alloysen
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