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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/1024

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contributor.authorMohanty, J R-
contributor.authorVerma, B B-
contributor.authorParhi, D R K-
contributor.authorRay, P K-
date.accessioned2009-08-28T09:37:51Z-
date.available2009-08-28T09:37:51Z-
date.issued2009-
identifier.citationArchives of Computational Materials Science and Surface Engineering, International Scientific Journal published quarterly by the Association of Computational Materials Science and Surface Engineering, Volume 1, Issue 3, Pages 133-138en
identifier.urihttp://hdl.handle.net/2080/1024-
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 respectively.en
format.extent627943 bytes-
format.mimetypeapplication/pdf-
language.isoen-
publisherInternational OCSCO World Pressen
subjectFatigue crack growth rateen
subjectArtificial Neural Networken
subjectConstant amplitude loadingen
titleApplication of Artificial Neural Network for Predicting Fatigue Crack Propagation Life of Aluminum Alloysen
typeArticleen
Appears in Collections:Journal Articles

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