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contributor.authorMohanty, J R-
contributor.authorVerma, B B-
contributor.authorRay, P K-
contributor.authorParhi, D R K-
date.accessioned2009-12-20T03:26:48Z-
date.available2009-12-20T03:26:48Z-
date.issued2010-
identifier.citationExpert Systems with Applications, Vol 37, Iss 4, P 3075-3087en
identifier.urihttp://dx.doi.org/10.1016/j.eswa.2009.09.022-
identifier.urihttp://hdl.handle.net/2080/1110-
descriptionCopyright for the published version belongs to Elsevieren
description.abstractA methodology has been developed to predict fatigue crack propagation life of 7020 T7 and 2024 T3 aluminum alloys under constant amplitude loading interspersed with mode-I spike overload. It has been assessed by adopting adaptive neuro-fuzzy inference system (ANFIS), a novel soft-computing approach, suitable for non-linear, noisy and complex problems like fatigue. The proposed model has proved its efficiency quite satisfactorily compared to authors’ previously proposed ‘Exponential Model’, when tested on both the alloys.en
format.extent465724 bytes-
format.mimetypeapplication/pdf-
language.isoen-
publisherElsevieren
subjectAdaptive neuro-fuzzy inference systemen
subjectAdaptive networken
subjectDelay cycleen
subjectExponential modelen
subjectFatigue crack growth rateen
subjectFatigue lifeen
subjectRetardation parametersen
titlePrediction of mode-I overload-induced fatigue crack growth rates using neuro-fuzzy approachen
typeArticleen
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