Prediction of mode-I overload-induced fatigue crack growth rates using neuro-fuzzy approach

Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/1110

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DC Field	Value	Language
dc.contributor.author	Mohanty, J R	-
dc.contributor.author	Verma, B B	-
dc.contributor.author	Ray, P K	-
dc.contributor.author	Parhi, D R K	-
dc.date.accessioned	2009-12-20T03:26:48Z	-
dc.date.available	2009-12-20T03:26:48Z	-
dc.date.issued	2010	-
dc.identifier.citation	Expert Systems with Applications, Vol 37, Iss 4, P 3075-3087	en
dc.identifier.uri	http://dx.doi.org/10.1016/j.eswa.2009.09.022	-
dc.identifier.uri	http://hdl.handle.net/2080/1110	-
dc.description	Copyright for the published version belongs to Elsevier	en
dc.description.abstract	A 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
dc.format.extent	465724 bytes	-
dc.format.mimetype	application/pdf	-
dc.language.iso	en	-
dc.publisher	Elsevier	en
dc.subject	Adaptive neuro-fuzzy inference system	en
dc.subject	Adaptive network	en
dc.subject	Delay cycle	en
dc.subject	Exponential model	en
dc.subject	Fatigue crack growth rate	en
dc.subject	Fatigue life	en
dc.subject	Retardation parameters	en
dc.title	Prediction of mode-I overload-induced fatigue crack growth rates using neuro-fuzzy approach	en
dc.type	Article	en
Appears in Collections:	Journal Articles

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