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

Title: Prediction of mode-I overload-induced fatigue crack growth rates using neuro-fuzzy approach
Authors: Mohanty, J R
Verma, B B
Ray, P K
Parhi, D R K
Keywords: Adaptive neuro-fuzzy inference system
Adaptive network
Delay cycle
Exponential model
Fatigue crack growth rate
Fatigue life
Retardation parameters
Issue Date: 2010
Publisher: Elsevier
Citation: Expert Systems with Applications, Vol 37, Iss 4, P 3075-3087
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.
Description: Copyright for the published version belongs to Elsevier
URI: http://dx.doi.org/10.1016/j.eswa.2009.09.022
http://hdl.handle.net/2080/1110
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