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http://hdl.handle.net/2080/1110
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DC Field | Value | Language |
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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 |
Files in This Item:
File | Description | Size | Format | |
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pkray-2.pdf | 454.81 kB | Adobe PDF | View/Open |
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