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http://hdl.handle.net/2080/2156
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DC Field | Value | Language |
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dc.contributor.author | Behera, R K | - |
dc.contributor.author | Swain, S K | - |
dc.contributor.author | Sen, S | - |
dc.contributor.author | Mishra, S C | - |
dc.date.accessioned | 2014-07-16T04:27:31Z | - |
dc.date.available | 2014-07-16T04:27:31Z | - |
dc.date.issued | 2013-08 | - |
dc.identifier.citation | Orissa Journal of Physics, Vol. 20, No.2 , August 2013, pp.217-224 | en |
dc.identifier.issn | 0974-8202 | - |
dc.identifier.uri | http://hdl.handle.net/2080/2156 | - |
dc.description | Copyright for this article belongs Orissa Physical Society | en |
dc.description.abstract | Mechanical properties of ductile cast iron (DI) depend on its microstructure,which is influenced by addition of alloying elements. Artificial Neural Network (ANN)technique with multilayer back propagation algorithm is used as a predictive tool for predicting UTS & 0.2%YS of ductile iron with respect to variation in wt% of alloying elements. Effect of Carbon Equivalent (%CE) and Mg wt% on UTS and 0.2%YS on 3MM & 12MM sections are studied. Comparison between predicted and experimental value shows good correlation with acceptable percentage of error. | en |
dc.format.extent | 284141 bytes | - |
dc.format.mimetype | application/pdf | - |
dc.language.iso | en | - |
dc.publisher | Orissa Journal of Physics | en |
dc.subject | Property Prediction | en |
dc.subject | Ductile Iron | en |
dc.title | Property Prediction of Ductile Iron (DI): Artificial Neural Network Approach | en |
dc.type | Article | en |
Appears in Collections: | Journal Articles |
Files in This Item:
File | Description | Size | Format | |
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R_K_Behera.pdf | 277.48 kB | Adobe PDF | View/Open |
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