Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/2156
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dc.contributor.authorBehera, R K-
dc.contributor.authorSwain, S K-
dc.contributor.authorSen, S-
dc.contributor.authorMishra, S C-
dc.date.accessioned2014-07-16T04:27:31Z-
dc.date.available2014-07-16T04:27:31Z-
dc.date.issued2013-08-
dc.identifier.citationOrissa Journal of Physics, Vol. 20, No.2 , August 2013, pp.217-224en
dc.identifier.issn0974-8202-
dc.identifier.urihttp://hdl.handle.net/2080/2156-
dc.descriptionCopyright for this article belongs Orissa Physical Societyen
dc.description.abstractMechanical 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.extent284141 bytes-
dc.format.mimetypeapplication/pdf-
dc.language.isoen-
dc.publisherOrissa Journal of Physicsen
dc.subjectProperty Predictionen
dc.subjectDuctile Ironen
dc.titleProperty Prediction of Ductile Iron (DI): Artificial Neural Network Approachen
dc.typeArticleen
Appears in Collections:Journal Articles

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