Property Prediction of Ductile Iron (DI): Artificial Neural Network Approach

Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/2156

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DC Field	Value	Language
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

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