Please use this identifier to cite or link to this item: http://nopr.niscpr.res.in/handle/123456789/24858
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dc.contributor.authorDebnath, S-
dc.contributor.authorMadhusoothanan, M-
dc.contributor.authorSrinivasmoorthy, V R-
dc.date.accessioned2013-12-17T06:02:03Z-
dc.date.available2013-12-17T06:02:03Z-
dc.date.issued2000-03-
dc.identifier.issn0975-1025 (Online); 0971-0426 (Print)-
dc.identifier.urihttp://hdl.handle.net/123456789/24858-
dc.description31-36en_US
dc.description.abstractThe modelling of tensile properties of needle-punched nonwoven fabrics produced from the blends of jute and polypropylene fibres with varying fabric weight, needling density and blend ratio has been done. The tenacity and initial modulus values of needle-punched nonwoven fabrics have been predicted with the help of empirical model (using multiple regression analysis) and artificial neural networks and compared with the experimental values. The artificial neural network model is found to be much better and more accurate than an empirical model. An attempt has also been made for the experimental verification of the predicted values for extrapolated input variables. The prediction by artificial neural network model shows better results than that by empirical model even for the extrapolated input variables.en_US
dc.language.isoen_USen_US
dc.publisherNISCAIR-CSIR, Indiaen_US
dc.rights CC Attribution-Noncommercial-No Derivative Works 2.5 Indiaen_US
dc.sourceIJFTR Vol.25(1) [March 2000]en_US
dc.subjectArtificial neural networken_US
dc.subjectInitial modulusen_US
dc.subjectJute/polypropyleneen_US
dc.subjectModellingen_US
dc.subjectNeedle-punched fabricen_US
dc.subjectTenacityen_US
dc.titleModelling of tensile properties of needle-punched nonwovens using artificial neural networksen_US
dc.typeArticleen_US
Appears in Collections:IJFTR Vol.25(1) [March 2000]

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