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Title: Modeling of compression properties of needle-punched nonwoven fabrics using artificial neural network
Authors: Debnath, Sanjoy
Madhusoothanan, M
Keywords: Artificial neural network
Compression properties
Jute-polypropylene blends
Needle-punched nonwoven
Polyester fibre
Woollenised jute
Issue Date: Dec-2008
Publisher: CSIR
Series/Report no.: Int. Cl. ⁸ D04H, G06N3/00
Abstract: The present study is concerned with the modeling of compression properties of needle-punched nonwoven fabrics produced from polyester and blend of jute-polypropylene fibres with varying fabric weight, needling density and blend ratio of jute and polypropylene fibres. Initial thickness, percentage compression, percentage thickness loss and compression resilience are the compression properties predicted with the help of artificial neural networks. A very good correlation (R2 values) with minimum error between the experimental and the predicted values of compression properties have been obtained by ANN with two and three hidden layers. An attempt has also been made for experimental verification of the predicted values for the input variables not used during the training phase. The prediction of compression properties by artificial neural network model in some particular sample is less accurate due to lack of learning during training phase.
Description: 392-399
ISSN: 0971-0426
Appears in Collections:IJFTR Vol.33(4) [December 2008]

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