Artificial Neural Networks - Industrial and Control Engineering Applications 2011
DOI: 10.5772/16123
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Modelling of Needle-Punched Nonwoven Fabric Properties Using Artificial Neural Network

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Cited by 14 publications
(21 citation statements)
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“…Each factor was run at three levels so that the experiment was a 3 3 factorial design with two replicates as per the Box Behnken design of experiment. 14 The samples were prepared after randomisation according to a random number table and the sequence of sample production comprised the process variables given at serial numbers 2,4,9,11,6,5,15,14,1,12,7,8,3,10,13. The reasons for randomisation were an effective statistical analysis and the validity of the inference drawn.…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…Each factor was run at three levels so that the experiment was a 3 3 factorial design with two replicates as per the Box Behnken design of experiment. 14 The samples were prepared after randomisation according to a random number table and the sequence of sample production comprised the process variables given at serial numbers 2,4,9,11,6,5,15,14,1,12,7,8,3,10,13. The reasons for randomisation were an effective statistical analysis and the validity of the inference drawn.…”
Section: Methodsmentioning
confidence: 99%
“…Compression properties of needlepunched nonwoven fabrics have been studied extensively [7][8][9][10][11] and have also been predicted by artificial neural network models. 12,13 Research also reveals the compression properties and parameters influencing such properties under wet conditions. 8,9 Reported research work on compression creep of jute-blended needle-punched nonwoven is scanty.…”
Section: Introductionmentioning
confidence: 99%
“…(Debnath et al, 2000a). The absolute error percentage in tenacity values for a particular sample in the machine direction and transverse direction for the ANN model are 13.02% and 10.40%, respectively.…”
Section: Prediction Of Tensile Propertiesmentioning
confidence: 99%
“…The tensile properties (tenacity and initial modulus) of needle-punched nonwovens produced from a blend of polypropylene and jute fibres have been predicted with the help of ANN and empirical models (Debnath et al, 2000a). (Debnath et al, 2000a).…”
Section: Prediction Of Tensile Propertiesmentioning
confidence: 99%
“…A visual inspection system has been based on wavelet texture analysis and robust bayesian ANNs (Liu et al, 2010), or similarly wavelet transforms and ANNs (Huang & Lin, 2008), while a neuro-fractal approach has been used for the recognition and classification of nonwoven web images (Payvand et al, 2010). Many quality issues are addressed via ANN methods, like the structure-properties relations of the nonwoven fabrics (Chen et al, 2007), the construction of a quality prediction system , the modeling of the compression properties of needle-punched nonwoven fabrics (Debnath & Madhusoothanan, 2008), the simulation of the drawing of spunbonding nowoven process (Chen et al, 2008) and also the objective evaluation of the pilling on nonwoven fabrics .…”
Section: Nonwovensmentioning
confidence: 99%