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Artificial neural network modeling for predicting melt blowing processing
Author(s) -
Chen Ting,
Wang Jun,
Huang Xiubao
Publication year - 2005
Publication title -
journal of applied polymer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.575
H-Index - 166
eISSN - 1097-4628
pISSN - 0021-8995
DOI - 10.1002/app.22495
Subject(s) - artificial neural network , fiber , mean squared error , correlation coefficient , layer (electronics) , biological system , materials science , field (mathematics) , computer science , artificial intelligence , algorithm , pattern recognition (psychology) , composite material , mathematics , machine learning , statistics , pure mathematics , biology
An artificial neural network model is established for predicting the fiber diameter of melt blown nonwoven fabrics from the processing parameters. An attempt is made to study the effect of the number of hidden layers and hidden layer neurons to minimize the prediction error. The artificial neural network with three hidden layers (5, 2, and 3 neurons in the first, second, and third hidden layer, respectively) yields the minimum prediction error and thus is determined as the preferred network. The square of the correlation coefficient of measured and predicted fiber diameters shows the good performance of the model. Using the established ANN model, computer simulations of the effects of the processing parameters on the fiber diameter are carried out. The results show great promise for this research in the field of computer assisted design of melt blowing technology. © 2005 Wiley Periodicals, Inc. J Appl Polym Sci 99: 424–429, 2006