Title :
Prediction of Fiber Diameter of Melt Blowing Nonwovens Produced by Dual Slot Inset Sharp Die
Author_Institution :
Coll. of Textiles, Zhongyuan Univ. of Technol., Zhengzhou, China
Abstract :
In this paper, two modeling approaches (artificial neural network and regression model) are established and used to predict the fiber diameter of melt blowing nonwovens. By analyzing the results of the models, the effects of process parameters on fiber diameter can be predicted. The results demonstrated that the ANN model yields more accurate and stable predictions than regression model, which is an effective and a viable modeling approach for predictors.
Keywords :
dies (machine tools); fabrics; melt processing; neural nets; production engineering computing; regression analysis; weaving; ANN model; dual slot inset sharp die; fiber diameter prediction; melt blowing nonwovens; process parameters; regression model; Artificial neural networks; Atmospheric modeling; Mathematical model; Neurons; Optical fiber networks; Polymers; Predictive models; artificial neural network; fiber diameter; inset sharp die; melt blowing; nonwoven; prediction; regression model;
Conference_Titel :
Intelligence Information Processing and Trusted Computing (IPTC), 2010 International Symposium on
Conference_Location :
Huanggang
Print_ISBN :
978-1-4244-8148-4
Electronic_ISBN :
978-0-7695-4196-9
DOI :
10.1109/IPTC.2010.131
