Prediction of Fiber Diameter of Melt Blowing Nonwovens Produced by Dual Slot Inset Sharp Die

Author

Bo, Zhao

Author_Institution

Coll. of Textiles, Zhongyuan Univ. of Technol., Zhengzhou, China

fYear

2010

fDate

28-29 Oct. 2010

Firstpage

169

Lastpage

172

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/IPTC.2010.131

Filename

5663044

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3513075