Applying Artificial Neural Network Model on Investigating the Fiber Diameter of Polybutylene Terephthalate (PBT) Spunbonding Nonwovens: Comparison with Mathematical Statistical Method

Abstract:

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In this paper, two models are founded and introduced to predict the fiber diameter of polybutylene terephthalate spunbonding nonwovens from the spunbonding process parameters. The results indicate the artificial neural network model has good approximation capability and fast convergence rate, and it can provide quantitative predictions of fiber diameter and yield more accurate and stable predictions than the mathematical statistical method. This area of research has great potential in the field of computer assisted design in spunbonding technology.

Info:

Periodical:

Key Engineering Materials (Volumes 426-427)

Edited by:

Dunwen Zuo, Hun Guo, Guoxing Tang, Weidong Jin, Chunjie Liu and Chun Su

Pages:

692-696

DOI:

10.4028/www.scientific.net/KEM.426-427.692

Citation:

B. Zhao "Applying Artificial Neural Network Model on Investigating the Fiber Diameter of Polybutylene Terephthalate (PBT) Spunbonding Nonwovens: Comparison with Mathematical Statistical Method", Key Engineering Materials, Vols. 426-427, pp. 692-696, 2010

Online since:

January 2010

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$35.00

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