The Prediction of Fiber Diameter of Spunbonding Nonwovens by Using Neural Network and Empirical Statistical Methods

Bo Zhao

doi:10.4028/www.scientific.net/AMM.33.309

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 33The Prediction of Fiber Diameter of Spunbonding...

The Prediction of Fiber Diameter of Spunbonding Nonwovens by Using Neural Network and Empirical Statistical Methods

Abstract:

In this paper, the empirical statistical and artificial neural network methods are established. We present a comparative study of two modeling methodological for predicting the fiber diameter of spunbonding nonwovens from the process parameters. The radial basis neural network, which has good approximation capability and fast convergence rate, is employed in this work, and it can provide quantitative predictions of fiber diameter. The effects of process parameters on fiber diameter are also determined by the ANN model. The results show the artificial neural network model yield more accurate and stable predictions than the statistical method, which reveals that artificial neural network technique is really an effective and viable modeling method.

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Periodical:

Applied Mechanics and Materials (Volume 33)

Pages:

309-312

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.33.309

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Online since:

October 2010

Authors:

Bo Zhao

Keywords:

Artificial Neural Network Model, Fiber Diameter, Process Parameter, Spunbonding Nonwoven, Statistical Model

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References

[1] N. Hajji and J. E. Spruiell: INDA J. Nonwovens Res. Vol. 4 (1992) No. 2, pp.16-21.

Google Scholar

[2] S. Misra: INDA J. Nonwovens Res. Vol. 5 (1993) No. 3, pp.13-19.

Google Scholar

[3] S. Debnath: Indian J. Fiber Texti. Res. Vol. 1(2000) No. 25, pp.251-255.

Google Scholar

[4] S. Ertugrul: Textile Res. J. Vol. 70 (2000) No. 7, pp.845-851.

Google Scholar

[5] A. Guba: J. Text. Inst. Vol 92 (2001) No17, pp.139-145.

Google Scholar

[6] M.C. Ramesh: J. Text. Inst. Vol. 86(1995) No3, pp.459-469.

Google Scholar

[7] Y .C. Zeng: Textile Res. J. Vol. 74 (2004) No. 5, pp.689-694.

Google Scholar

[8] P. K. Majumadr: Textile Res. J. Vol. 74 (2004) No. 5, pp.652-655.

Google Scholar

[9] T. Chen: Proceedings of the Textile Institute 84TH World Conference, 2005, pp.1006-1013.

Google Scholar

[10] D. E. Rumelhat: Eds., Parallel Distributed Processing-Explorations in the Microstructure of Cognition, Vol. 1, Foundations, MIT Press, Cambridge, MA, 1986, pp.563-670.

Google Scholar

[11] P. D. Wasserman: Advanced Methods in Neural Computing (Van Nostrand Reinhold, NY 1993).

Google Scholar