Predicting the fiber diameter of spunbonding nonwovens fabrics by means of physical model, statistical method and artifical neural network theory
International Journal of Clothing Science and Technology
ISSN: 0955-6222
Article publication date: 20 April 2015
Abstract
Purpose
The purpose of this paper is to establish three modeling methods (physical model, statistical model, and artificial neural network (ANN) model) and use it to predict the fiber diameter of spunbonding nonwovens from the process parameters.
Design/methodology/approach
The results show the physical model is based on the inherent physical principles, it can yield reasonably good prediction results and provide insight into the relationship between process parameters and fiber diameter.
Findings
By analyzing the results of the physical model, the effects of process parameters on fiber diameter can be predicted. The ANN model has good approximation capability and fast convergence rate, it can provide quantitative predictions of fiber diameter and yield more accurate and stable predictions than the statistical model.
Originality/value
The effects of process parameters on fiber diameter are also determined by the ANN model. Excellent agreement is obtained between these two modeling methods.
Keywords
Citation
Zhao, B. (2015), "Predicting the fiber diameter of spunbonding nonwovens fabrics by means of physical model, statistical method and artifical neural network theory", International Journal of Clothing Science and Technology, Vol. 27 No. 2, pp. 262-271. https://doi.org/10.1108/IJCST-01-2014-0015
Publisher
:Emerald Group Publishing Limited
Copyright © 2015, Emerald Group Publishing Limited