A soft computing approach to model the structure–property relations of nonwoven fabrics

Abstract

AbstractA soft computing approach to model the structure–property relations of nonwoven fabrics for filtration use is developed. Because the number of samples is very limited, the artificial neural network model to be established must be a small‐scale one. Consequently, this soft computing approach includes two stages. In the first stage, the structural parameters are selected by using a ranking method, to find the most relevant parameters as the input variables to fit the small‐scale artificial neural network model. The first part of this method takes the human knowledge on the nonwoven products into account. The second part uses a data sensitivity criterion based on a distance method that analyzes the measured data of nonwoven properties. In the second stage, the artificial neural network model of the structure–property relations of nonwoven fabrics is established. The results show that the artificial neural network model yields accurate prediction and a reasonably good artificial neural network model can be achieved with relatively few data points by integrated with the input variable selecting method developed in this research. The results also show that there is great potential for this research in the field of computer‐assisted design in nonwoven technology. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 103: 442–450, 2007