Modelling of Needle-Punched Nonwoven Fabric Properties Using Artificial Neural Network

Abstract

Needle-punched nonwoven is an industrial fabric used in wide range of applications areas. The physical structure of needle-punched nonwoven is very complex in nature and therefore engineering the fabric according the required properties is difficult. Because of this, the basic mathematical modeling is not very successful for predicting various important properties of the fabrics. In recent days, artificial neural networks (ANN) have shown a great assurance for modeling non-linear processes. Rajamanickam et al., 1997 and Ramesh et al., 1995 used ANN to model the tensile properties of air jet yarn. The ANN model had also been used to model to assess the set marks and also the relaxation curve of yarn after dynamic loading (Vangheluwe et al., 1993 and 1996). Luo & David, 1995 used the HVI experimental test results to train the neural nets and predict the yarn strength. Researchers also made an attempt to build models for predicting ring or rotor yarn hairiness using a back propagation ANN model by Zhu & Ethridge, 1997. Fan & Hunter, 1998 developed ANN for predicting the fabric properties based on fibre, yarn and fabric constructional parameters and suggested the suitable computer programming for development of neural network model using back-propagation simulator. Wen et al., 1998 used back-propagation neural network model for classification of textile faults. Postle, 1997 enlighten on measurement and fabric categorisation and quality evaluation by neural networks. Park et al., 2000 also enlightened the use of fuzzy logic and neural network method for hand evaluation of outerwear knitted fabrics. Gong & Chen, 1999 found that the use of neural network is very effective for predicting problems in clothing manufacturing. Xu et al., 1999 used three clustering analysis technique viz. sum of squares, fuzzy and neural network for cotton trash classification. They found neural network clustering yields the highest accuracy, but it needs more computational time for network training. Vangheluwe et al., 1993 found Neural nets showed good results assessing the visibility set marks in fabrics. The review of literature shows that the ANN model is a powerful and accurate tool for predicting a nonlinear relationship between input and output variables. Jute, polypropylene, jute-polypropylene blended and polyester needle punched nonwoven fabrics have been prepared using series of textile machinery normally used in needlepunching process for preparation of the fabric samples. Textile materials are compressive in