Modeling of thermal properties of multilayered fabrics by ANN consisting of polypropylene needle-punched nonwovens

Abstract

This paper presents the prediction of thermal and evaporative resistances of multilayered fabrics meant for cold weather conditions using artificial neural network (ANN) model. Thermal and evaporative resistances of fabrics were evaluated using sweating guarded hot plate method. The significance and interdependency of thickness on other fabric and process parameters and its effect on prediction performance of ANN model is analyzed in detail. For this purpose, two different network architectures were used to predict the thermal properties of multilayered fabrics. In both the networks, three-layer structure consisting of input, hidden and output layers was used. First, network was constructed with four input parameters, namely linear density of fiber, mass per unit area, punch density, and thickness of nonwoven fabric which predicts thermal and evaporative resistances. Second network was made with three input parameters, namely linear density, mass per unit area, and punch density. The network parameters were optimized to give minimum mean square error (MSE), mean absolute error percentage, and good correlation coefficient. The trend analysis was conducted and influence of various input parameters on the thermal properties of multilayered fabrics was studied. The significance of each input parameter in the prediction of thermal properties was studied by carrying out sensitivity analysis. The mean square error of the test dataset before and after the exclusion of the corresponding input parameter is taken for analysis. The input parameters were ranked based on the MSE ratio of test dataset. The predicted thermal properties of multilayered fabrics are correlated well with the experimental values. It was observed that the ANN model with minimum input parameters, namely linear density of fiber, mass per unit area, and punch density can predict the thermal properties of multilayered fabrics with good accuracy.