Thermo-physiological properties of textiles are crucial in determining the heat and moisture transport from skin to environment and
in the assessment of overall wearer comfort. Engineering fabrics with desirable thermo-physiological properties suited for specific applications such as active wear, intimate wear is a big challenge for textile manufacturers as gamut of fibre, yarn and fabric parameters are known to influence the thermo-physiological properties. The present study was undertaken with an aim to explore suitable combination of fibre and yarn variables for engineering polyester–cotton plated fabrics with good thermo-physiological properties. Categorical variables i.e. outer layer yarn type and inner layer fibre linear density were found to affect the thermal, moisture vapour and liquid moisture transfer properties of developed test samples. Fabrics knitted with carded yarn and polyester fibre of high linear density showed high thermal resistance and would feel warmer on initial skin contact owing to low thermal absorptivity. However, the air permeability and moisture vapour transmission rate increased with combination of combed cotton yarn in the outer and coarse polyester fibre in the inner layer. Combed yarn fabrics were superior in trans planar wicking compared to carded yarn fabrics which showed higher water absorbency and would be slow drying fabrics.
Thermo-physiological properties of textiles play a very crucial role in providing thermal equilibrium to human beings in changing ambient conditions and activity level and in turn dictate the overall wearer comfort. A number of prediction tools like mechanistic, statistical and stochastic (artificial neural network) models are finding application in textiles for reasonable prediction of various aspects of textiles before the actual commencement of fabric production and testing. In this study, thermo-physiological properties of polyester–cotton plated fabrics were predicted by two approaches: artificial neural network and response surface equations. A multilayered back propagation artificial neural network was developed with four input nodes corresponding to four selected input parameters: back layer yarn linear density, filament fineness, total yarn linear density and loop length and one output node corresponding to the predicted thermo-physiological property. Four individual networks working in tandem with common set of input parameters and each giving an individual output were developed such that the outputs of four networks were thermal resistance, thermal absorptivity, air permeability and moisture vapour transmission rate respectively. Network architecture gave good prediction performance with low values of mean absolute percentage error and high coefficient of determination. Response surface equations were developed to predict the thermo-physiological properties and good agreement between experimental and predicted values for all the properties was found with coefficient of determination over 0.9. Artificial neural network predicted the thermal resistance and air permeability of plated fabrics with good accuracy. However, the response surface equations served better prediction tool for thermal absorptivity and moisture vapour transmission rate prediction.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.