Neuro-fuzzy Modelling and Experimental Study of the Physiological Comfort of Green Cotton Fabric Based on Yarn Properties

Abstract

In textile and garment industry, the physiological comfort of fabric as one of the important parameters, can be improved by the fabric finishing treatment. Nevertheless, the toxic chemicals produced in this process leads to the pollution of the environment. Therefore, this study aims to improve the physiological comfort of the cotton fabric without applying the finishing process as green technology. Accordingly, air permeability and moisture transfer as two important parameters of the fabric physiological comfort are evaluated with the structural parameters of the cotton yarn using experimental and theoretical procedures. For theoretical evaluations, a novel neuro-fuzzy network (ANFIS) is proposed and used for modelling and estimation. The structural parameters of yarn are the yarn linear density, yarn twist and fineness of fibers, which are defined as inputs and air permeability and moisture transfer of the cotton samples are considered as the outputs of developed ANFIS model. According to the experimental and modeling results, the fiber fineness, yarn linear density (Ne) and yarn twist have the same effect on the output parameters. It is also found that both parameters of the physiological comfort sensory can be improved effectively without finishing process. Simulation results show the novel proposed ANFIS that has high learning capability, fast convergence and accuracy greater than 99% and negligible error value smaller than of 1% can be reasonably used in textile industry. In addition, for the winter garments, the optimum points of turns per meter (T.P.M) coefficient, English count of yarn, and fibers fineness are 4.5, 25 and 3, respectively.