Abstract
An artificial intelligence-based system approach is presented in which the effects of the operating parameters and intrinsic features of yarn and fabric on Thermal Conductivity of Stretch Knitted Fabrics are investigated. These parameters were pre-selected according to their possible influence on the outputs which were the thermal conductivity. An original fuzzy logic based method was proposed to select the most relevant parameters. The results show that Knitted Structure’s is the most important input parameter, followed by Lycra Proportion (%), Loop length (cm), Yarn Count, Weight per Unit Area (g/m2), Thickness (m), Gauge, Lycra Yarn Count (dtex) and Yarn Composition. According to our previous works, two types of model have been set up by utilizing multilayer feed forward neural networks, which take into account the generality and the specificity of the product families respectively. The relative importance of the input variables was calculated using the connection weight approach. The results were found to agree with the fuzzy logic based sensitivity criterion. The trend analysis of the developed model revealed the influence of various input parameters on the thermal conductivity of knitted fabrics. Thus, it is believed that artificial intelligence System could efficiently be applied to the knit industry to understand, evaluate and predict thermal comfort parameters of stretch knitted fabrics.
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