An optimal artificial neural network system for designing knit stretch fabrics

In this paper, a computer-aided system for designing knit stretch materials is presented. It allows designers to optimize the structure of knit stretch materials according to the functional properties. This system aims at modeling the relation between functional properties (outputs) and structural parameters (inputs) of knitted fabrics. Thirteen features characterizing knit structures and operating parameters were taken as input parameters of the artificial neural networks (ANNs). These parameters were preselected according to their possible influence on the outputs which were the elongation, growth, and elastic recovery. In order to reduce the complexity of the models, an original fuzzy logic-based method was proposed to select the most relevant parameters which were taken as input variables of the ANNs. The selection procedure of structural parameters allows designers to focus on the most relevant parameters in order to conduct production experiments related to the new product. Then, two types of model are set up by utilizing multilayer feed forward neural networks, which take into account the generality and the specificity of the product families, respectively. The presented models have been validated with the use of experimental data concerning several families of knitted fabrics.