Predicting Bursting Strength of Plain Knitted Fabrics using ANN
In this study, the effects of yarn parameters, on the bursting strength of the plain knitted fabrics were examined with the help of artificial neural networks. In order to obtain yarns having different properties such as tenacity, elongation, unevenness, the yarns were produced from six different types of cotton. In addition to cotton type, yarns were produced in four different counts having three different twist coefficients. Artificial neural network (ANN) was used to analyze the bursting strength of the plain knitted fabrics. As independent variables, yarn properties such as tenacity, elongation, unevenness, count, twists per inch together with the fabric property number of wales and courses per cm were chosen. For the determination of the best network architecture, three levels of number of neurons, number of epochs, learning rate and momentum coefficient were tried according to the orthogonal experimental design. After the best neural network for predicting the bursting strength of the plain knitted fabrics was obtained, statistical analysis of the obtained neural network was performed. Satisfactory results for the prediction of the bursting strength of the plain knitted fabrics were gained as a result of the study.
Total handle evaluation from selected mechanical properties of knitted fabrics using neural network
This paper concentrated on the objective evaluation of total hand value in knitted fabrics using the theory of neural networks and the comparison of two methods. For the objective evaluation of overall hand feeling in knitted fabric, 47 kinds of weft‐knitted and warp‐knitted fabrics were manufactured. The optimum construction of neural networks was investigated through the change of layer and neuron number. For the comparison of the two methods, a subjective test was carried out. Two techniques, KES‐FB system and neural network applied simulator, were compared using nine randomly selected knitted fabrics. These fabrics were used to show that the neural network adapted simulation method was in good agreement with subjective test results.
Effect of Lyocell Blend Yarn and Pile Type on the Properties of Pile Loop Knit Fabrics
This paper investigates some physical properties of pile loop knit fabrics. Several fabric parameters, such as pile type (pile loop and cut-pile loop fabrics), fiber type (100% cotton and 50/50% cotton/lyocell blend), fabric tightness (slack, medium, and tight), relaxation (dry relaxation and wash and dry relaxation), are changed. The effects of these parameters on the physical properties of the fabrics, such as dimensional, drapeability, abrasion, and spirality, are analyzed. To determine their relationship and significance, a bivariate correlation analysis and an analysis of variance are conducted. The presence of lyocell and cut-pile loop fabrics increases the lengthwise shrinkage and widthwise extension after repeated wash and dry cycles. Fabric thickness decreases with the presence of lyocell and decreases fabric tightness. Lyocell blend and ground-face fabrics have lower drape coefficients. Spirality tends to increase with the presence of lyocell and pile loop fabrics. Fabric abrasion behavior is also studied.