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.
A study of the effect of PEG-1000 phase change materials coating on selected fabric properties
Phase change materials (PCM) are materials that are used as latent heat storage media due to their ability to undergo change from one phase to another. The use of PCM to enhance the temperature self-regulating feature of textiles has become prevalent in recent times. A PCM is incorporated into the textile substrate using various means, one of which is coating. However, the process of coating the textile substrate with the PCM material, as well as the effect of the material itself, may result in the reduction of certain key properties of the textile material. The aim of this study is to examine the effect of a Sodium Alginate microencapsulated PEG-1000 coating (applied by the pad-dry-cure method) on selected properties of a 100% polyester single jersey knitted fabric, namely bursting strength, thickness, drape coefficient, air permeability, moisture regain and fabric weight. The material and coating were selected on the basis of availability, use and cost. The study showed that the coating provided small increases in the bursting strength, weight, moisture regain and thickness, and a substantial increase in the drape coefficient of the fabric. The air permeability could not be properly assessed due to an instrument error. As such the coating could be presumed to have caused a slight impairment to the overall comfort of the fabric.
Yarn Strength Prediction Using Neural Networks
Over the past several years, numerous relationships between fiber properties and yarn performance have been developed. The classical method for predicting yam performance is statistical analysis such as linear multiple regression. Neural networks are now a subject of interest in many fields and a tool for many areas of problem solving. This study investigates the application of neural networks to yarn strength prediction. HVI test results of the 1989 crop are used to train the neural nets and performance is discussed.