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.
Predicting Bursting Strength of Cotton Plain Knitted Fabrics Using Intelligent Techniques
The bursting strength of cotton plain knitted fabrics is predicted before manufacturing using intelligent techniques of neural network and neuro-fuzzy approaches in this re search. Among many parameters that affect fabric bursting strength, fabric weight, yarn breaking strength, and yarn breaking elongation are input elements for the predictions. In this research, both the multi-layer feed-forward neural network and adaptive network- based fuzzy inference system, a combination of a radial basis neural network and the Sugeno-Takagi fuzzy system, are studied. Both systems have the ability to learn training data successfully, and testing errors are small enough to give an approximate knowledge of the bursting strength of the fabric to be knitted.
An Empirical Model for Fabric Hand
A new model for fabric hand assessment is developed that uses fuzzy comprehensive evaluation to predict hand. One of the main aspects of this research is the establishment of a fuzzy relation matrix R for objective evaluation of fabric hand. This was achieved by investigating a large number ( n = 100) of light weight fabrics for selected physical and mechanical properties and developing a series of membership functions for the properties tested. Another important element is the weighted factor vector A, which was deduced from a survey of a panel of judges ( n = 25). A in conjunction with R generates multifactorial equations that can be used to calculate fabric hand.