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.
Prediction of Fuzz Fibers on Fabric Surface by Using Neural Network and Regression Analysis
Fuzz on the fabric surface is very important for appearance quality, since it can lead to pilling and an unpleasant handle and appearance. Thus, it is important to predict the fuzz value on the fabric surface before producing the fabric. The amount of fuzz on the fabric surface, as has been determined by image processing techniques, can be predicted by two different methods, the Artificial Neural Network (ANN) and Regression Analysis. During knitting, the yarn used is abraded to an uncertain degree, depending on the knitting conditions and yarn properties, and this situation can lead to poor prediction results. However, it has been demonstrated that the prediction results obtained by Artificial Neural Networks look more promising than that of Regression Analyses.
Metallurgical Process Engineering
The "material of choice" - steel.- Uprising of China's steel industry.- Process manufacturing industry and process engineering.- Engineering science in steel manufacturing process.- Analysis and integration of metallurgical manufacturing process.- Multi-dimensional mass flow control for metallurgical manufacturing process.- Time factors in steel manufacturing process.-Operating dynamics in the production process of steel plant.- Structure and mode of steel plant.- Steel plant and environment.