Investigation of Effect of Roller Setting on Yarn Quality Properties in Draw Frame
In the scope of the study, it was aimed to determine the effect of the variation of the roller setting on the selected yarn properties in the first and second passage draw frame stages. For this purpose, two different draft distances were chosen for both passages and draw frame strip was produced. Then, these strips were made into yarn with constant production parameters. The hairiness, unevenness and strength properties of the yarns were tested with standard method. As a result, it was observed that increasing the draft distance affects negatively the selected three yarn properties. In this case, it has been determined that the shorter draft distance has more positive results in terms of yarn quality parameters for Şanlıurfa GAP cotton selected as the sample in the study.
Optimization of doubling at draw frame for quality of carded ring yarn
Sliver has a huge impact on yarn quality parameter like evenness, imperfection index, Cvm%, U% of yarn. In general doubling means the increase in weight per unit length. Doubling is a process used in yarn manufacturing by which a single product is produced from a several intermediate feed product. The study was performed on 0.12 hank card sliver which was used to produce 24/1 Ne carded yarn. At first the number of doubling in the breaker and finisher drawing was 6, 7, and 8 respectively. The paper is to find out whether the change of number of doubling has any effect on yarn quality or to find out the suitable number of sliver doubling in passage 1 and passage 2.
Development of prediction system using artificial neural networks for the optimization of spinning process
This article correlates draw frame settings with quality characteristics of sliver and ring spun yarn using artificial neural networks. Considering the importance of draw frame as the last quality improvement machine in the spinning process, the quality influencing parameters of the draw frame were used as input for artificial neural networks. The neural networks were trained using a combination of Levenberg-Marquardt algorithm and Bayesian regularization for better generalization of the networks. Cross validation was performed for each trained network to test the performance of networks. The promising results achieved by this research work emphasize the ability of neural networks to predict the quality characteristics of sliver and yarn using the artificial neural networks. Therefore, draw frame parameters can be adjusted on the basis of required sliver and yarn quality. Furthermore, machines can be involved in the decision making process in spinning mills.