Abstract
In the melt-spinning process, evenness of melt-spun yarns will affect the appearance, hairiness, strength, and the production of yarns. Great variations in yarns may cause defects. Yarns with small variation can have a more stable quality. In this study, we applied the fuzzy neural network (FNN) control theory to the melt-spinning machine. By adjusting the speed of the take-up rollers, the average yarn diameter reached the target value, allowing a reduction in variations. Diameter error and diameter error variation were also used as input, and the increments in take-up roller speed were used as output. The FNN was used to adjust the mean and standard deviation of the membership function in the second layer and the connection weighted value of the third and fourth layers in order to achieve convergence and the learning effect. The experimental results showed that the FNN controller maintained the average diameter of yarns and reduced the variation of the yarn diameter. Therefore, the proposed method could be successfully applied to on-line control of yarn evenness.
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