Improvement of Shape Recognition Performance of Sendzimir Mill Control Systems Using Echo State Neural Networks Original Research Article

High rigidity twenty-high Sendzimir mills (ZRMs) are widely used for rolling stainless steels, silicon sheets, etc. A ZRM uses a small diameter work roll to produce massive rolling forces. Since a work roll with a small diameter can be bent easily, strips often have complex shapes with mixed quarter and deep edge waves in the shape of plates. In order to solve this problem, fuzzy neural network controls arc generally used for shape recognition in ZRM control systems. Among various neural network types, the multi-layer perceptron (MLP) is typically used in current ZRMs. However, an MLP causes the loss of a large amount of shape recognition data. To improve the shape recognition performance of ZRM control systems, echo state networks (ESNs) arc proposed to be used. Through simulation results, it is found that shape recognition performance could be improved using the proposed ESN method.