Edge-Drop Control Behavior for Silicon Strip Cold Rolling with a Sendzimir Mill

Edge-drop control is important for silicon strip cold rolling, as the silicon strip is mainly used as a laminated core. Moreover, cold rolling is the key process for the thin strip edge-drop control, and a Sendzimir mill is one of the most popular cold rolling mills for silicon strips. Thus, the mastery of edge-drop control behavior for silicon strip cold rolling with a Sendzimir mill is beneficial for the improvement of the strip profile quality. With the finite element method, two models are built to analyze the edge-drop control behavior, one is the roll system and strip integrated elastic-plastic deformation statics model, and the other is the strip plastic deformation dynamics model. The first model provides the roll gap contour for the second model, then the strip profile can be calculated in the second model, which considers the transverse flow of the metal. Firstly, the compositions of edge-drop for the silicon strip are analyzed systematically, which are the edge-drop for work roll bending, the edge-drop for work roll flattening, and the edge-drop for transverse flow of the metal. Secondly, the influence of different rolling process parameters on the three parts are analyzed, such as the entrance thickness, the rolling reduction, the rolling tension, and so on; further, the influence of the roll contours are also analyzed. Finally, the edge-drop control behavior of the different rolling process parameters and roll contours are obtained. The research results provide theoretical guidance for edge drop control in the Sendzimir mill.

Improved shape control performance of a Sendzimir mill using wavelet radial basis function network and fuzzy logic actuator

A shape control system based on a wavelet radial basis function network for a Sendzimir mill (ZRM) and fuzzy control are developed to improve the shape control performance of a conventional ZRM system. The conventional shape recognition system for a ZRM adopted an incomplete multi-layer perceptron neural network system that was constructed two decades ago. The poor shape recognition of this system leads to actuator saturation and shape control performance deterioration. Therefore, the full automatic operation of a ZRM is often stopped, and manual input need to be performed. This affects the quality, causes a decline in the productivity of the steel strip and an unnecessary waste of manpower. In this paper, a wavelet radial basis network is developed to replace the multi-layer perceptron network and consequently improve shape recognition performance. A modified fuzzy controller is also constructed to prevent actuator saturation that occurs in a conventional shape control system owing to the use of a fixed gain-based fuzzy controller. A comparative simulation based on the data measured from an actual ZRM plant demonstrates the efficacy of the proposed shape control system.