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- Title
Shape recognition performance analysis and improvement in Sendzimir rolling mills.
- Authors
Jeong, Cheol; Park, Jung; Han, Seong; Kim, Jong
- Abstract
Twenty-high Sendzimir rolling mills (ZRMs) typically use small diameter work rolls to provide massive pass reduction. Because of the small diameter of the work rolls, a rolled steel strip has a complex shape mixed with quarter, edge, and center waves. When the strip shape is controlled automatically, actuator saturation occurs in the shape actuator such as AS-U rack. These problems affect productivity and the quality of products made from the rolled material. We analyzed problems on the shape control system of a ZRM. The shape recognition performance was analyzed by comparing the measured and recognized shapes by multi-layer perceptron (MLP) method. In addition, neural network using the radial basis function (RBF) method was proposed to improve the shape recognition performance of the shape control system in a ZRM. P-gain which compensates the scale of the strip shape is added to prevent actuator saturation. Finally, we verify the variation of actuator position using ZRM's shape control simulator. Through simulation results, we found that shape recognition performance can be improved by the proposed method based on RBF neural network and actuator saturation problem can be improved by increasing shape recognition performance.
- Subjects
ROLLING-mills; RADIAL basis functions; GEOMETRIC shapes; DEFORMATIONS (Mechanics); FUZZY control systems; ELECTRIC motors; GAIN measurement
- Publication
Journal of Mechanical Science & Technology, 2014, Vol 28, Issue 4, p1455
- ISSN
1738-494X
- Publication type
Article
- DOI
10.1007/s12206-013-0965-2