Abstract
All the factors that influence the rolling force are analyzed, and the neural network model which uses the back propagation (BP) learning algorithm for the calculation of rolling force is created. The initial network’s weights corresponding to the input material grades are taught by the traditional theoretical model, and saved in the database. In order to increase the prediction accuracy of rolling force, we use the measured rolling force data to teach the neural network after several coils of the same input material are rolled down.
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Yang, J., Che, H., Dou, F., Liu, S. (2007). Application of Neural Network on Rolling Force Self-learning for Tandem Cold Rolling Mills. In: Liu, D., Fei, S., Hou, ZG., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4491. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72383-7_57
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DOI: https://doi.org/10.1007/978-3-540-72383-7_57
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72382-0
Online ISBN: 978-3-540-72383-7
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