Prediction of Work Parameters in a Cold Mill Using Neural Network
Keywords:
cold rolling mill, stand, work parameters
Abstract
The accurate prediction of work parameters is essential for a quality product. A mathematical model is used for parameters calculus. It is important to directly predict the roll force and the other parameters and to compute a corrective coefficient. Combining the network of work parameters and the mathematical model grove up the possibility to obtain parameter who give a new quality for laminates strip.
References
[1]. Sungzoon Cho, Yongjung Cho, Sungchul Yoon, “Reliable Roll Force Prediction in Cold Mill Using Multiple Neural Network” IEE TRANSACTIONS ON NEURAL NETWORK, vol. 8, no. 4, 1977.
[2]. C. Bishop, “Neural Networks for Pattern Recognition”. Oxford,U.K:Oxford Univ. Press. 1995.
[3]. D.Cohn, L. Atlas, R. Ladner, ”Improving generalization with active learning”, Machine Learning, vol. 15, no. 2, p. 201-221, 1994.
[4]. Pohang Iron and Steel Company Tech.Rep.2nd Cold Mill Contr. Equipment (PCM part), POSCO, Korea, 1989.
[5]. W. Lee, “Improvement of set-up model for tandem cold rolling mill”, Tech. Rep. POSCO Res.Inst.Sci.Technol., 1994.
[6]. N. Pican.F., Alexandre and P. Bresson, ”Artificial neural networks for the presetting of a steel temper mill”, IEEE Expert, vol. 11, no. 1, p. 22-27, 1996.
[7]. N. Portman. ”Aplication of neural nerworks in rolling mill automation”, Iron and Steel Eng., vol. 72, no. 2, p. 33-36, 1995.
[8]. B. Rosen, ”Ensemble learning using decorrelated neural networks”, Connections Sci., vol. 8, p. 373-384, 1996.
[2]. C. Bishop, “Neural Networks for Pattern Recognition”. Oxford,U.K:Oxford Univ. Press. 1995.
[3]. D.Cohn, L. Atlas, R. Ladner, ”Improving generalization with active learning”, Machine Learning, vol. 15, no. 2, p. 201-221, 1994.
[4]. Pohang Iron and Steel Company Tech.Rep.2nd Cold Mill Contr. Equipment (PCM part), POSCO, Korea, 1989.
[5]. W. Lee, “Improvement of set-up model for tandem cold rolling mill”, Tech. Rep. POSCO Res.Inst.Sci.Technol., 1994.
[6]. N. Pican.F., Alexandre and P. Bresson, ”Artificial neural networks for the presetting of a steel temper mill”, IEEE Expert, vol. 11, no. 1, p. 22-27, 1996.
[7]. N. Portman. ”Aplication of neural nerworks in rolling mill automation”, Iron and Steel Eng., vol. 72, no. 2, p. 33-36, 1995.
[8]. B. Rosen, ”Ensemble learning using decorrelated neural networks”, Connections Sci., vol. 8, p. 373-384, 1996.
Published
2006-05-15
How to Cite
1.
DRAGOMIR S, BORDEI M, TUDOR B. Prediction of Work Parameters in a Cold Mill Using Neural Network. The Annals of “Dunarea de Jos” University of Galati. Fascicle IX, Metallurgy and Materials Science [Internet]. 15May2006 [cited 3May2024];29(1):81-4. Available from: https://www.gup.ugal.ro/ugaljournals/index.php/mms/article/view/3234
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