On the Prediction of the Strip Shape in a Cold Rolling Mill (1700 mm)
Keywords:
shape, prediction, dynamic load, monitoring, roll bending
Abstract
In this paper is shown a new way for predicting the precision of laminated strip in a cold rolling mill (1700 mm). The increasing demands on the quality of rolled strip; need new technology for monitoring the strip shape, by using complex system control for technological parameter of the rolling mill process. It is very important to reduce the dynamic load, to choose the optimal functionary parameters and modern systems to control the stress, tensions, lamination force and speed in cold rolling mill machine.
References
[1]. Tamiga,T.Furui K., 1990, Analysis of Complex Phenomenon in Cold Rooling “Proccedings of International Conference on Steel Rolling, vol. 2, Tokyo.
[2]. Forsyth P.I.E., 1991, The physical basis of Metal Deformation Blockie and Sou, London.
[3]. The Shock and Vibration Digest ,1992., Published by Shock and Vibration Information Center, Washington D.C.
[4]. Sungzoon Cho,Yongjung Cho, Sungchul Yoon, 1977, Reliable Roll Force Prediction in Cold Mill Using Multiple Neural Network, IEE Transactions On Neural Network, vol. 8, no. 4.
[5]. Bishop C., 1995, Neural Networks for Pattern Recognition, Oxford,U.K:Oxford Univ. Press.
[6]. Cohn D., Atlas L., Ladner R., 1994, Improving generalization with active learning, Machine Learning, vol. 15, no. 2, p. 201-221.
[7]. Pohang Iron and Steel Company Tech.Rep., 1989, 2nd Cold Mill Contr. Equipment (PCM part), POSCO, Korea.
[8]. Lee W., 1994, Improvement of set-up model for tandem cold rolling mill, Tech. Rep. POSCO Res. Inst. Sci. Technol.
[9]. Pican N, Alexandre F. and Bresson P., 1996, Artificial neural networks for the presetting of a steel temper mill, IEEE Expert, vol. 11, no. 1, p. 22-27.
[10]. Portman N., 1995, Application of neural nerworks in rolling mill automation, Iron and Steel Eng., vol. 72, no. 2, p. 33-36.
[11]. Rosen B., 1996, Ensemble learning using decorrelated neural networks, Connections Sci., vol. 8, p. 373-384.
[2]. Forsyth P.I.E., 1991, The physical basis of Metal Deformation Blockie and Sou, London.
[3]. The Shock and Vibration Digest ,1992., Published by Shock and Vibration Information Center, Washington D.C.
[4]. Sungzoon Cho,Yongjung Cho, Sungchul Yoon, 1977, Reliable Roll Force Prediction in Cold Mill Using Multiple Neural Network, IEE Transactions On Neural Network, vol. 8, no. 4.
[5]. Bishop C., 1995, Neural Networks for Pattern Recognition, Oxford,U.K:Oxford Univ. Press.
[6]. Cohn D., Atlas L., Ladner R., 1994, Improving generalization with active learning, Machine Learning, vol. 15, no. 2, p. 201-221.
[7]. Pohang Iron and Steel Company Tech.Rep., 1989, 2nd Cold Mill Contr. Equipment (PCM part), POSCO, Korea.
[8]. Lee W., 1994, Improvement of set-up model for tandem cold rolling mill, Tech. Rep. POSCO Res. Inst. Sci. Technol.
[9]. Pican N, Alexandre F. and Bresson P., 1996, Artificial neural networks for the presetting of a steel temper mill, IEEE Expert, vol. 11, no. 1, p. 22-27.
[10]. Portman N., 1995, Application of neural nerworks in rolling mill automation, Iron and Steel Eng., vol. 72, no. 2, p. 33-36.
[11]. Rosen B., 1996, Ensemble learning using decorrelated neural networks, Connections Sci., vol. 8, p. 373-384.
Published
2008-05-15
How to Cite
1.
DRAGOMIR S, DRAGOMIR G, BORDEI M. On the Prediction of the Strip Shape in a Cold Rolling Mill (1700 mm). The Annals of “Dunarea de Jos” University of Galati. Fascicle IX, Metallurgy and Materials Science [Internet]. 15May2008 [cited 2May2024];31(1):84-7. Available from: https://www.gup.ugal.ro/ugaljournals/index.php/mms/article/view/3130
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