Research about the Vibration Parameters for a Cold Rolling Mill Machine
Keywords:
cold rolling mill, strip, vibration, prediction, undulation
Abstract
By using the equipments of vibration measurement we can fight against the damages (strip undulation and thickness variation on the length) who is show in while of mill work. The amplitude of vibration parameters determine the apparition of patterns on laminated strip.
The researches about the vibration parameters are essential for a product quality. To directly introduce by a milling program, the roll force and the other parameters, these must be in correlation with amplitude acceleration, frequency and velocity vibration.
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, Nr. 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.Alexandreand.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.
[9]. C. Bishop, “Neural Networks for Pattern Recognition”. Oxford, U.K: Oxford Univ. Press. 1995.
[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.
[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.Alexandreand.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.
[9]. C. Bishop, “Neural Networks for Pattern Recognition”. Oxford, U.K: Oxford Univ. Press. 1995.
[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.
Published
2006-11-15
How to Cite
1.
DRAGOMIR S, DRAGOMIR G, BORDEI M. Research about the Vibration Parameters for a Cold Rolling Mill Machine. The Annals of “Dunarea de Jos” University of Galati. Fascicle IX, Metallurgy and Materials Science [Internet]. 15Nov.2006 [cited 3May2024];29(2):31-4. Available from: https://www.gup.ugal.ro/ugaljournals/index.php/mms/article/view/3200
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