Genetic Algorithms in Hot Steel Rolling for Scale Defect Prediction
Scale defects are common surface defects in hot steel rolling. The modelling of such defects is problematic and their causes are not straightforward. In this study, we investigated genetic algorithms in search for a mathematical solution to scale formation. For this research, a high-dimensional data set from hot steel rolling process was gathered. The synchronisation of the variables as well as the allocation of the measurements made on the steel strip were solved before the modelling phase.
[1] P.H. Bolt, F. Friedel, H. Pircher, X. Cornet, S. Ehlers, and F. Steinert,
"Investigation of the formation, constitution and properties of scale
formed during the finishing rolling, cooling and coiling of thin hot
strips," ECSC steel RTD project tech. rep.
[2] T. Fukagawa, H. Okada, and Y. Maehara, "Mechanism of red scale
defect formation in Si-added hot-rolled steel sheets," Tetsu-to-Hagane
(Iron and Steel Institute of Japan), vol. 34 no. 11, 1994, pp. 906-911.
[3] V. Ginzburg, Steel-rolling technology: theory and practice. Marcel
Dekker Inc. New York ,1989.
[4] Surface defects in hot rolled flat steel products. Verlag Stahleisen Gmbh,
1996.
[5] J. Andorfer, D. Paesold, R. Puntigam, K. Rendl, J. Zeindl, D. Auzinger,
G. Hubmer, "New achievements in quality control systems for hot
band," METEC 2003, 6th international metallurgy trade fair.
[6] J.H. Holland, Adaptation in natural and artificial systems: an
introductory analysis with applications to biology, control, and artificial
intelligence. The University of Michigan Press, Ann Arbor, 1975.
[7] R.Y. Chen, and W.Y.D. Yuen, "Effects of finishing and coiling
temperatures on the scale structure and picklability of hot rolled strip,"
Iron and Steelmaker, vol. 27 no. 4, 2000, pp. 47-53.
[8] K.S. Tan, M. Krzyzanowski, and J.H. Beynon, "Effect of Steel
Composition on Failure of Oxide Scales in Tension under Hot Rolling
Conditions," Steel Research, vol. 72 no. 7, 2001, pp. 250-258.
[9] J. Haapam├ñki, S. Tamminen, and J. Röning, "Data mining methods in
scale defect prediction for hot steel rolling," in Proc. 23rd IASTED Int.
Conf. Artificial Intelligence and Applications, Feb. 2005 pp. 90-94
[10] Y. Bisseur, E.B. Martin, A.J. Morris, and P. Kitson, "Fault detection in
hot steel rolling using neural networks and multivariate statistics," IEE
Proc. Control Theory and Applications, 147(6), 2000, pp. 633-640.
[11] T. Kohonen, Self organizing maps. Springer, Berlin, 1995.
[12] E. Alhoniemi, J. Hollmén, O. Simula, and J. Vesanto, "Process
monitoring and modeling using the self-organizing map," Integrated
Computer-Aided Engineering, vol. 6 no. 1, 1999, pp. 3-14.
[13] L. Cser, A.S. Korhonen, J. Gulyás, P. Mäntylä, O. Simula, Gy. Reiss,
and P. Ruha, "Data mining and state monitoring in hot rolling," Proc. of
the 2nd Int. Conf. on Intelligent Processing and Manufacturing of
Materials, Honolulu, Hawaii, July 10-15, 1999, Vol. 1, IEEE, pp. 529-
537.
[14] M. Krzyzanowski, and J.H. Beynon, "The tensile failure of mild steel
oxides under hot rolling conditions," Steel Research vol. 70 no. 1, 1999,
pp. 22-27.
[15] S. Haykin, Neural networks: a comprehensive foundation. Macmillan
Publishing Company, 1994.
[16] Z. Michalewicz, Genetic algorithms + data structures = evolution
programs. 3. ed. 1996 Springer-Verlag.
[17] M. Kotani, M. Ochi, S. Ozawa, and K. Akazawa, "Evolutionary
discriminant functions using genetic algorithms with variable-length
chromosome," Proc. Int. joint conference on Neural Networks, 2001,
IJCNN pp. 761-766.
[18] J.R. Koza, Genetic programming: "A paradigm for genetically breeding
populations of computer programs to solve problems," Stanford
University, 1990. Available http://www.geneticprogramming.
com/jkpdf/tr1314.pdf
[19] D.E. Goldberg, Genetic algorithms in search optimization and machine
learning. Addison-Wesley, 1989.
[1] P.H. Bolt, F. Friedel, H. Pircher, X. Cornet, S. Ehlers, and F. Steinert,
"Investigation of the formation, constitution and properties of scale
formed during the finishing rolling, cooling and coiling of thin hot
strips," ECSC steel RTD project tech. rep.
[2] T. Fukagawa, H. Okada, and Y. Maehara, "Mechanism of red scale
defect formation in Si-added hot-rolled steel sheets," Tetsu-to-Hagane
(Iron and Steel Institute of Japan), vol. 34 no. 11, 1994, pp. 906-911.
[3] V. Ginzburg, Steel-rolling technology: theory and practice. Marcel
Dekker Inc. New York ,1989.
[4] Surface defects in hot rolled flat steel products. Verlag Stahleisen Gmbh,
1996.
[5] J. Andorfer, D. Paesold, R. Puntigam, K. Rendl, J. Zeindl, D. Auzinger,
G. Hubmer, "New achievements in quality control systems for hot
band," METEC 2003, 6th international metallurgy trade fair.
[6] J.H. Holland, Adaptation in natural and artificial systems: an
introductory analysis with applications to biology, control, and artificial
intelligence. The University of Michigan Press, Ann Arbor, 1975.
[7] R.Y. Chen, and W.Y.D. Yuen, "Effects of finishing and coiling
temperatures on the scale structure and picklability of hot rolled strip,"
Iron and Steelmaker, vol. 27 no. 4, 2000, pp. 47-53.
[8] K.S. Tan, M. Krzyzanowski, and J.H. Beynon, "Effect of Steel
Composition on Failure of Oxide Scales in Tension under Hot Rolling
Conditions," Steel Research, vol. 72 no. 7, 2001, pp. 250-258.
[9] J. Haapam├ñki, S. Tamminen, and J. Röning, "Data mining methods in
scale defect prediction for hot steel rolling," in Proc. 23rd IASTED Int.
Conf. Artificial Intelligence and Applications, Feb. 2005 pp. 90-94
[10] Y. Bisseur, E.B. Martin, A.J. Morris, and P. Kitson, "Fault detection in
hot steel rolling using neural networks and multivariate statistics," IEE
Proc. Control Theory and Applications, 147(6), 2000, pp. 633-640.
[11] T. Kohonen, Self organizing maps. Springer, Berlin, 1995.
[12] E. Alhoniemi, J. Hollmén, O. Simula, and J. Vesanto, "Process
monitoring and modeling using the self-organizing map," Integrated
Computer-Aided Engineering, vol. 6 no. 1, 1999, pp. 3-14.
[13] L. Cser, A.S. Korhonen, J. Gulyás, P. Mäntylä, O. Simula, Gy. Reiss,
and P. Ruha, "Data mining and state monitoring in hot rolling," Proc. of
the 2nd Int. Conf. on Intelligent Processing and Manufacturing of
Materials, Honolulu, Hawaii, July 10-15, 1999, Vol. 1, IEEE, pp. 529-
537.
[14] M. Krzyzanowski, and J.H. Beynon, "The tensile failure of mild steel
oxides under hot rolling conditions," Steel Research vol. 70 no. 1, 1999,
pp. 22-27.
[15] S. Haykin, Neural networks: a comprehensive foundation. Macmillan
Publishing Company, 1994.
[16] Z. Michalewicz, Genetic algorithms + data structures = evolution
programs. 3. ed. 1996 Springer-Verlag.
[17] M. Kotani, M. Ochi, S. Ozawa, and K. Akazawa, "Evolutionary
discriminant functions using genetic algorithms with variable-length
chromosome," Proc. Int. joint conference on Neural Networks, 2001,
IJCNN pp. 761-766.
[18] J.R. Koza, Genetic programming: "A paradigm for genetically breeding
populations of computer programs to solve problems," Stanford
University, 1990. Available http://www.geneticprogramming.
com/jkpdf/tr1314.pdf
[19] D.E. Goldberg, Genetic algorithms in search optimization and machine
learning. Addison-Wesley, 1989.
@article{"International Journal of Electrical, Electronic and Communication Sciences:63376", author = "Jarno Haapamäki and Juha Röning", title = "Genetic Algorithms in Hot Steel Rolling for Scale Defect Prediction", abstract = "Scale defects are common surface defects in hot steel rolling. The modelling of such defects is problematic and their causes are not straightforward. In this study, we investigated genetic algorithms in search for a mathematical solution to scale formation. For this research, a high-dimensional data set from hot steel rolling process was gathered. The synchronisation of the variables as well as the allocation of the measurements made on the steel strip were solved before the modelling phase.
", keywords = "Genetic algorithms, hot strip rolling, knowledge discovery, modeling.", volume = "1", number = "5", pages = "780-4", }
