Comparison of empirical and neural network hot-rolling process models

Abstract

Steel manufacturers are under pressure to improve their productivity levels by optimizing their process parameters to create maximum efficiency and quality levels. One of the keys to achieve this goal is the automation of the steel making process. Automation using artificial intelligence techniques applied to the hot rolling process is a potentially important steel manufacturing technique. The mathematical modelling of the process has been recognized as a desirable approach for designing mill equipment and ensuring productivity and service quality. However, such an analysis is generally very complex and time-consuming. Thus, there is considerable interest in developing simple and effective techniques to obtain accurate rolling force, torque, and slab temperatures. In this paper, a neural network (NN) is used to model the roughing stage of a plate hot rolling process. The NN approach can predict steady-state values of the force, torque, and slab temperature. The proposed NN model is compared with the classical empirical models commonly used in industrial practice. Experimental data obtained from the Ereğli Iron and Steel Factory in Turkey was used in developing the models.

Keywords

hot rolling process experimental modelling neural networks non-linear identification

References

Sims

R. B.

, ‘The calculation of roll force and torque in hot rolling mills’ Proc. Instn Mech. Engrs 168 (6) (1954): 191–200.

Orowan

, ‘The calculation of roll pressure in hot and cold flat rolling’ Proc. Instn. Mech. Engrs 150 (4) (1943): 140–167.

Hitchcock

J. H.

, ‘Elastic deformation of roll during cold rolling’ Report of Special Research Committee on Roll Neck Bearings (New York: ASME Research Publication 1935): Pp. 33–41.

Ford

Alexander

J. M.

, ‘Simplified hot rolling calculations’ J. Inst. Met. 92 (1964): 397–404.

Barnett

M. R.

Jonas

J. J.

, ‘Influence of ferrite rolling temperature on grain size and texture in annealed low carbon steels’ ISIJ Int. 37 (7) (1997): 706–714.

Kirihata

Jr Siciliano

Maccagno

T. M.

Jonas

J. J.

, ‘Mathematical modelling of rolling of multiply-alloyed mean flow stress during medium carbon steels’ ISIJ Int. 38 (2) (1998): 187–195.

Kwak

W. J.

Kim

Y. H.

Park

H. D.

Lee

J. H.

Hwang

S. M.

, ‘Fe-based on-line model for the prediction of roll force and roll power in hot strip rolling’ ISIJ Int. 40 (10) (2000): 1013–1018.

Sungzoon

Cho

Yoon

, ‘Reliable roll force prediction in cold mill using multiple neural networks’ IEEE Trans. Neural Netw. 8 (1997): 874–882.

Hagan

M. T.

Menhaj

, ‘Training feed forward networks with the Marquardt algorithm’ IEEE Trans. Neural Netw. 5 (6) (1994): 989–993.

10.

Lee

D. M.

Lee

, ‘Application of neural-network for improving accuracy of roll force model in hot-rolling mill’ Control Engng Pract. 10 (2) (2002): 473–478.

11.

Wang

Liu

Wang

Zhao

Yuan

, ‘Application of ANN in combination with mathematical models in prediction of rolling load of the finishing stands in hsm’ In Proceedings of the seventh International Conference on Steel Rolling, Chiba, Japan, 1998, 206–209.

12.

Nishino

Narazaki

Kitamura

Morimoto

Ohe

, ‘An adaptive approach to improve the accuracy of a rolling load prediction model for a plate rolling process’ ISIJ Int. 40 (12) (2000): 1216–1222.

13.

Takahashi

, ‘State of the art in hot rolling process control: Review’ Control Engng Pract. 9 (2001): 987–993.

14.

Gorni

A. A.

, ‘Application of artificial neural networks in the modeling of plate mill processes’ JOM-e 49 (4) (April 1997): 252–260.

15.

Poliak

E. I.

Shim

M. K.

Kim

G. S.

Choo

W. Y.

, ‘Application of linear regression analysis in accuracy assessment of rolling force calculations’ Met. Mater. 4 (1998): 1047–1056.

16.

Portmann

N. F.

Lindhoff

Sorgel

Gramckow

, ‘Application of neural networks in rolling mill automation’ Iron Steel Engr. 72 (2) (1995): 33–36.

17.

Lee

D. M.

Choi

S. G.

, ‘Application of on-line adaptable neural network for the rolling force set-up of a plate mill’ Engng Appl. Artif. Intell. 17 (2004): 557–565.

18.

Son

J. S.

Lee

D. M.

Kim

I. S.

Choi

S. G.

, ‘A study on on-line learning neural network for prediction for rolling force in hot-rolling mill’ J. Mater. Process. Technol. 164–165 (2005): 1612–1617.

19.

Pichler

Pffaffermayr

, ‘Neural networks for on-line optimisation of the rolling process’ Iron Steel Rev. (August 1996): 45–56.

20.

Duemmler

Nitsche

H. J.

Sesselmann

, ‘Not much artificial about artificial intelligence—artificial intelligence in flat product mini steel mills increases productivity and product quality’ Siemens Newslet. Metal., Mining More (03/1997): 1–6.

21.

–zsoy

Ruddle

E. D.

Crawley

A. F.

, ‘Optimum scheduling of a hot rolling process by nonlinear programming’ Can. Metall. Q. 31 (3) (1992): 217–224.

22.

Tarokh

Seredynski

, ‘Roll force estimation in plate rolling’ J. Iron Steel Inst. 208 (1970): 694.

23.

Schultz

R. G.

Smith

A. W.

, ‘Determination of a mathematical model for rolling mill control’ Iron Steel Engr. 80 (1965): 127–133.

24.

Lopresti

P. V.

Patton

T. N.

, ‘An optimal closed loop control of a rolling mill’ In Proceedings of the Joint Automatic Control Conference, New York 1967, pp. 767–777.

25.

Cybenko

, ‘Approximation by superposition of a sigmoidal function’ Math. Control, Signals Syst. 2 (1989): 492–499.

26.

Babuska

, Fuzzy modeling for control (Boston, MA: Kluwer 1998).

27.

Arahal

M. R.

Berenguel

Camacho

E. F.

, ‘Neural identification applied to predictive control of a solar plant’ Control Engng Pract. 6 (1998): 333–344.

28.

Gomm

J. B.

Evans

J. T.

Williams

, ‘Development and performance of a neural-network predictive controller’ Control Engng Pract. 5 (1) (1997): 49–59.

29.

Chen

Billings

S. A.

Grant

P. M.

, ‘Non-linear system identification using neural networks’ Int. J. Control 51 (1990): 1191–1214.