Comparison of empirical and neural network hot-rolling process models

E Oznergiz, C Ozsoy I Delice, and A KuralJed GoodellSeptember 9th,2009

Introduction

A fast, reliable, and accurate mathematical model is needed to predict the rolling force, torque and exit temperature in the rolling process.

Function of Paper: To propose an adaptable neural network model for a rolling mill

Why important?

Neural Network?

An Artificial Neural Network is a computer model designed to simulate the behavior of biological neural networks, as in pattern recognition, language processing, and problem solving, with the goal of self-directed information processing.

Introduction - References

1 Sims, R. B. The calculation of roll force and torque inhot rolling mills. Proc. Instn Mech. Engrs, 1954, 168(6),191–200.

2 Orowan, E. The calculation of roll pressure in hot andcold flat rolling. Proc. Instn. Mech. Engrs, 1943, 150(4),140–167.

3 Hitchcock, J. H. Elastic deformation of roll duringcold rolling. Report of Special Research Committee onRoll Neck Bearings, 1935, pp. 33–41 (ASME ResearchPublication, New York).

4 Ford, H. and Alexander, J. M. Simplified hot rollingcalculations. J. Inst. Met., 1964, 92, 397–404.

5 Barnett, M. R. and Jonas, J. J. Influence of ferrite rollingtemperature on grain size and texture in annealed lowcarbon steels. ISIJ Int., 1997, 37(7), 706–714.6 Kirihata, A., Siciliano, Jr, F., Maccagno, T. M.,and Jonas, J. J. Mathematical modelling of rolling ofmultiply-alloyed mean flow stress during mediumcarbon steels. ISIJ Int., 1998, 38(2), 187–195.

7 Kwak, W. J., Kim, Y. H., Park, H. D., Lee, J. H., andHwang, S. M. Fe-based on-line model for the predictionof roll force and roll power in hot strip rolling. ISIJ Int.,2000, 40(10), 1013–1018.

8 Sungzoon, C., Cho, Y., and Yoon, S. Reliable roll forceprediction in cold mill using multiple neural networks.IEEE Trans. Neural Netw., 1997, 8, 874–882.

9 Hagan, M. T. and Menhaj, M. Training feed forwardnetworks with the Marquardt algorithm. IEEE Trans.Neural Netw., 1994, 5(6), 989–993.

10 Lee, D. M. and Lee, Y. Application of neural-networkfor

improving accuracy of roll force model in hot-rolling mill. Control Engng Pract., 2002, 10(2), 473–478.

11 Lu, C., Wang, X., Liu, X., Wang, G., Zhao, K., and Yuan, J.Application of ANN in combination with mathematicalmodels in prediction of rolling load of the finishingstands in hsm. In Proceedings of the seventh InternationalConference on Steel Rolling, Chiba, Japan, 1998,206–209.

12 Nishino, S., Narazaki, H., Kitamura, A., Morimoto, Y.,and Ohe, K. An adaptive approach to improve theaccuracy of a rolling load prediction model for a platerolling process. ISIJ Int., 2000, 40(12), 1216–1222.

13 Takahashi, R. State of the art in hot rolling processcontrol: review. Control Engng Pract., 2001, 9, 987–993.

14 Gorni, A. A. Application of artificial neural networks inthe modeling of plate mill processes. JOM-e, 49(4), April1997, 252–260.

15 Poliak, E. I., Shim, M. K., Kim, G. S., and Choo, W. Y.Application of linear regression analysis in accuracyassessment of rolling force calculations. Met. Mater.,1998, 4, 1047–1056.

16 Portmann, N. F., Lindhoff, D., Sorgel, G., andGramckow, O. Application of neural networks in rollingmill automation. Iron Steel Engr., 1995, 72(2), 33–36.

17 Lee, D. M. and Choi, S. G. Application of on-lineadaptable neural network for the rolling force set-up ofa plate mill. Engng Appl. Artif. Intell., 2004, 17, 557–565.

18 Son, J. S., Lee, D. M., Kim, I. S., and Choi, S. G. A studyon on-line learning neural network for prediction forrolling force in hot-rolling mill. J. Mater. Process.Technol., 2005, 164–165, 1612–1617.

19 Pichler, R. and Pffaffermayr, M. Neural networks foron-line optimisation of the rolling process. Iron SteelRev., August 1996, 45–56.

20 Duemmler, A., Nitsche, H. J., and Sesselmann, R. Notmuch artificial about artificial intelligence – artificialintelligence in flat product mini steel mills increasesproductivity and product quality. Siemens Newslet.Metal., Mining More, 03/1997, 1–6.

21 O¨ zsoy, C., Ruddle, E. D., and Crawley, A. F. Optimumscheduling of a hot rolling process by nonlinearprogramming. Can. Metall. Q., 1992, 31(3), 217–224.

22 Tarokh, M. and Seredynski, F. Roll force estimation inplate rolling. J. Iron Steel Inst., 1970, 208, 694.

23 Schultz, R. G. and Smith, A. W. Determination of amathematical model for rolling mill control. Iron SteelEngr., 1965, 80, 127–133.

24 Lopresti, P. V. and Patton, T. N. An optimal closedloop control of a rolling mill. In Proceedings of theJoint Automatic Control Conference, New York 1967,pp. 767–777.

25 Cybenko, G. Approximation by superposition of asigmoidal function. Math. Control, Signals Syst., 1989, 2,492–499.

26 Babuska, R. Fuzzy modeling for control, 1998 (Kluwer, Boston, MA).

27 Arahal, M. R., Berenguel, M., and Camacho, E. F.Neural identification applied to predictive control of asolar plant. Control Engng Pract., 1998, 6, 333–344.

28 Gomm, J. B., Evans, J. T., and Williams, D. Development and performance of a neural-network predictive controller. Control Engng Pract., 1997, 5(1), 49–59.

Relevance to Course

The paper shows an effective way to compute the needed rolling force, torque and temperature needed for hot rolling

Design Principles

1. Empirical Model 2. Lookup tables3. Neural Network

Empirical vs NN

Design parameters

Outputs: Rolling force Torque Exit Temperature

Design principles: Empirical model

Design Principles: Neural Network

MISO System– Multi Input Single Output

Back Propagation Algorithm

To find Force and Torque:Inputs: Roll radius, number of revolutions, entry slab temperature, entry and exit thickness. Output: Force and Torque

To find Exit TemperatureInputs: Energy required, exit thickness, radius, number of revolutions, entry slab temperature, slab width, slab volume. Output: Exit Temperature

Machines

Hot rolling mill at Eregli Iron and Steel Factory in Turkey.

The equipment: Slab furnace Pre-rolling mill Reversible mill Seven strip rolling stands Cooling system Shearing System Data Acquisition and Computer control system

Experimental Equipment

Dimensions monitored during each pass by an X-ray

Temperature monitored with pyrometer

Roll force and torque monitored using four load cells placed along the mill

Empirical Results

Neural Network Results

Results between models

NN model was 22 % better predictor for force, 24% better for torque, and 14 % better for exit temperature

Errors decreased by 85% for force, 97% for torque, and 92% for temperature

Conclusions

Practical use – faster rolling, reduction in energy , more flatness control

Simple learning method vs Adaptable NN

Offline vs Online – weight update

Industries most impacted – any industry using sheet metal