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Comparison of empirical and neural network hot-rolling process models Comparison of empirical and neural network hot-rolling process models

Comparison of empirical and neural network hot-rolling process models - PowerPoint Presentation

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Comparison of empirical and neural network hot-rolling process models - PPT Presentation

E Oznergiz C Ozsoy I Delice and A Kural Jed Goodell September 9 th 2009 Introduction A fast reliable and accurate mathematical model is needed to predict the rolling force torque and exit temperature in the rolling process ID: 673501

neural rolling control force rolling neural force control mill hot roll network model temperature torque exit application steel process

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Slide1

Comparison of empirical and neural network hot-rolling process models

E

Oznergiz

, C

Ozsoy

I

Delice

, and A

Kural

Jed Goodell

September 9

th

,2009Slide2

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? Slide3

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.Slide4

Introduction - References

1

Sims, R. B. The calculation of roll force and torque in

hot 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. 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

, C.,

Ruddle

, E. D., and Crawley, A. F. Optimum

scheduling of a hot rolling process by nonlinear

programming. Can. Metall. Q., 1992, 31(3), 217–224.

22

Tarokh

, M. and

Seredynski

, F. Roll force estimation in

plate rolling. J. Iron Steel Inst., 1970, 208, 694.

23

Schultz, R. G. and Smith, A. W. Determination of a

mathematical model for rolling mill control. Iron Steel

Engr., 1965, 80, 127–133.

24

Lopresti

, P. V. and 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

, G. Approximation by superposition of a

sigmoidal

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 a

solar 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.Slide5

Relevance to Course

The paper shows

an effective

way to compute the needed

rolling force

, torque and temperature needed for hot rollingSlide6

Design Principles

Empirical Model

Lookup tables

Neural

Network

Empirical

vs

NNSlide7

Design parameters

Outputs

:

Rolling force

Torque

Exit TemperatureSlide8

Design principles: Empirical modelSlide9

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 TemperatureSlide10

Machines

Hot rolling mill at

Eregli

Iron and Steel Factory in Turkey.

The equipment:

Slab furnace

Pre-rolling mill

Reversible mill

Seven strip rolling standsCooling systemShearing SystemData Acquisition and Computer control systemSlide11

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 millSlide12

Empirical ResultsSlide13

Neural Network ResultsSlide14

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 temperatureSlide15

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