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Comparison of empirical and neural network hot-rolling process models

E Oznergiz , C Ozsoy I Delice , and A Kural Jed Goodell September 9 th ,2009. Comparison of empirical and neural network hot-rolling process models. Introduction.

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Comparison of empirical and neural network hot-rolling process models

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  1. E Oznergiz, C Ozsoy I Delice, and A Kural Jed Goodell September 9th,2009 Comparison of empirical and neural network hot-rolling process models

  2. 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?

  3. 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.

  4. Introduction - References 11 Lu, C., Wang, X., Liu, X., Wang, G., Zhao, K., and Yuan, J. 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, S., Narazaki, H., Kitamura, A., Morimoto, Y., and Ohe, K. An adaptive approach to improve the accuracy of a rolling load prediction model for a plate rolling process. ISIJ Int., 2000, 40(12), 1216–1222. 13 Takahashi, R. State of the art in hot rolling process control: review. Control EngngPract., 2001, 9, 987–993. 14Gorni, 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., and Choo, W. Y. Application of linear regression analysis in accuracy assessment of rolling force calculations. Met. Mater., 1998, 4, 1047–1056. 16 Portmann, N. F., Lindhoff, D., Sorgel, G., and Gramckow, O. Application of neural networks in rolling mill automation. Iron Steel Engr., 1995, 72(2), 33–36. 17 Lee, D. M. and Choi, S. G. Application of on-line adaptable neural network for the rolling force set-up of a 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 study on on-line learning neural network for prediction for rolling force in hot-rolling mill. J. Mater. Process. Technol., 2005, 164–165, 1612–1617. 19Pichler, R. and Pffaffermayr, M. Neural networks for on-line optimisation of the rolling process. Iron Steel Rev., August 1996, 45–56. 20Duemmler, 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 O¨ 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. 22Tarokh, 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. 24Lopresti, 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. 25Cybenko, G. Approximation by superposition of a sigmoidal function. Math. Control, Signals Syst., 1989, 2, 492–499. 26Babuska, R. Fuzzy modeling for control, 1998 (Kluwer, Boston, MA). 27Arahal, M. R., Berenguel, M., and Camacho, E. F. Neural identification applied to predictive control of a solar plant. Control EngngPract., 1998, 6, 333–344. 28Gomm, J. B., Evans, J. T., and Williams, D. Development and performance of a neural-network predictive controller. Control EngngPract., 1997, 5(1), 49–59. 1 Sims, R. B. The calculation of roll force and torque in hot rolling mills. Proc. Instn Mech. Engrs, 1954, 168(6), 191–200. 2Orowan, E. The calculation of roll pressure in hot and cold flat rolling. Proc. Instn. Mech. Engrs, 1943, 150(4), 140–167. 3 Hitchcock, J. H. Elastic deformation of roll during cold rolling. Report of Special Research Committee on Roll Neck Bearings, 1935, pp. 33–41 (ASME Research Publication, New York). 4 Ford, H. and Alexander, J. M. Simplified hot rolling calculations. J. Inst. Met., 1964, 92, 397–404. 5 Barnett, M. R. and Jonas, J. J. Influence of ferrite rolling temperature on grain size and texture in annealed low carbon 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 of multiply-alloyed mean flow stress during medium carbon steels. ISIJ Int., 1998, 38(2), 187–195. 7 Kwak, W. J., Kim, Y. H., Park, H. D., Lee, J. H., and Hwang, S. M. Fe-based on-line model for the prediction of roll force and roll power in hot strip rolling. ISIJ Int., 2000, 40(10), 1013–1018. 8Sungzoon, C., Cho, Y., and Yoon, S. Reliable roll force prediction in cold mill using multiple neural networks. IEEE Trans. Neural Netw., 1997, 8, 874–882. 9 Hagan, M. T. and Menhaj, M. Training feed forward networks 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 EngngPract., 2002, 10(2), 473–478.

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

  6. Design Principles • Empirical Model • Lookup tables • Neural Network Empirical vs NN

  7. Design parameters • Outputs: • Rolling force • Torque • Exit Temperature

  8. Design principles: Empirical model

  9. 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 Temperature • Inputs: Energy required, exit thickness, radius, number of revolutions, • entry slab temperature, slab width, slab volume. • Output: Exit Temperature

  10. 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

  11. 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

  12. Empirical Results

  13. Neural Network Results

  14. 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

  15. 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

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