Metamodel-based optimization of hot rolling processes in the metal industry

  • Christian Jung
  • Martin Zaefferer
  • Thomas Bartz-Beielstein
  • Günter Rudolph
ORIGINAL ARTICLE

Abstract

To maximize the throughput of a hot rolling mill, the number of passes has to be reduced. This can be achieved by maximizing the thickness reduction in each pass. For this purpose, exact predictions of roll force and torque are required. Hence, the predictive models that describe the physical behavior of the product have to be accurate and cover a wide range of different materials. Due to market requirements, a lot of new materials are tested and rolled. If these materials are chosen to be rolled more often, a suitable flow curve has to be established. It is not reasonable to determine those flow curves in laboratory, because of costs and time. A strong demand for quick parameter determination and the optimization of flow curve parameter with minimum costs is the logical consequence. Therefore, parameter estimation and the optimization with real data, which were collected during previous runs, is a promising idea. Producers benefit from this data-driven approach and receive a huge gain in flexibility when rolling new materials, optimizing current production, and increasing quality. This concept would also allow to optimize flow curve parameters, which have already been treated by standard methods. In this article, a new data-driven approach for predicting the physical behavior of the product and setting important parameters is presented. We demonstrate how the prediction quality of the roll force and roll torque can be optimized sustainably. This offers the opportunity to continuously increase the workload in each pass to the theoretical maximum while product quality and process stability can also be improved.

Keywords

Flowcurve Kriging Metamodel Metal Hot rolling 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Barton RR, Meckesheimer M (2006) Metamodel-based simulation optimization. In: Henderson SG, Nelson BL (eds) Simulation, handbooks in operations research and management science, vol 13. Elsevier, pp 535–574. doi:10.1016/S0927-0507(06)13018-2, http://www.sciencedirect.com/science/article/pii/S0927050706130182
  2. 2.
    Bartz-Beielstein T (2003) Experimental analysis of evolution strategies—overview and comprehensive introduction. Interner Bericht des Sonderforschungsbereichs 531 Computational Intelligence CI–157/03. Universität Dortmund, GermanyGoogle Scholar
  3. 3.
    Bartz-Beielstein T, Lasarczyk C, Preuß M. (2005) Sequential parameter optimization. In: McKay B. et al (eds) Proceedings 2005 congress on evolutionary computation (CEC’05), Edinburgh, Scotland, vol 1. IEEE Press, Piscataway, pp 773–780Google Scholar
  4. 4.
    Bartz-Beielstein T, Parsopoulos KE, Vrahatis MN (2004) Design and analysis of optimization algorithms using computational statistics. Appl Numer Anal Comput Math (ANACM) 1(2):413–433MathSciNetCrossRefMATHGoogle Scholar
  5. 5.
    Bartz-Beielstein T, Zaefferer M (2012) A gentle introduction to sequential parameter optimization. Tech. Rep. TR 01/2012. CIplusGoogle Scholar
  6. 6.
    Forrester A, Sóbester A., Keane A (2007) Multi-fidelity optimization via surrogate modelling. Proc R Soc A: Math Phys Eng Sci 463(2088):3251–3269. doi:10.1098/rspa.2007.1900 MathSciNetCrossRefMATHGoogle Scholar
  7. 7.
    Forrester A, Sobester A, Keane A (2008) Engineering design via surrogate modelling. WileyGoogle Scholar
  8. 8.
    Hajduk M, Zidek M, Elfmark J, Kopec S (1972) Derivation of mean values of inherent deformation resistance in hot rolling of tonnage steel. Hutn Listy 27(8):567Google Scholar
  9. 9.
    Hajduk M et al (1972) Effect of improper selection of the rpm of vertical and horizontal drives on balanced rolling force distribution in a universal rolling mill. Hutn Listy 27(8):259Google Scholar
  10. 10.
    Hensel A, Spittel T (1978) Kraft- und Arbeitsbedarf bildsamer Formgebungsverfahren. Verlag GrundstoffindustrieGoogle Scholar
  11. 11.
    Hernandez C, Medina S, Ruiz J (1996) Modelling austenite flow curves in low alloy and microalloyed steels. Acta Mater 44(1):155–163. doi:10.1016/1359-6454(95)00153-4. http://www.sciencedirect.com/science/article/pii/1359645495001534 CrossRefGoogle Scholar
  12. 12.
    Hinkfoth R (2003) Massivumformung. Wissenschaftsverlag, AachenGoogle Scholar
  13. 13.
    Jin Y (2005) A comprehensive survey of fitness approximation in evolutionary computation. Soft Comput 9(1):3–12CrossRefGoogle Scholar
  14. 14.
    Jones D, Schonlau M, Welch W (1998) Efficient global optimization of expensive black-box functions. J Glob Optim 13:455–492MathSciNetCrossRefMATHGoogle Scholar
  15. 15.
    Jones DR (2001) A taxonomy of global optimization methods based on response surfaces. J Glob Optim 21:345–383. doi:10.1023/A:1012771025575 MathSciNetCrossRefMATHGoogle Scholar
  16. 16.
    Kleijnen JPC (2008) Design and analysis of simulation experiments. Springer, New YorkMATHGoogle Scholar
  17. 17.
    Lambiase F (2013) Optimization of shape rolling sequences by integrated artificial intelligent techniques. Int J Adv Manuf 68(1–4):443–452CrossRefGoogle Scholar
  18. 18.
    Mancini E, Campana F, Sasso M (2012) Newaz, G.: Effects of cold rolling process variables on final surface quality of stainless steel thin strip. Int J Adv Manuf 61(1–4):63–72CrossRefGoogle Scholar
  19. 19.
    Mandal S, Rakesh V, Sivaprasad S, Venugopal S, Kasiviswanathan KV (2009) Constitutive equations to predict high temperature flow stress in a Ti-modified austenitic stainless steel. Mater Sci EngGoogle Scholar
  20. 20.
    Nelder J, Mead R (1965) A simplex method for function minimization. Comput J 7:308–313MathSciNetCrossRefMATHGoogle Scholar
  21. 21.
    Powell M (1988) A review of algorithms for nonlinear equations and unconstrained optimization. In: Proceedings ICIAM, pp 220–232Google Scholar
  22. 22.
    Powell M (1992) A direct search optimization method that models the objective and constraint functions by linear interpolation. Tech. Rep. DAMTP 1992/NA5, Department of Applied Mathematics and Theoretical Physics, University of Cambridge, EnglandGoogle Scholar
  23. 23.
    Sacks J, Welch WJ, Mitchell TJ, Wynn HP (1989) Design and analysis of computer experiments. Stat Sci 4(4):409–435MathSciNetCrossRefMATHGoogle Scholar
  24. 24.
    Sheu JJ (2004) Simulation and optimization of the cold roll-forming process. In: AIP. AIP, Melville, pp 452–457Google Scholar
  25. 25.
    Sun J, Du F, Li X (2008) FEM Simulation of the roll deformation of six-high CVC mill in cold strip rolling. In: 2008 international workshop on modelling, simulation and optimization (WMSO). IEEE, pp 412–415Google Scholar
  26. 26.
    Tselikov A, Nikitin G, Rokotyan S (1981) The theory of lengthwise rolling. Mir PublishersGoogle Scholar
  27. 27.
    Weber K (1973) Grundlagen des Bandwalzens. Leipzig, VEB Deutscher Verlag fuer GrundstoffindustrieGoogle Scholar
  28. 28.
    Lin Ming-Song YC, Chen JZ (2008) Prediction of 42crmo steel flow stress at high temperature and strain rate. Mech Res CommunGoogle Scholar

Copyright information

© Springer-Verlag London 2016

Authors and Affiliations

  • Christian Jung
    • 1
  • Martin Zaefferer
    • 1
  • Thomas Bartz-Beielstein
    • 1
  • Günter Rudolph
    • 2
  1. 1.Faculty for Computer and Engineering SciencesCologne University of Applied SciencesGummersbachGermany
  2. 2.Faculty for Computational IntelligenceTU Dortmund UniversityDortmundGermany

Personalised recommendations