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Optimum Work Roll Profile Selection in the Hot Rolling of Wide Steel Strip Using Computational Intelligence

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Computational Intelligence (Fuzzy Days 1999)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1625))

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Abstract

The finishing train of a hot strip mill has been modelled by using a constant volume element model. The accuracy of the model has been increased by using an Artificial Neural Network (ANN). A non-linear Rank Based Genetic Algorithm has been developed for the optimization of the work roll profiles in the finishing stands of the simulated hot strip mill. It has been compared with eight other experimental optimization algorithms: Random Walk, Hill Climbing, Simulated Annealing (SA) and five different Genetic Algorithms (GA). Finally, the work roll profiles have been optimized by the non-linear Rank Based Genetic Algorithm. The quality of the strip from the simulated mill was significantly improved.

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References

  1. Wilms, Vogtmann, Klöckner, Beisemann, Rohde: Steuerung von Profil und Planheit in Warmbreitbandstraßen, Stahl u. Eisen 105 (1985) Nr. 22, pp 1181–1190

    Google Scholar 

  2. Hensel, Poluchin: Technologie der Metallformung, Deutscher Verlag für Grundstoffindustrie, Leipzig, 1990

    Google Scholar 

  3. Sheppard, Roberts: Shape Control and Correction in Strip and Sheet, International Metallurgical Reviews 1973, Vol. 8, pp 1–17

    Google Scholar 

  4. Emicke, Lucas: Einflüße auf die Walzgenauigkeit beim Warmwalzen von Blechen und Bändern, Neue Hütte, 1. Jg. Heft 5, 1956, pp 257–274

    Google Scholar 

  5. Takashi, Satou, Yabuta: Adaptive Technology for Thickness Control of Finisher Set-up on Hot Strip Mill, ISIJ International, Vol. 35 (1995), No. 1, pp 42–49

    Google Scholar 

  6. Ortmann, Burkhard: Modernisierung der Automatisierungssysteme der Warmbreitbandstraße bei Voest-Alpine Stahl Linz, Stahl u. Eisen 115 (1995) Nr.2, pp 35–40

    Google Scholar 

  7. Nolle, Armstrong, Ware: Optimisation of Work Roll Profiles in a 2-hight Rolling Mill Stand by Means of Computational Intelligence, Proc. of the 4th Int. Mendel Conf, Brno, 1998, pp 277–284

    Google Scholar 

  8. Hensel, Arnold, Thilo Spittel: Kraft-und Arbeitsbedarf bildsamer Formgebungsverfahren, VEB Deutscher Verlag für Grundstoffindustrie, Leipzig, 1978

    Google Scholar 

  9. Winkler W.: Theoretische Untersuchungen über die Einflüße, welche die Stärkenunterschiede beim Warmwalzen von Breitstreifen bestimmen, Dr.-Ing. Dissertation, TH Achen, 1943

    Google Scholar 

  10. Wusatowski, Zygmunt: Fundamentals of Rollling, Pergamon Press Ltd, Oxford, London, Edinburgh, 1969

    Google Scholar 

  11. Portmann, Lindhoff, Sorgel, Gramckow: Application of neural networks in rolling mill automation, Iron and Steel Engineer, Feb. 1995, pp 33–36

    Google Scholar 

  12. Hornik Stinchcombe, White: Multilayer Feedforward Networks are Universal Approximators, Neural Networks, Vol. 2, 1989, pp 359–366

    Article  Google Scholar 

  13. Hornik: Approximation Capabilities of Multilayer Feedforward Networks, Neural Networks, Vol. 4, 1991, pp 251–257

    Article  Google Scholar 

  14. Picton, Phil: Introduction to Neural Networks, The MacMillian Press LTD, 1994

    Google Scholar 

  15. Sprecher, D.: On the Structure of Continuous Functions of Several Variables, Transaction of the American Mathematical Society, Vol. 115, 1964, pp 340–355

    Article  MathSciNet  Google Scholar 

  16. Baker: Adaptive Selection Methods for Genetic Algorithms, Proc. on an Int. Conf. on GA’s and Their Application, Hilldsdale, 1985, pp 101–111

    Google Scholar 

  17. Kirkpatrick, S., C.D. Gelatt, Jr., M. P. Vecchi: Optimization by Simulated Annealing, Science, 13 May 1983, Vol. 220, No. 4598

    Google Scholar 

  18. Metropolis, A, W. Rosenbluth, M. N. Rosenbluth, H. Teller, E. Teller: Equation of State Calculations by Fast Computing Machines, The Journal of Chemical Physics, Vol. 21, No. 6, June 1953

    Google Scholar 

  19. Syswerda: Uniform Crossover in Genetic Algorithms, Proceedings of International Conference on Genetic Algorithm 1989 (ICGA’89), pp 2–9

    Google Scholar 

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Author information

Authors and Affiliations

  1. Faculty of Technology, The Open University in Wales, Cardiff, CF1 9SA, UK

    Lars Nolle, Alun Armstrong & Adrian Hopgood

  2. School of Accounting and Mathematics, University of Glamorgan, Pontypridd, CF37 1DL, UK

    Andrew Ware

Authors
  1. Lars Nolle
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  2. Alun Armstrong
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  3. Adrian Hopgood
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  4. Andrew Ware
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Editor information

Editors and Affiliations

  1. Computer Science I, University of Dortmund, D-44221, Dortmund, Germany

    Bernd Reusch

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© 1999 Springer-Verlag Berlin Heidelberg

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Nolle, L., Armstrong, A., Hopgood, A., Ware, A. (1999). Optimum Work Roll Profile Selection in the Hot Rolling of Wide Steel Strip Using Computational Intelligence. In: Reusch, B. (eds) Computational Intelligence. Fuzzy Days 1999. Lecture Notes in Computer Science, vol 1625. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48774-3_50

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  • DOI: https://doi.org/10.1007/3-540-48774-3_50

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66050-7

  • Online ISBN: 978-3-540-48774-6

  • eBook Packages: Springer Book Archive

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