Online Prediction of Wear on Rolls of a Bar Rolling Mill Based on Semi-Analytical Equations and Artificial Neural Networks

  • Yukio Shigaki
  • Marcos Antonio Cunha
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 223)


This paper presents a computer model for online prediction of the wear contour of grooved rolls in the round-oval-round pass rolling process based on semi-analytical equations and artificial neural networks (ANN). This wear may adversely affect the shape quality of final product and is a result of complex interactions of many variables in the rolling process. The temperature of the material, amount of rolled material, water cooling system efficiency, diameters of the rolls, rolling speed and rolling load are some of these factors that play important role when assessing the wear of the rolls. A first ANN learns the average electrical current for thousands of hot rolled billets, and is done for ideal conditions, with new rolls. A second ANN calculates empirical coefficients in order to define the spread of the workpiece and then its contour is calculated accurately. This second ANN has inputs of differences on ideal and real electrical currents (and, thus, the rolling load variation) generated from the first ANN, temperature, water cooling pressure, speed of the rolls, diameters of the rolls, etc. Then the coefficients \({\varvec{\gamma }}\) and \({\varvec{\kappa }}\) (for wear profile) are calculated and input in semi-analytical equations to define the wear and its contour, as an online prediction. The works of Shinokura and Takai (1984) and Byon and Lee (2007) apply constant values for these two coefficients, limiting its application for other operational data variation during the rolling process. The model presented in this work uses an ANN to adapt \({\varvec{\gamma }}\) and \({\varvec{\kappa }}\) to cope with this variation. More than 50,000 billets were monitored and their operational data collected. The model was tested and the results agree well in real operational situations.


Bar rolling Artificial neural networks Wear 


  1. 1.
    Oike, Y., Okubo, I., Hirano, H., Umeda, K.: Tetsu-to-Hagane 63, S222 (in Japanese) (1977)Google Scholar
  2. 2.
    Archard, J.F.: Contacts and rubbing of flat surfaces. J. Appl. Phys. 24, 981–988 (1953)CrossRefGoogle Scholar
  3. 3.
    Shinokura, T., Takai, K.A.: A new method for calculating spread in rod rolling. J. Appl. Metalworking 2, 94 (1982)CrossRefGoogle Scholar
  4. 4.
    Lee, Y., Choi, S., Kim, Y.H.: Mathematical model and experimental validation of surface profile of workpiece in round-oval-round pass sequence. J. Mater. Process. Technol. 108, 4465–4470 (2000)Google Scholar
  5. 5.
    Kim, D.H., Kim, B.M., Lee, Y.: Application of ANN for the dimensional accuracy of workpiece in hot rod rolling process. J. Mater. Process. Technol. 130–131, 214–218 (2002)CrossRefGoogle Scholar
  6. 6.
    Kim, D.H., Kim, B.M., Lee, Y.: Adjustment of roll gap for the dimension accuracy of bar in hot bar rolling process. Int. J. KSPE 4(1), 56–62 (2003)Google Scholar
  7. 7.
    Byon, S.M., Lee, Y.: Experimental and semi-analytical study of wear contour of roll groove and its applications to rod mill. ISIJ Int. 47(47), 1006–1015 (2007)CrossRefGoogle Scholar
  8. 8.
    Byon, S.M., Lee, Y.: A study of roll gap adjustment due to roll wear in groove rolling: experiment and modeling. In: Proceedings of the Institution of Mechanical Engineers, Part B: J. Eng. Manuf. 222(7), (2008)Google Scholar
  9. 9.
    Byon, S.M., Lee, Y.: Experimental study for roll gap adjustment due to roll wear in single-stand rolling and multi-stand rolling test. J. Mech. Sci. Technol. 22, 937–945 (2008)CrossRefGoogle Scholar
  10. 10.
    Zheng, J., Dong, Y.: International Conference on Mechanic automation and control engineering (MACE), 26–28, (2010)Google Scholar
  11. 11.
    Sydenham, P.H.; Thorn, R.: Handbook of measuring system design. John Wiley & Sons (2005)Google Scholar
  12. 12.
    Kewalramani, M.A., Gupta, R.: Concrete compressive strength prediction using ultrasonic pulse velocity through artificial neural networks. Autom. Constr. 15, 374–379 (2006)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.CEFET-MGFederal Center of Technological Education of Minas GeraisBelo HorizonteBrazil
  2. 2.GERDAUDivinopolis millBrazil

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