Transactions of Materials Processing (소성∙가공)

The Korean Society for Technology of Plasticity and materials processing (한국소성∙가공학회)
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Prediction of Ski-Effect in Plate Rolling Process using Neural Network Algorithm

후판 압연에서 신경망 알고리즘을 이용한 스키 예측

  • Received : 2013.04.24
  • Accepted : 2013.07.23
  • Published : 2013.08.01
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Abstract

A series of finite element analyses of the rolling process were performed and a neural network algorithm was employed to calculate the amount of ski-effect for an arbitrary thickness of incoming material in the roll gap. Pilot hot plate rolling tests were also conducted to verify the usefulness of the finite element analyzes conducted in this study. In these experiments, plates with thicknesses varying from 25 to 65 mm were tested. In addition, a number of rolling reductions of up to 31% were examined. Finally, a number of circumferential upper and lower rolls were investigated. Experimental validations demonstrated that the neural network algorithm predicted the proper amount of ski when rolling conditions(material thickness, reduction ratio, roll velocity differential) changed arbitrarily.

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References

  1. M. Yoshii, K. ohmori, T. seto, H. nikaido, H. nishizaki, M. Inoue, 1991, Analysis of Warping Phenomenon In Plate Rolling, ISIJ Int., Vol. 31, No. 9, pp. 973-978. https://doi.org/10.2355/isijinternational.31.973
  2. J. S. Lu, O. K. Harrer, W. Schwenzfeier, F. D. Fischer, 2000, Analysis of the Bending of the Rolling Material in Asymmetrical Sheet Rolling, Int. J. Mech. Sci.,Vol. 42, No. 1, pp. 49-61. https://doi.org/10.1016/S0020-7403(98)00112-X
  3. M. Salimi, F. Sassani , 2002, Modified Slab Analysis of Asymmetrical Plate Rolling, Int. J. Mech. Sci.,Vol. 44, No. 9, pp. 1999-2023. https://doi.org/10.1016/S0020-7403(02)00043-7
  4. M. Philipp, W. Schwenzfeier, F. D. Fischer, R. Wodlinger, C. Fischer, 2007, Front end Bending in Plate Rolling Influenced by Circumferential Speed Mismatch and Geometry, J. Mater. Process. Technol., Vol. 184, No. 1-3, pp. 224-232. https://doi.org/10.1016/j.jmatprotec.2006.11.027
  5. A. Nilsson, 2007, Front-end Bending in Plate Rolling, Scand. J. Metall., Vol. 30, No. 5, pp. 337-344.
  6. M. Bagheripoor, H. Bisadi , 2012, Application of Artificial Neural Networks for the Prediction of Roll Force and Roll Torque in Hot Strip Rolling Process, Appl. Math. Modell., Vol. 37, No. 7, pp. 4593-4607.
  7. D. E Rumelhart, G. E. Hinton, R. J. Williams, 1986, Learning Representations by Back-propagating Errors, Nature, Vol. 323, No. 9, pp. 533-536. https://doi.org/10.1038/323533a0
  8. C. O. Hlady, J. K. Brimacombe, I. V. Samarasekera, E. B. Hawbolt, 1995, Heat Transfer in the Hot Rolling of Metals, Metall. Mater. Trans. B, Vol. 26, No. 5, pp. 1019-1028. https://doi.org/10.1007/BF02654104
  9. J. G. Lenard, Z. Malinowski, M. Pietrzyk, 1992, Comparison of the Predictive Capabilities of Mathematical Models of the Flat Rolling Process, J. Mater. Process. Technol., Vol. 34, No. 1-4, pp. 85-92. https://doi.org/10.1016/0924-0136(92)90093-8