Modeling the Temperature of Hot Rolled Steel Plate with Semi-supervised Learning Methods

  • Henna Tiensuu
  • Ilmari Juutilainen
  • Juha Röning
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6926)


The semi-supervised learning methods utilize both the labeled and unlabeled data to produce better learners than the usual methods using only the labeled data. In this study, semi-supervised learning is applied to the modeling of the rolling temperature of steel plate. Measurement of the rolling temperature in the extreme conditions of rolling mill is difficult and thus there is a large amount of missing response measurements. Previous research mainly focuses on semi-supervised classification. Application of semi-supervised learning to regression problems is largely understudied. Co-training is a semi-supervised method, which is promising in the semi-supervised regression setting. In this paper, we used COREG algorithm [10] to a data set collected from steel plate rolling. Our results show that COREG can effectively exploit unlabeled data and improves the prediction accuracy. The achieved prediction accuracy 16°C is a major improvement in comparison to the earlier approach in which temperature is predicted using physical-mathematical models. In addition, features that describe the rolling process and are applicable to input variables of learning methods are presented. The results can be utilized to develop statistical models for temperature prediction for other rolling processes as well.


semi-supervised learning methods COREG-algorithm hot plate rolling process rolling temperature model 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Peter, B., Mansour, Y. (eds.) Proceedings of the Eleventh Annual Conference on Computational Learning Theory, pp. 92–100. ACM, New York (1998)CrossRefGoogle Scholar
  2. 2.
    Chapelle, O., Schlkopf, B., Zien, A.: Semi-Supervised Learning. The MIT Press, Cambridge (2006)CrossRefGoogle Scholar
  3. 3.
    Friedman, J.: Multivariate adaptive regression splines (with discussion). Annals of Statistics 19(1), 1–141 (1991)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Friedman, J.: Stochastic gradient boosting. Computational Statistics and Data Analysis 38(4), 367–378 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, New York (2001)CrossRefzbMATHGoogle Scholar
  6. 6.
    Kiuchi, M., Yanagimotoa, J., Wakamatsua, E.: Overall thermal analysis of hot plate/sheet rolling. CIRP Annals - Manufacturing Technology 49(1), 209–212 (2000)CrossRefGoogle Scholar
  7. 7.
    Leden, B.: Steeltemp-a program for temperature analysis in steel plants. Scandinavian Journal of Metallurgy 15, 215–223 (1986)Google Scholar
  8. 8.
    Montequn, V.R., Fernndez, F.O., de Martnez, N.A., Rodrguez, J.A.G.: Using artificial intelligence to model heavy plate mill rolling. JOM Journal of the Minerals, Metals and Materials Society 54(7), 46–50 (2002)CrossRefGoogle Scholar
  9. 9.
    Oznergiz, E., Gilez, K., Ozsoy, C.: Neural network modeling of a plate hot-rolling process and comparision with the conventional techniques. In: Chen, B.M. (ed.) Proceedings of the International Conference on Control and Automation (ICCA 2005), pp. 646–651. IEEE, USA (2005)CrossRefGoogle Scholar
  10. 10.
    Zhou, Z.-H., Li, M.: Semisupervised regression with cotraining-style algorithms. IEEE Trans. on Knowl. and Data Eng. 19, 1479–1493 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Henna Tiensuu
    • 1
  • Ilmari Juutilainen
    • 1
  • Juha Röning
    • 1
  1. 1.Computer Science and Engineering LaboratoryUniversity of OuluOuluFinland

Personalised recommendations