Detecting Defects of Steel Slabs Using Symbolic Regression

  • Petr Gajdoš
  • Jan Platoš
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 188)

Abstract

The quality of products of heavy industries plays an important role because of further usage of such products, e.g. bad quality of steel ingots can lead to a poor quality of metal plates and following wastrels in such processes, where these metal plates are consumed. Of course, single and relatively small mistake at the beginning of a complex process of product manufacturing can lead to great finance losses. This article describes a method of defects detection and quality prediction of steel slabs, which is based on soft-computing methods. The proposed method helps us to identify possible defects of slabs still in the process of their manufacturing. Experiment with real data illustrates applicability of the method.

Keywords

Quality prediction Symbolic Regression Data Analysis 

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References

  1. 1.
    Fang, H., Ross, P., Corne, D.: Genetic algorithms for timetabling and scheduling (1994), http://www.asap.cs.nott.ac.uk/ASAP/ttg/resources.html
  2. 2.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley (1989)Google Scholar
  3. 3.
    Huang, C., Li, G., Xu, Z., Yu, A., Chang, L.: Design of optimal digital lattice filter structures based on genetic algorithm. Signal Processing 92(4), 989–998 (2012)CrossRefGoogle Scholar
  4. 4.
    Ishibuchi, H., Nakashima, Y., Nojima, Y.: Performance evaluation of evolutionary multiobjective optimization algorithms for multiobjective fuzzy genetics-based machine learning. Soft. Comput. 15(12), 2415–2434 (2011)CrossRefGoogle Scholar
  5. 5.
    Juzoji, H., Nakajima, I., Kitano, T.: A development of network topology of wireless packet communications for disaster situation with genetic algorithms or with dijkstra’s. In: ICC, pp. 1–5 (2011)Google Scholar
  6. 6.
    Melanie, M.: An Introduction to Genetic Algorithms. A Bradford Book. MIT Press (1999)Google Scholar
  7. 7.
    Melanie, M., Forrest, S.: Genetic algorithms and artificial life. Santa Fe Institute, working Paper 93-11-072 (1994)Google Scholar
  8. 8.
    Pan, S.-T.: A canonic-signed-digit coded genetic algorithm for designing finite impulse response digital filter. Digital Signal Processing 20(2), 314–327 (2010)CrossRefGoogle Scholar
  9. 9.
    Park, B.J., Choi, H.R.: A genetic algorithm for integration of process planning and scheduling in a job shop. In: Australian Conference on Artificial Intelligence, pp. 647–657 (2006)Google Scholar
  10. 10.
    Sedighi, K.H., Manikas, T.W., Ashenayi, K., Wainwright, R.L.: A genetic algorithm for autonomous navigation using variable-monotone paths. I. J. Robotics and Automation 24(4) (2009)Google Scholar
  11. 11.
    Tsang, E.P.K., Warwick, T.: Applying genetic algorithms to constraints satisfaction optimization problems. In: Proc. of 9th European Conf. on AI, Aiello L.C. (1990)Google Scholar
  12. 12.
    Wainwright, R.L.: Introduction to genetic algorithms theory and applications. In: The Seventh Oklahoma Symposium on Artificial Intelligence (November 1993)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Petr Gajdoš
    • 1
    • 2
  • Jan Platoš
    • 1
    • 2
  1. 1.Department of Computer Science, FEECSVŠB – Technical University of OstravaOstravaCzech Republic
  2. 2.IT4Innovations, Center of ExcellenceVŠB – Technical University of OstravaOstravaCzech Republic

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