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Development of neural network-based models to predict mechanical properties of hot dip galvanised steel coils

Author

Listed:
  • Ana Gonzalez-Marcos
  • Fernando Alba-Elias
  • Manuel Castejon-Limas
  • Joaquin Ordieres-Mere

Abstract

In the industrial arena, artificial neural networks are among the most significant techniques in system modelling because of their efficiency and simplicity. In this paper, we present an application of artificial neural networks, along with other techniques stemming from data mining, to model the yield strength, tensile strength, elongation, strain hardening coefficient and the Lankford's anisotropy coefficient of galvanised steel coils, according to the manufacturing process data. In particular, we propose the use of these models to improve the current control systems of hot-dip galvanising lines since an open loop control strategy must be adopted because the mechanical properties of hot-dip galvanising coils are not directly measurable.

Suggested Citation

  • Ana Gonzalez-Marcos & Fernando Alba-Elias & Manuel Castejon-Limas & Joaquin Ordieres-Mere, 2011. "Development of neural network-based models to predict mechanical properties of hot dip galvanised steel coils," International Journal of Data Mining, Modelling and Management, Inderscience Enterprises Ltd, vol. 3(4), pages 389-405.
    • Handle: RePEc:ids:ijdmmm:v:3:y:2011:i:4:p:389-405
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