Journal of Materials Science

, Volume 42, Issue 14, pp 5391–5397 | Cite as

Artificial neural network modeling for the prediction of critical transformation temperatures in steels

  • Carlos Garcia-MateoEmail author
  • Carlos Capdevila
  • Francisca Garcia Caballero
  • Carlos García de Andrés


Accurate knowledge of critical transformation temperatures in steels such as the austenitizing temperature, Tγ, isothermal bainite and martensite start temperatures, BS and MS, is of unquestionable significance from an industrial and research point of view. Therefore a significant amount of work has been devoted not only in understanding the physical mechanism lying beneath those transformations, but also obtaining quantitatively accurate models. Nowadays, with modern computing systems, more rigorous and complex data analysis methods can be applied whenever required. Thus, Artificial Neural Network (ANN) analysis becomes a very attractive alternative, for being easily distributed, self-sufficient and for its ability of accompanying its predictions by an indication of their reliability.


Martensite Artificial Neural Network Bainite Artificial Neural Network Model Bayesian Framework 



The authors acknowledge financial support from the European Coal and Steel Community (ECSC agreement number 7210-PR/345) and the Spanish Ministerio de Ciencia y Tecnología (Project-MAT 2002-10812 E). C. Garcia-Mateo would like to thank Spanish Ministerio de Ciencia y Tecnología for the financial support in the form of a temporal Ramón y Cajal contract (RyC 2004 Program).


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Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Carlos Garcia-Mateo
    • 1
    Email author
  • Carlos Capdevila
    • 1
  • Francisca Garcia Caballero
    • 1
  • Carlos García de Andrés
    • 1
  1. 1.MATERALIA Research Group, Department of Physical Metallurgy, Centro Nacional de Investigaciones Metalúrgicas (CENIM)Consejo Superior de Investigaciones Científicas (CSIC)MadridSpain

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