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

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Abstract

Accurate knowledge of critical transformation temperatures in steels such as the austenitizing temperature, T γ, isothermal bainite and martensite start temperatures, B S and M S, 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.

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Acknowledgements

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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Authors and Affiliations

  1. MATERALIA Research Group, Department of Physical Metallurgy, Centro Nacional de Investigaciones Metalúrgicas (CENIM), Consejo Superior de Investigaciones Científicas (CSIC), Avda. Gregorio del Amo, 8, 28040 , Madrid, Spain

    Carlos Garcia-Mateo, Carlos Capdevila, Francisca Garcia Caballero & Carlos García de Andrés

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  1. Carlos Garcia-Mateo
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  2. Carlos Capdevila
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  3. Francisca Garcia Caballero
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  4. Carlos García de Andrés
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Correspondence to Carlos Garcia-Mateo.

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Garcia-Mateo, C., Capdevila, C., Caballero, F.G. et al. Artificial neural network modeling for the prediction of critical transformation temperatures in steels. J Mater Sci 42, 5391–5397 (2007). https://doi.org/10.1007/s10853-006-0881-2

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  • DOI: https://doi.org/10.1007/s10853-006-0881-2

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