Neural network design with combined backpropagation and creepingrandom- search learning algorithms applied to the determination of retained austenite in TRIP steels

Authors

  • I. Toda-Caraballo Grupo Materalia - Dep.Metalurgia Física. Centro Nacional de Investigaciones Metalúrgicas (CENIM). Consejo Superior de Investigaciones Científicas (CSIC)
  • C. Garcia-Mateo Grupo Materalia - Dep.Metalurgia Física. Centro Nacional de Investigaciones Metalúrgicas (CENIM). Consejo Superior de Investigaciones Científicas (CSIC)
  • C. Capdevila Grupo Materalia - Dep.Metalurgia Física. Centro Nacional de Investigaciones Metalúrgicas (CENIM). Consejo Superior de Investigaciones Científicas (CSIC)
  • C. Capdevila Grupo Materalia - Dep.Metalurgia Física. Centro Nacional de Investigaciones Metalúrgicas (CENIM). Consejo Superior de Investigaciones Científicas (CSIC)

DOI:

https://doi.org/10.3989/revmetalmadrid.0924

Keywords:

Neural Networks, Creeping-random-search, Back propagation algorithm, Retained austenite, TRIP

Abstract


At the beginning of the decade of the nineties, the industrial interest for TRIP steels leads to a significant increase of the investigation and application in this field. In this work, the flexibility of neural networks for the modelling of complex properties is used to tackle the problem of determining the retained austenite content in TRIP-steel. Applying a combination of two learning algorithms (backpropagation and creeping-random-search) for the neural network, a model has been created that enables the prediction of retained austenite in low-Si / low-Al multiphase steels as a function of processing parameters.

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Published

2010-12-30

How to Cite

Toda-Caraballo, I., Garcia-Mateo, C., Capdevila, C., & Capdevila, C. (2010). Neural network design with combined backpropagation and creepingrandom- search learning algorithms applied to the determination of retained austenite in TRIP steels. Revista De Metalurgia, 46(6), 499–510. https://doi.org/10.3989/revmetalmadrid.0924

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