Microstructural evolution and constitutive models to predict hot deformation behaviors of a nickel-based superalloy

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  author =       "Y. C. Lin and Fu-Qi Nong and Xiao-Min Chen and 
                 Dong-Dong Chen and Ming-Song Chen",
  title =        "Microstructural evolution and constitutive models to
                 predict hot deformation behaviors of a nickel-based
  journal =      "Vacuum",
  volume =       "137",
  pages =        "104--114",
  year =         "2017",
  ISSN =         "0042-207X",
  DOI =          "doi:10.1016/j.vacuum.2016.12.022",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0042207X16308041",
  abstract =     "To investigate the hot deformation behaviors of a
                 nickel-based superalloy, the hot compressive tests are
                 conducted at the deformation temperature range of
                 920-1040 degreeC and strain rate range of 0.001-1s-1.
                 It is found that the effects of strain rate and
                 deformation temperature on the grain boundary maps are
                 significant. An almost competed dynamic
                 recrystallization (DRX) microstructure occurs at
                 relatively low strain rates. However, the increased
                 strain rate easily leads to the uneven microstructures.
                 The DRX degree notably increases with the increase of
                 deformation temperature, because the high temperature
                 enhances the grain boundary migration mobility and
                 facilitates the nucleation and growth of DRX grains.
                 Based on the experimental results, multi-gene genetic
                 programming (MGGP), artificial neural network (ANN) and
                 Arrhenius type phenomenological models are established
                 to predict the flow stress. Due to the obvious
                 over-fitting problem of MGGP model, a Hannan-Quinn
                 information criterion based MGGP (HQC-MGGP) approach is
                 proposed. The performances of MGGP, HQC-MGGP, ANN and
                 phenomenological models are compared. It is found that
                 HQC-MGGP model has the best performance to predict the
                 flow stress under the experimental conditions.
                 Therefore, HQC-MGGP model is accurate and reliable in
                 describing the hot deformation behaviors of the studied
                 nickel-based superalloy.",
  keywords =     "genetic algorithms, genetic programming, Alloy, Hot
                 deformation, Grain boundary map, Constitutive model",

Genetic Programming entries for Y C Lin Fu-Qi Nong Xiao-Min Chen Dong-Dong Chen Ming-Song Chen