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@ARTICLE{Hter:875308,
      author       = {Hüter, Claas and Yin, X. and Vo, T. and Braun, Silvia},
      title        = {{A} pragmatic dataset augmentation approach for
                      transformation temperature prediction in steels},
      journal      = {Computational materials science},
      volume       = {176},
      issn         = {0927-0256},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2020-01939},
      pages        = {109488 -},
      year         = {2020},
      abstract     = {We introduce an augmentation approach for the prediction of
                      phase transformation temperatures that combines
                      thermodynamic considerations and thermodynamic databases.
                      Using the example of the bainitic start temperature, , we
                      demonstrate the improvement of prediction accuracy that this
                      augmentation scheme can provide. The training and testing
                      dataset available from already published experimental
                      measurements provides a varying set of alloying elements and
                      measured bainitic start temperatures. In terms of a
                      minimalistic thermodynamic model, we explain the benefit of
                      augmenting the presented data set by the chemical potential
                      of carbon in the ferritic phase at an estimated start
                      temperature. To evaluate this augmentation scheme, we
                      determine the prediction accuracy of sets of artificial
                      neural networks (ANNs) for the unaugmented dataset, for the
                      – only a posteriori accessible – dataset which is
                      augmented with the chemical potential at the measured
                      bainitic start temperature, and the prediction accuracy for
                      the dataset augmented by an estimated , approximated with
                      two different approaches. While the dataset which is
                      augmented with the chemical potential at the measured
                      bainitic start temperatures would not be practically usable
                      for the prediction of a not yet measured bainitic start
                      temperature, it provides theoretical limits of the
                      achievable accuracy gain due to the augmentation. The
                      developed approximation schemes for at are usable to predict
                      for a given composition. We distinguish two levels of
                      computational expense, which provide a mean absolute error
                      of either about 14 °C or about 4 °C, thus reaching the
                      regime of experimental measurement accuracy.},
      cin          = {IEK-2 / IBG-2-3-TA},
      ddc          = {530},
      cid          = {I:(DE-Juel1)IEK-2-20101013 /
                      I:(DE-Juel1)IBG-2-3-TA-20110204},
      pnm          = {113 - Methods and Concepts for Material Development
                      (POF3-113)},
      pid          = {G:(DE-HGF)POF3-113},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000519572500007},
      doi          = {10.1016/j.commatsci.2019.109488},
      url          = {https://juser.fz-juelich.de/record/875308},
}