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@ARTICLE{Fehlemann:999141,
      author       = {Fehlemann, Niklas and Aguilera, Ana Lia Suarez and
                      Sandfeld, Stefan and Bexter, Felix and Neite, Maximilian and
                      Lenz, David and Könemann, Markus and Münstermann,
                      Sebastian},
      title        = {{I}dentification of martensite bands in dual phase steels
                      – a deep learning object detection approach using {F}aster
                      {R}‐{CNN}},
      journal      = {Steel research international},
      volume       = {94},
      number       = {7},
      issn         = {1611-3683},
      address      = {Weinheim},
      publisher    = {Wiley-VCH-Verl.},
      reportid     = {FZJ-2023-01191},
      pages        = {2200836},
      year         = {2023},
      abstract     = {Martensite banding in dual phase steels is an important
                      research topic in the field of materials design, since it is
                      affecting the local damage properties of the material to a
                      large extent. Therefore, it is necessary to quantify the
                      amount of banding and the geometrical details of the bands
                      in a specific microstructure, for example for simulative
                      approaches. In this study, an object detection approach is
                      used to separate martensite bands from the rest of the
                      microstructure and a subsequent effort is made to transfer
                      these results to statistical quantities for the generation
                      of representative volume elements (RVE). For this, a
                      convolutional neural network (Faster R-CNN) was trained on
                      manually labeled SEM-images of DP800 steel. As exact
                      geometric definitions of martensite bands in such
                      two-dimensional images are difficult, the influence of
                      different band definitions was investigated. The result of
                      the training shows generally good prediction accuracy but is
                      strongly dependent on the chosen band definition and the
                      underlying human bias from the labeling process. A
                      statistical analysis using cross-validation additionally
                      shows that reliable results can already be achieved with
                      only small datasets of around 50 to 100 training images due
                      to the used transfer learning approach. This is an important
                      outcome as it eliminates the need to generate an enormously
                      large dataset which can only be obtained from very time
                      consuming microscopy work and manual labeling of the
                      images.},
      cin          = {IAS-9},
      ddc          = {660},
      cid          = {I:(DE-Juel1)IAS-9-20201008},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511)},
      pid          = {G:(DE-HGF)POF4-5111},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000943084800001},
      doi          = {10.1002/srin.202200836},
      url          = {https://juser.fz-juelich.de/record/999141},
}