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@ARTICLE{Iraki:1026960,
      author       = {Iraki, Tarek and Morand, Lukas and Link, Norbert and
                      Sandfeld, Stefan and Helm, Dirk},
      title        = {{A}ccurate distances measures and machine learning of the
                      texture-property relation for crystallographic textures
                      represented by one-point statistics},
      journal      = {Modelling and simulation in materials science and
                      engineering},
      volume       = {32},
      number       = {5},
      issn         = {0965-0393},
      address      = {Bristol},
      publisher    = {IOP Publ.},
      reportid     = {FZJ-2024-03539},
      pages        = {055016 -},
      year         = {2024},
      abstract     = {The crystallographic texture of metallic materials is a key
                      microstructural feature that is responsible for the
                      anisotropic behavior, e.g. important in forming operations.
                      In materials science, crystallographic texture is commonly
                      described by the orientation distribution function, which is
                      defined as the probability density function of the
                      orientations of the monocrystal grains conforming a
                      polycrystalline material. For representing the orientation
                      distribution function, there are several approaches such as
                      using generalized spherical harmonics, orientation
                      histograms, and pole figure images. Measuring distances
                      between crystallographic textures is essential for any task
                      that requires assessing texture similarities, e.g. to guide
                      forming processes. Therefore, we introduce novel distance
                      measures based on (i) the Earth Movers Distance that takes
                      into account local distance information encoded in
                      histogram-based texture representations and (ii) a distance
                      measure based on pole figure images. For this purpose, we
                      evaluate and compare existing distance measures for selected
                      use-cases. The present study gives insights into advantages
                      and drawbacks of using certain texture representations and
                      distance measures with emphasis on applications in materials
                      design and optimal process control.},
      cin          = {IAS-9},
      ddc          = {530},
      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:001236131100001},
      doi          = {10.1088/1361-651X/ad4c81},
      url          = {https://juser.fz-juelich.de/record/1026960},
}