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@ARTICLE{Morand:1032625,
      author       = {Morand, Lukas and Iraki, Tarek and Dornheim, Johannes and
                      Sandfeld, Stefan and Link, Norbert and Helm, Dirk},
      title        = {{M}achine learning for structure-guided materials and
                      process design},
      journal      = {Materials and design},
      volume       = {248},
      issn         = {0264-1275},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2024-06391},
      pages        = {113453 -},
      year         = {2024},
      abstract     = {In recent years, there has been a growing interest in
                      accelerated materials innovation in the context of the
                      process-structure-property chain. In this regard, it is
                      essential to take into account manufacturing processes and
                      tailor materials design approaches to support downstream
                      process design approaches. As a major step into this
                      direction, we present a holistic and generic optimization
                      approach that covers the entire process-structure-property
                      chain in materials engineering. Our approach specifically
                      employs machine learning to address two critical
                      identification problems: a materials design problem, which
                      involves identifying near-optimal material microstructures
                      that exhibit desired properties, and a process design
                      problem that is to find an optimal processing path to
                      manufacture these microstructures. Both identification
                      problems are typically ill-posed, which presents a
                      significant challenge for solution approaches. However, the
                      non-unique nature of these problems offers an important
                      advantage for processing: By having several target
                      microstructures that perform similarly well, processes can
                      be efficiently guided towards manufacturing the best
                      reachable microstructure. The functionality of the approach
                      is demonstrated at manufacturing crystallographic textures
                      with desired properties in a simulated metal forming
                      process.},
      cin          = {IAS-9},
      ddc          = {690},
      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:001361340800001},
      doi          = {10.1016/j.matdes.2024.113453},
      url          = {https://juser.fz-juelich.de/record/1032625},
}