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@ARTICLE{LeDinh:1054076,
      author       = {Le-Dinh, Tan and Schlenz, Hartmut and Menzler, Norbert H.
                      and Franco, Alejandro A. and Guillon, Olivier},
      title        = {{D}ata-driven machine learning modelling for the
                      manufacturing of the fuel electrode support in solid oxide
                      cells},
      journal      = {Energy and AI},
      volume       = {24},
      issn         = {2666-5468},
      address      = {Amsterdam},
      publisher    = {Elsevier ScienceDirect},
      reportid     = {FZJ-2026-01710},
      pages        = {100687 - 100699},
      year         = {2026},
      abstract     = {tape casting typically involves a multi-stage process,
                      demanding precise control over tape thicknessand density.
                      However, conventional SOC manufacturing processes are
                      resource-intensive and oftenrely on $industry/R\&D$
                      unpublished knowledge and trial-and-error practices to
                      achieve the targetproperties of the resulting tape. Hence,
                      machine learning (ML) was employed for predicting
                      thethickness and density across three distinct stages of the
                      fabrication process: tape casting, sintering,and
                      NiO-reduction process. Our developed ML models (e.g., Extra
                      Trees and Ridge Regressions)demonstrate exceptional accuracy
                      (R2 > 0.9) for each specific prediction task.
                      Concurrently,experimental data analysis was conducted to
                      elucidate the impact of the manufacturing parameterson the
                      tape properties. Our data-driven ML approach offers a
                      pathway towards achieving precise tapeproperty control and
                      advancing more efficient SOC support manufacturing.},
      cin          = {IMD-2},
      ddc          = {624},
      cid          = {I:(DE-Juel1)IMD-2-20101013},
      pnm          = {1222 - Components and Cells (POF4-122)},
      pid          = {G:(DE-HGF)POF4-1222},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.1016/j.egyai.2026.100687},
      url          = {https://juser.fz-juelich.de/record/1054076},
}