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@PHDTHESIS{Dokhanchi:971794,
      author       = {Dokhanchi, Ali},
      othercontributors = {Bobzin, Kirsten and Bergs, Thomas},
      title        = {{T}owards digital shadow in plasma spraying},
      volume       = {74},
      school       = {RWTH Aachen University},
      type         = {Dissertation},
      address      = {Düren},
      publisher    = {Shaker Verlag},
      reportid     = {RWTH-2023-09800},
      isbn         = {978-3-8440-9268-4},
      series       = {Schriftenreihe Oberflächentechnik},
      pages        = {1 Online-Ressource : Illustrationen, Diagramme},
      year         = {2023},
      note         = {Druckausgabe: 2023. - Auch veröffentlicht auf dem
                      Publikationsserver der RWTH Aachen University; Dissertation,
                      RWTH Aachen University, 2023},
      abstract     = {Atmospheric Plasma Spraying (APS) is a versatile coating
                      technology, which offers a broad range of functional
                      features. Deposition efficiency (DE) is a major performance
                      measure in APS, which is determined by dozens of intrinsic
                      and extrinsic influencing factors. Because of the nonlinear
                      and complicated interdependencies of the contributing
                      variables, enhancing DE has always been a challenging task
                      in the process development of APS. Hence, employing an
                      ensemble of computer-aided methods is inevitable to
                      understand and control these correlations in such a complex
                      coating technology. The concept of the so-called Digital
                      Shadow combines domain-specific models with data-driven
                      techniques of Artificial Intelligence (AI), inferred by
                      autonomous agents to create a sufficiently accurate image of
                      the production process including all relevant data. This
                      dissertation is devoted to the development of the primary
                      steps towards a Digital Shadow in APS with the ultimate goal
                      of improving the process efficiency. Modern AI methods,
                      namely Support Vector Machine (SVM) and Adaptive Neuro-Fuzzy
                      Inference System (ANFIS), were used in this work to predict
                      DE. For this purpose, both simulation and experimental data
                      from the entire process chain of APS were employed to train
                      the AI models, and combine them in the frame of an expert
                      system. These data include process parameters, in-flight
                      particle properties and DE on the substrate. The developed
                      expert system consists of two subsystems: one for predicting
                      in-flight particle properties from process parameters using
                      SVM technique and another for predicting DE from particle
                      properties using ANFIS. To tackle the problem of
                      insufficient data for training the aforementioned AI models
                      two approaches were pursued: 1) A method was developed for
                      in situ determination of spatially resolved deposition
                      efficiencies on the substrate, namely Local Deposition
                      Efficiency (LDE). By using LDE, sufficient amount of data
                      for learning algorithms could be generated, while providing
                      that much data for ex situ measurements of global DE and
                      their corresponding particle properties would be
                      impractical. 2) Simulation data for the in-flight particle
                      properties were generated by using the simulation models of
                      the plasma jet already developed at IOT. The combination of
                      these two strategies provided the aggregated and purpose
                      driven data sets required for a Digital Shadow in APS. The
                      developed expert system can be used as a tool to adjust the
                      process parameters to produce sustainable and cost-effective
                      coatings, and subsequently improves the integration of
                      coating process into production chain.},
      cin          = {419010},
      ddc          = {620},
      cid          = {$I:(DE-82)419010_20140620$},
      pnm          = {DFG project 390621612 - EXC 2023: Internet of Production
                      (IoP) (390621612) / WS-B2.II - Discontinuous Production
                      (X080067-WS-B2.II) / DFG project 352196289 - Entwicklung
                      einer Methode zur in-situ Bestimmung des
                      Auftragswirkungsgrads beim Thermischen Spritzen (352196289)},
      pid          = {G:(GEPRIS)390621612 / G:(DE-82)X080067-WS-B2.II /
                      G:(GEPRIS)352196289},
      typ          = {PUB:(DE-HGF)11 / PUB:(DE-HGF)3},
      doi          = {10.18154/RWTH-2023-09800},
      url          = {https://publications.rwth-aachen.de/record/971794},
}