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@PHDTHESIS{Dahlem:949206,
      author       = {Dahlem, Jan Philipp},
      othercontributors = {Schmitt, Robert H. and Mayer, René},
      title        = {{H}ybrid modeling of transient volumetric machine tool
                      errors for virtual climatization},
      school       = {Rheinisch-Westfälische Technische Hochschule Aachen},
      type         = {Dissertation},
      address      = {Aachen},
      publisher    = {RWTH Aachen University},
      reportid     = {RWTH-2023-01620},
      pages        = {1 Online-Ressource : Illustrationen, Diagrammen},
      year         = {2023},
      note         = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
                      University; Dissertation, Rheinisch-Westfälische Technische
                      Hochschule Aachen, 2023},
      abstract     = {The global trend towards more individual and complex
                      products, and hence, smaller batch sizes, and tighter
                      tolerances is ongoing. At the same time, the need for more
                      sustainable production technologies and an overall smaller
                      carbon footprint is higher than ever before. Industries are
                      facing the challenge of meeting the resulting requirements
                      of both developments. The reduction of the energy
                      consumption of production companies is a central component
                      of the international targeted transformation process. In
                      2020, 44.2 $\%$ of electricity consumption in Germany was
                      attributable to industry. As machine tools are the backbone
                      of modern production, their further development in terms of
                      higher performance, increased volumetric accuracy in
                      conjunction with significantly more efficient operation is
                      essential.In order to ensure high accuracy requirements in
                      production, complex disturbances due to temperature input
                      are typically suppressed with energy-intensive
                      air-conditioning and cooling measures. As an energy-saving
                      alternative, the concept of virtual climatization for
                      machine tools is presented in this thesis, which is only
                      able to predict thermally transient errors of machine tools
                      for a targeted compensation with the help of suitable
                      models. The work is therefore concerned with the goal to
                      identify and develop suitable models for this application
                      and combining them into reliable hybrid models. Already
                      known prior knowledge about the thermal behavior of machine
                      tools shall be taken into account in the form of submodels.
                      At the same time, the manual effort for the modeling shall
                      be kept as low as possible to facilitate a broad application
                      to different machine tool types. By means of a suitable
                      approximation of physical structural deformations and a
                      combination with machine learning algorithms, an efficient
                      model adaptation to machine-specific conditions shall be
                      implemented. In addition, the work investigated the question
                      of how the necessary model data can be acquired for the
                      model setup and also for the application in operation. A
                      special focus is on the application of the overall concept
                      as a retrofit solution for existing machines. The research
                      work follows the method of Design Science Research. The
                      developed artifacts, including the Abstracted Physical Body
                      Model and the hybrid model structure for the combination
                      with machine learning, as well as various concepts for the
                      acquisition of suitable model data, are investigated and
                      validated in a consolidated experiment, following the Design
                      of Experiments format. A comparison of different
                      combinations of submodels with respect to the model
                      properties is carried out, whereby the advantages of the
                      pursued approach can be clearly highlighted.},
      cin          = {417510 / 417200},
      ddc          = {620},
      cid          = {$I:(DE-82)417510_20140620$ / $I:(DE-82)417200_20140620$},
      pnm          = {DFG project 390621612 - EXC 2023: Internet of Production
                      (IoP) (390621612) / DFG project 298597595 -
                      Modellgestütztes und maschinenintegrierbares
                      Multisensorsystem zur automatischen und prozessparallelen
                      Ermittlung und Kompensation des volumetrischen
                      Maschinenfehlers am funktionalen Punkt (AutoKomp II)
                      (298597595)},
      pid          = {G:(GEPRIS)390621612 / G:(GEPRIS)298597595},
      typ          = {PUB:(DE-HGF)11},
      doi          = {10.18154/RWTH-2023-01620},
      url          = {https://publications.rwth-aachen.de/record/949206},
}