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@PHDTHESIS{Knaak:1010863,
      author       = {Knaak, Christian},
      othercontributors = {Häfner, Constantin Leon and Schmitt, Robert H.},
      title        = {{E}chtzeitüberwachung und -optimierung der {N}ahtqualität
                      beim {L}aserstrahlschweißen mittels bildgebender {S}ensorik
                      und künstlicher {I}ntelligenz; 1. {A}uflage},
      school       = {RWTH Aachen University},
      type         = {Dissertation},
      address      = {Aachen},
      publisher    = {Apprimus Verlag},
      reportid     = {RWTH-2025-04385},
      isbn         = {978-3-98555-277-1},
      series       = {Ergebnisse aus der Lasertechnik},
      pages        = {1 Online-Ressource : Illustrationen},
      year         = {2025},
      note         = {Druckausgabe: 2025. - Auch veröffentlicht auf dem
                      Publikationsserver der RWTH Aachen University; Dissertation,
                      RWTH Aachen University, 2025},
      abstract     = {Laser-based manufacturing technology is an indispensable
                      tool for the cost-efficient production of products, such as
                      battery electric vehicles, which are of utmost importance
                      for meeting current societal challenges. However, decreasing
                      cycle times, growing demands on product quality, and
                      increasing flexibility requirements as well as desired
                      increases in cost efficiency represent growing challenges
                      from the perspective of manufacturing technology. In
                      addition to process-specific possibilities for improvement
                      within the framework of the respective applications, the
                      digitalization and automation of manufacturing processes in
                      particular offer the potential to further facilitate market
                      access with regard to resource-saving end products. In order
                      to tap this potential, it is necessary to collect and
                      intelligently process extensive machine, process and
                      production data so that data-supported recommendations for
                      action can be derived. In the context of this work, the
                      holistic evaluation and optimization of product quality
                      during production is in the foreground. In this context, an
                      AI-based process monitoring system is developed and
                      evaluated using the example of laser-welded seams on
                      galvanized car body components, which is able to distinguish
                      between different seam irregularities and process deviations
                      during the process. In addition, the neural network-based AI
                      system will be extended to extract characteristic process
                      features from the image data, which will provide the
                      informational foundation for downstream process control.
                      Application-specific optimizations of the neural network
                      architecture are also part of the investigations. The
                      metrological basis for the quality assurance system is
                      provided by image-based sensors in different observation
                      configurations, which also allow a comparison of individual
                      image features with regard to their detection performance.
                      An additional evaluation of the seam quality takes place in
                      the form of a hybrid modelling of the weld penetration depth
                      with subsequent calibration on the basis of specific image
                      features. The hybrid model allows the weld penetration depth
                      to be calculated based on current image data and the process
                      parameters used during the process. Since an evaluation of
                      the uncertainty of the used AI system is crucial in the
                      context of this application, an approach is presented that
                      allows the estimation of the epistemic uncertainty of the
                      neural network based on outlier detection. Ultimately, a
                      process control system will be implemented and tested using
                      algorithms from the field of reinforcement learning, which
                      promise a high degree of adaptability to new process
                      conditions. The overall system is examined with respect to
                      its real-time capability and finally evaluated on the basis
                      of experimental investigations to determine the achievable
                      defect detection and mitigation performance.},
      cin          = {418710 / 053100},
      ddc          = {620},
      cid          = {$I:(DE-82)418710_20140620$ / $I:(DE-82)053100_20140620$},
      typ          = {PUB:(DE-HGF)11 / PUB:(DE-HGF)3},
      doi          = {10.18154/RWTH-2025-04385},
      url          = {https://publications.rwth-aachen.de/record/1010863},
}