h1

% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@PHDTHESIS{Kraus:972935,
      author       = {Kraus, Andreas},
      othercontributors = {Kampker, Achim and Heimes, Heiner Hans},
      title        = {{A}nwendungsorientierte {M}ethodik zur frühzeitigen
                      {I}ntegration datenbasierter {A}nsätze in die {A}nlaufphase
                      disruptiver {P}rodukte},
      volume       = {26},
      school       = {RWTH Aachen University},
      type         = {Dissertation},
      address      = {Aachen},
      publisher    = {Apprimus Verlag},
      reportid     = {RWTH-2023-10476},
      isbn         = {978-3-98555-179-8},
      series       = {Ergebnisse aus der Elektromobilproduktion},
      pages        = {1 Online-Ressource : Illustrationen, Diagramme},
      year         = {2023},
      note         = {Druckausgabe: 2023. - Auch veröffentlicht auf dem
                      Publikationsserver der RWTH Aachen University. - Weitere
                      Reihe: Edition Wissenschaft Apprimus. - Weitere Reihe:
                      Elektromobilproduktion; Dissertation, RWTH Aachen
                      University, 2023},
      abstract     = {Manufacturing companies are progressively confronted with
                      the challenge of an increasing number of production ramp-up
                      phases, which are centrally characterized by the instability
                      of production, especially in case of disruptive products. In
                      recent years, data-based approaches from the area of machine
                      learning have increasingly been established in the
                      production environment as an effective tool for improved
                      process stability and thus have the potential to reduce the
                      loss in added value during ramp-up phases. Especially at the
                      beginning of ramp-up phases, the essential prerequisite for
                      an effective use of data-based approaches (i.e., a
                      sufficient data basis for the required training process) is
                      not given. The generation of a sufficient data basis prior
                      to the start of production is in most cases accompanied by a
                      high expenditure of time and costs and furthermore increases
                      strongly with the complexity of the underlying use case. The
                      aim of this dissertation is to develop an
                      application-oriented methodology that supports manufacturing
                      companies in the early identification of potential failures
                      in production as well as in the development and integration
                      of suitable data-based countermeasures. By applying the
                      methodology in early stages of the product development
                      process, companies have the opportunity to integrate the
                      developed measures into their production environment prior
                      to the beginning of the ramp-up phase of new products, thus
                      preventing the loss in added value during the production
                      ramp-up phase itself. The methodology developed within the
                      scope of this dissertation consists of three successive
                      modules that systematically guide users from the
                      identification of potential failures to the development of
                      data-based countermeasures as well as their integration into
                      production. In the first module, all relevant process
                      variables and product parameters along the process chain
                      under consideration are identified. Based on this, the
                      impacts and sensitivities of the individual process steps
                      are quantified, followed by the derivation of an
                      interdependence graph along the process chain.
                      Correspondingly, a failure cost graph along the process
                      chain is determined on the basis of the process costs. The
                      results of the first module serve as a basis for analysis at
                      the beginning of the second module, in order to detect
                      quality and cost critical process steps. As a countermeasure
                      for potential failures within the critical process steps,
                      data-based approaches from the area of machine learning are
                      derived and subsequently complemented by the identification
                      of suitable data-based and knowledge-based sources for the
                      required model training. In the third module of the
                      methodology, the modeling and corresponding model training
                      of the data-based approach is performed and thus yields the
                      final trained model. Lastly, a complete catalog of measures
                      is derived in order to complement the integration in
                      production.},
      cin          = {420910},
      ddc          = {620},
      cid          = {$I:(DE-82)420910_20140620$},
      typ          = {PUB:(DE-HGF)11 / PUB:(DE-HGF)3},
      doi          = {10.18154/RWTH-2023-10476},
      url          = {https://publications.rwth-aachen.de/record/972935},
}