---
res:
  bibo_abstract:
  - Given a steadily increasing demand on multi-material lightweight designs, fast
    and cost-efficient production technologies, such as the mechanical joining process
    clinching, are becoming more and more relevant for series production. Since the
    application of such joining techniques often base on the ability to reach similar
    or even better joint loading capacities compared to established joining processes
    (e.g., spot welding), few contributions investigated the systematic improvement
    of clinch joint characteristics. In this regard, the use of data-driven methods
    in combination with optimization algorithms showed already high potentials for
    the analysis of individual joints and the definition of optimal tool configurations.
    However, the often missing consideration of uncertainties, such as varying material
    properties, and the related calculation of their impact on clinch joint properties
    can lead to poor estimation results and thus to a decreased reliability of the
    entire joint connection. This can cause major challenges, especially for the design
    and dimensioning of safety-relevant components, such as in car bodies. Motivated
    by this, the presented contribution introduces a novel method for the robust estimation
    of clinch joint characteristics including uncertainties of varying and versatile
    process chains in mechanical joining. Therefore, the utilization of Gaussian process
    regression models is demonstrated and evaluated regarding the ability to achieve
    sufficient prediction qualities.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Christoph
      foaf_name: Zirngibl, Christoph
      foaf_surname: Zirngibl
  - foaf_Person:
      foaf_givenName: Benjamin
      foaf_name: Schleich, Benjamin
      foaf_surname: Schleich
  - foaf_Person:
      foaf_givenName: Sandro
      foaf_name: Wartzack, Sandro
      foaf_surname: Wartzack
  bibo_doi: 10.1007/s00170-022-10441-7
  dct_date: 2022^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/0268-3768
  - http://id.crossref.org/issn/1433-3015
  dct_language: eng
  dct_publisher: Springer Science and Business Media LLC@
  dct_subject:
  - Industrial and Manufacturing Engineering
  - Computer Science Applications
  - Mechanical Engineering
  - Software
  - Control and Systems Engineering
  dct_title: Robust estimation of clinch joint characteristics based on data-driven
    methods@
...