---
res:
  bibo_abstract:
  - Given strict emission targets and legal requirements, especially in the automotive
    industry, environmentally friendly and simultaneously versatile applicable production
    technologies are gaining importance. In this regard, the use of mechanical joining
    processes, such as clinching, enable assembly sheet metals to achieve strength
    properties similar to those of established thermal joining technologies. However,
    to guarantee a high reliability of the generated joint connection, the selection
    of a best-fitting joining technology as well as the meaningful description of
    individual joint properties is essential. In the context of clinching, few contributions
    have to date investigated the metamodel-based estimation and optimization of joint
    characteristics, such as neck or interlock thickness, by applying machine learning
    and genetic algorithms. Therefore, several regression models have been trained
    on varying databases and amounts of input parameters. However, if product engineers
    can only provide limited data for a new joining task, such as incomplete information
    on applied joining tool dimensions, previously trained metamodels often reach
    their limits. This often results in a significant loss of prediction quality and
    leads to increasing uncertainties and inaccuracies within the metamodel-based
    design of a clinch joint connection. Motivated by this, the presented contribution
    investigates different machine learning algorithms regarding their ability to
    achieve a satisfying estimation accuracy on limited input data applying a statistically
    based feature selection method. Through this, it is possible to identify which
    regression models are suitable to predict clinch joint characteristics considering
    only a minimum set of required input features. Thus, in addition to the opportunity
    to decrease the training effort as well as the model complexity, the subsequent
    formulation of design equations can pave the way to a more versatile application
    and reuse of pretrained metamodels on varying tool configurations for a given
    clinch joining task.@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: 10.3390/ai3040059
  bibo_issue: '4'
  bibo_volume: 3
  dct_date: 2022^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/2673-2688
  dct_language: eng
  dct_publisher: MDPI AG@
  dct_subject:
  - Industrial and Manufacturing Engineering
  dct_title: Estimation of Clinch Joint Characteristics Based on Limited Input Data
    Using Pre-Trained Metamodels@
...