---
_id: '34417'
abstract:
- lang: eng
  text: 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.
author:
- first_name: Christoph
  full_name: Zirngibl, Christoph
  last_name: Zirngibl
- first_name: Benjamin
  full_name: Schleich, Benjamin
  last_name: Schleich
- first_name: Sandro
  full_name: Wartzack, Sandro
  last_name: Wartzack
citation:
  ama: Zirngibl C, Schleich B, Wartzack S. Estimation of Clinch Joint Characteristics
    Based on Limited Input Data Using Pre-Trained Metamodels. <i>AI</i>. 2022;3(4):990-1006.
    doi:<a href="https://doi.org/10.3390/ai3040059">10.3390/ai3040059</a>
  apa: Zirngibl, C., Schleich, B., &#38; Wartzack, S. (2022). Estimation of Clinch
    Joint Characteristics Based on Limited Input Data Using Pre-Trained Metamodels.
    <i>AI</i>, <i>3</i>(4), 990–1006. <a href="https://doi.org/10.3390/ai3040059">https://doi.org/10.3390/ai3040059</a>
  bibtex: '@article{Zirngibl_Schleich_Wartzack_2022, title={Estimation of Clinch Joint
    Characteristics Based on Limited Input Data Using Pre-Trained Metamodels}, volume={3},
    DOI={<a href="https://doi.org/10.3390/ai3040059">10.3390/ai3040059</a>}, number={4},
    journal={AI}, publisher={MDPI AG}, author={Zirngibl, Christoph and Schleich, Benjamin
    and Wartzack, Sandro}, year={2022}, pages={990–1006} }'
  chicago: 'Zirngibl, Christoph, Benjamin Schleich, and Sandro Wartzack. “Estimation
    of Clinch Joint Characteristics Based on Limited Input Data Using Pre-Trained
    Metamodels.” <i>AI</i> 3, no. 4 (2022): 990–1006. <a href="https://doi.org/10.3390/ai3040059">https://doi.org/10.3390/ai3040059</a>.'
  ieee: 'C. Zirngibl, B. Schleich, and S. Wartzack, “Estimation of Clinch Joint Characteristics
    Based on Limited Input Data Using Pre-Trained Metamodels,” <i>AI</i>, vol. 3,
    no. 4, pp. 990–1006, 2022, doi: <a href="https://doi.org/10.3390/ai3040059">10.3390/ai3040059</a>.'
  mla: Zirngibl, Christoph, et al. “Estimation of Clinch Joint Characteristics Based
    on Limited Input Data Using Pre-Trained Metamodels.” <i>AI</i>, vol. 3, no. 4,
    MDPI AG, 2022, pp. 990–1006, doi:<a href="https://doi.org/10.3390/ai3040059">10.3390/ai3040059</a>.
  short: C. Zirngibl, B. Schleich, S. Wartzack, AI 3 (2022) 990–1006.
date_created: 2022-12-14T12:32:29Z
date_updated: 2022-12-14T13:43:53Z
doi: 10.3390/ai3040059
intvolume: '         3'
issue: '4'
keyword:
- Industrial and Manufacturing Engineering
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://www.mdpi.com/2673-2688/3/4/59
oa: '1'
page: 990-1006
project:
- _id: '130'
  grant_number: '418701707'
  name: 'TRR 285: TRR 285'
- _id: '132'
  name: 'TRR 285 - B: TRR 285 - Project Area B'
- _id: '144'
  name: 'TRR 285 – B05: TRR 285 - Subproject B05'
publication: AI
publication_identifier:
  issn:
  - 2673-2688
publication_status: published
publisher: MDPI AG
status: public
title: Estimation of Clinch Joint Characteristics Based on Limited Input Data Using
  Pre-Trained Metamodels
type: journal_article
user_id: '7850'
volume: 3
year: '2022'
...