---
_id: '65620'
abstract:
- lang: eng
text: "Abstract\r\n The design
of clinch joints is a cost- and time-intensive iterative process due to the complex
relationships between tool and process parameters and the resulting joint properties.
To address this, this contribution proposes a novel hybrid workflow that combines
knowledge- and data-based approaches. Relationships are categorized based on their
knowledge quality and the need for a quantitative prediction. Well-established,
generalizable relationships are formalized in an ontology as design guidelines
(no quantification required) or SWRL rules (quantification required) to model
expert knowledge. In contrast, hard-to-formalize or not-fully-understood relationships
are treated with regression models for continuous or classification models for
binary criteria. These approaches are combined in a generic user interface (GUI),
where the ontology can be accessed using predefined SPARQL queries to select and
adapt parameters using expert knowledge. These parameters are then used as input
for the metamodels. The developed workflow is evaluated on two exemplary joining
tasks to illustrate, how designers can retrieve similar prior joints, adapt parameters
using the encoded design rules and predict resulting joint properties under varying
process conditions. In summary, the combination of ontology and metamodels facilitates
the transition of trial and error into an efficient, documentable design process."
article_number: '56'
author:
- first_name: Jonathan-Markus
full_name: Einwag, Jonathan-Markus
last_name: Einwag
- first_name: Maximilian
full_name: Wiemer, Maximilian
last_name: Wiemer
- first_name: Sandro
full_name: Wartzack, Sandro
last_name: Wartzack
- first_name: Stefan
full_name: Goetz, Stefan
last_name: Goetz
citation:
ama: Einwag J-M, Wiemer M, Wartzack S, Goetz S. A hybrid knowledge based and data
based approach for efficient clinch joint design. Discover Mechanical Engineering.
2026;5(1). doi:10.1007/s44245-026-00230-x
apa: Einwag, J.-M., Wiemer, M., Wartzack, S., & Goetz, S. (2026). A hybrid knowledge
based and data based approach for efficient clinch joint design. Discover Mechanical
Engineering, 5(1), Article 56. https://doi.org/10.1007/s44245-026-00230-x
bibtex: '@article{Einwag_Wiemer_Wartzack_Goetz_2026, title={A hybrid knowledge based
and data based approach for efficient clinch joint design}, volume={5}, DOI={10.1007/s44245-026-00230-x},
number={156}, journal={Discover Mechanical Engineering}, publisher={Springer Science
and Business Media LLC}, author={Einwag, Jonathan-Markus and Wiemer, Maximilian
and Wartzack, Sandro and Goetz, Stefan}, year={2026} }'
chicago: Einwag, Jonathan-Markus, Maximilian Wiemer, Sandro Wartzack, and Stefan
Goetz. “A Hybrid Knowledge Based and Data Based Approach for Efficient Clinch
Joint Design.” Discover Mechanical Engineering 5, no. 1 (2026). https://doi.org/10.1007/s44245-026-00230-x.
ieee: 'J.-M. Einwag, M. Wiemer, S. Wartzack, and S. Goetz, “A hybrid knowledge based
and data based approach for efficient clinch joint design,” Discover Mechanical
Engineering, vol. 5, no. 1, Art. no. 56, 2026, doi: 10.1007/s44245-026-00230-x.'
mla: Einwag, Jonathan-Markus, et al. “A Hybrid Knowledge Based and Data Based Approach
for Efficient Clinch Joint Design.” Discover Mechanical Engineering, vol.
5, no. 1, 56, Springer Science and Business Media LLC, 2026, doi:10.1007/s44245-026-00230-x.
short: J.-M. Einwag, M. Wiemer, S. Wartzack, S. Goetz, Discover Mechanical Engineering
5 (2026).
date_created: 2026-05-13T11:46:44Z
date_updated: 2026-05-13T11:50:48Z
doi: 10.1007/s44245-026-00230-x
intvolume: ' 5'
issue: '1'
language:
- iso: eng
project:
- _id: '130'
name: 'TRR 285: Methodenentwicklung zur mechanischen Fügbarkeit in wandlungsfähigen
Prozessketten'
- _id: '132'
name: TRR 285 - Project Area B
- _id: '144'
name: TRR 285 - Subproject B05
publication: Discover Mechanical Engineering
publication_identifier:
issn:
- 2731-6564
publication_status: published
publisher: Springer Science and Business Media LLC
status: public
title: A hybrid knowledge based and data based approach for efficient clinch joint
design
type: journal_article
user_id: '107109'
volume: 5
year: '2026'
...
---
_id: '61524'
author:
- first_name: Jonathan-Markus
full_name: Einwag, Jonathan-Markus
last_name: Einwag
- first_name: Christian
full_name: Steinfelder, Christian
last_name: Steinfelder
- first_name: Sandro
full_name: Wartzack, Sandro
last_name: Wartzack
- first_name: Alexander
full_name: Brosius, Alexander
last_name: Brosius
- first_name: Stefan
full_name: Goetz, Stefan
last_name: Goetz
citation:
ama: 'Einwag J-M, Steinfelder C, Wartzack S, Brosius A, Goetz S. From simulation
to metamodel to experiment: Evaluating the prediction accuracy of polynomial regression
models for clinch joint properties. Journal of Manufacturing Processes.
2025;154:179-191. doi:10.1016/j.jmapro.2025.09.059'
apa: 'Einwag, J.-M., Steinfelder, C., Wartzack, S., Brosius, A., & Goetz, S.
(2025). From simulation to metamodel to experiment: Evaluating the prediction
accuracy of polynomial regression models for clinch joint properties. Journal
of Manufacturing Processes, 154, 179–191. https://doi.org/10.1016/j.jmapro.2025.09.059'
bibtex: '@article{Einwag_Steinfelder_Wartzack_Brosius_Goetz_2025, title={From simulation
to metamodel to experiment: Evaluating the prediction accuracy of polynomial regression
models for clinch joint properties}, volume={154}, DOI={10.1016/j.jmapro.2025.09.059},
journal={Journal of Manufacturing Processes}, publisher={Elsevier BV}, author={Einwag,
Jonathan-Markus and Steinfelder, Christian and Wartzack, Sandro and Brosius, Alexander
and Goetz, Stefan}, year={2025}, pages={179–191} }'
chicago: 'Einwag, Jonathan-Markus, Christian Steinfelder, Sandro Wartzack, Alexander
Brosius, and Stefan Goetz. “From Simulation to Metamodel to Experiment: Evaluating
the Prediction Accuracy of Polynomial Regression Models for Clinch Joint Properties.”
Journal of Manufacturing Processes 154 (2025): 179–91. https://doi.org/10.1016/j.jmapro.2025.09.059.'
ieee: 'J.-M. Einwag, C. Steinfelder, S. Wartzack, A. Brosius, and S. Goetz, “From
simulation to metamodel to experiment: Evaluating the prediction accuracy of polynomial
regression models for clinch joint properties,” Journal of Manufacturing Processes,
vol. 154, pp. 179–191, 2025, doi: 10.1016/j.jmapro.2025.09.059.'
mla: 'Einwag, Jonathan-Markus, et al. “From Simulation to Metamodel to Experiment:
Evaluating the Prediction Accuracy of Polynomial Regression Models for Clinch
Joint Properties.” Journal of Manufacturing Processes, vol. 154, Elsevier
BV, 2025, pp. 179–91, doi:10.1016/j.jmapro.2025.09.059.'
short: J.-M. Einwag, C. Steinfelder, S. Wartzack, A. Brosius, S. Goetz, Journal
of Manufacturing Processes 154 (2025) 179–191.
date_created: 2025-10-06T07:37:24Z
date_updated: 2025-11-12T08:35:35Z
department:
- _id: '43'
- _id: '157'
doi: 10.1016/j.jmapro.2025.09.059
intvolume: ' 154'
language:
- iso: eng
page: 179-191
project:
- _id: '130'
name: 'TRR 285: Methodenentwicklung zur mechanischen Fügbarkeit in wandlungsfähigen
Prozessketten'
- _id: '132'
name: TRR 285 - Project Area B
- _id: '140'
name: TRR 285 - Subproject B01
- _id: '144'
name: TRR 285 - Subproject B05
publication: Journal of Manufacturing Processes
publication_identifier:
issn:
- 1526-6125
publication_status: published
publisher: Elsevier BV
status: public
title: 'From simulation to metamodel to experiment: Evaluating the prediction accuracy
of polynomial regression models for clinch joint properties'
type: journal_article
user_id: '107109'
volume: 154
year: '2025'
...
---
_id: '60198'
abstract:
- lang: eng
text: Abstract. The growing significance of lightweight design, reveals
drawbacks of conventional joining processes such as welding, which are known to
consume a considerable amount of energy. This fosters the use of mechanical joining
processes including clinching. However, the lack of universally applicable design
methods results in a cost- and time-intensive design process. The utilization
of machine learning methods can overcome these drawbacks. To ensure a reliable
clinch joint design, inherent uncertainties of the design parameter such as tool
deviations need to be considered in the design process. Varying distributions
of design parameters, due to changes in the manufacturing process, can lead to
high-computational effort in recalculating the resulting clinch joint properties
with numerical simulations. Current metamodel-based methods for consideration
of inherent uncertainties within the design parameters do not investigate the
transferability of metamodels to different distributions of design parameters,
which can lead to incorrect predictions. Therefore, this contribution investigates
the performance of several metamodels on differently distributed design parameters.
The obtained results indicate that metamodels demonstrate the best performance
when training and evaluation distributions are identical and that polynomial regression
models perform best on disparate distributions, when trained on uniform distributions.
author:
- first_name: Jonathan-Markus
full_name: Einwag, Jonathan-Markus
last_name: Einwag
- first_name: Yannik
full_name: Mayer, Yannik
last_name: Mayer
- first_name: Stefan
full_name: Goetz, Stefan
last_name: Goetz
- first_name: Sandro
full_name: Wartzack, Sandro
last_name: Wartzack
citation:
ama: 'Einwag J-M, Mayer Y, Goetz S, Wartzack S. Impact of the parameter distribution
on the predictive quality of metamodels for clinch joint properties. In: Materials
Research Proceedings. Vol 52. Materials Research Forum LLC; 2025. doi:10.21741/9781644903551-35'
apa: Einwag, J.-M., Mayer, Y., Goetz, S., & Wartzack, S. (2025). Impact of the
parameter distribution on the predictive quality of metamodels for clinch joint
properties. Materials Research Proceedings, 52. https://doi.org/10.21741/9781644903551-35
bibtex: '@inproceedings{Einwag_Mayer_Goetz_Wartzack_2025, title={Impact of the parameter
distribution on the predictive quality of metamodels for clinch joint properties},
volume={52}, DOI={10.21741/9781644903551-35},
booktitle={Materials Research Proceedings}, publisher={Materials Research Forum
LLC}, author={Einwag, Jonathan-Markus and Mayer, Yannik and Goetz, Stefan and
Wartzack, Sandro}, year={2025} }'
chicago: Einwag, Jonathan-Markus, Yannik Mayer, Stefan Goetz, and Sandro Wartzack.
“Impact of the Parameter Distribution on the Predictive Quality of Metamodels
for Clinch Joint Properties.” In Materials Research Proceedings, Vol. 52.
Materials Research Forum LLC, 2025. https://doi.org/10.21741/9781644903551-35.
ieee: 'J.-M. Einwag, Y. Mayer, S. Goetz, and S. Wartzack, “Impact of the parameter
distribution on the predictive quality of metamodels for clinch joint properties,”
in Materials Research Proceedings, 2025, vol. 52, doi: 10.21741/9781644903551-35.'
mla: Einwag, Jonathan-Markus, et al. “Impact of the Parameter Distribution on the
Predictive Quality of Metamodels for Clinch Joint Properties.” Materials Research
Proceedings, vol. 52, Materials Research Forum LLC, 2025, doi:10.21741/9781644903551-35.
short: 'J.-M. Einwag, Y. Mayer, S. Goetz, S. Wartzack, in: Materials Research Proceedings,
Materials Research Forum LLC, 2025.'
date_created: 2025-06-12T15:03:22Z
date_updated: 2025-06-12T15:06:41Z
doi: 10.21741/9781644903551-35
intvolume: ' 52'
language:
- iso: eng
project:
- _id: '130'
grant_number: '418701707'
name: 'TRR 285: TRR 285: Methodenentwicklung zur mechanischen Fügbarkeit in wandlungsfähigen
Prozessketten'
- _id: '132'
name: 'TRR 285 - B: TRR 285 - Project Area B'
- _id: '144'
name: 'TRR 285 – B05: TRR 285 - Subproject B05'
publication: Materials Research Proceedings
publication_identifier:
issn:
- 2474-395X
publication_status: published
publisher: Materials Research Forum LLC
status: public
title: Impact of the parameter distribution on the predictive quality of metamodels
for clinch joint properties
type: conference
user_id: '107109'
volume: 52
year: '2025'
...
---
_id: '54797'
abstract:
- lang: eng
text: Focusing on upcoming challenges in lightweight design, such as increasing
emission targets or novel multimaterial connections, versatile applicable and
environmentally friendly production technologies are crucial. In this context,
mechanical joining technology clinching offers a fast and energy-efficient procedure
for assembling sheet metals, being a proper alternative to established joining
methods, such as spot welding. However, the design of clinch points is a challenge,
which is partly supported by numerical or data-based approaches for optimal tool
dimensions assuring proper joint characteristics. While this is usually done for
an ideal environment, real joining processes are characterized by multiple inevitably
varying parameters, e.g. of the material, which have a significant impact on the
quality of clinch points. Therefore, this contribution addresses the current gap
by analyzing the effect of parameter variations or uncertainties on the resulting
joint characteristics and studying the impact of the nominal tool design. Thus,
an efficient meta-model-based variation simulation procedure is proposed and used
for analyzing the effect of different tool design configurations and variation
scenarios. Based on the results, it was found that varying process parameters
have a strong impact on the resulting joint characteristics, whereby the effect
significantly depends on the nominal tool design. This reveals the potential for
a robust tool design and implies that the nominal tool design and the tolerancing
of parameters should be done simultaneously for a reliable virtual joining point
design without extensive iterations and physical tests.
author:
- first_name: C
full_name: Zirngibl, C
last_name: Zirngibl
- first_name: S
full_name: Goetz, S
last_name: Goetz
- first_name: S
full_name: Wartzack, S
last_name: Wartzack
citation:
ama: 'Zirngibl C, Goetz S, Wartzack S. Influence of process variations on clinch
joint characteristics considering the effect of the nominal tool design. Proceedings
of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical
Engineering. Published online 2024. doi:10.1177/09544089241259347'
apa: 'Zirngibl, C., Goetz, S., & Wartzack, S. (2024). Influence of process variations
on clinch joint characteristics considering the effect of the nominal tool design.
Proceedings of the Institution of Mechanical Engineers, Part E: Journal of
Process Mechanical Engineering. https://doi.org/10.1177/09544089241259347'
bibtex: '@article{Zirngibl_Goetz_Wartzack_2024, title={Influence of process variations
on clinch joint characteristics considering the effect of the nominal tool design},
DOI={10.1177/09544089241259347},
journal={Proceedings of the Institution of Mechanical Engineers, Part E: Journal
of Process Mechanical Engineering}, publisher={SAGE Publications}, author={Zirngibl,
C and Goetz, S and Wartzack, S}, year={2024} }'
chicago: 'Zirngibl, C, S Goetz, and S Wartzack. “Influence of Process Variations
on Clinch Joint Characteristics Considering the Effect of the Nominal Tool Design.”
Proceedings of the Institution of Mechanical Engineers, Part E: Journal of
Process Mechanical Engineering, 2024. https://doi.org/10.1177/09544089241259347.'
ieee: 'C. Zirngibl, S. Goetz, and S. Wartzack, “Influence of process variations
on clinch joint characteristics considering the effect of the nominal tool design,”
Proceedings of the Institution of Mechanical Engineers, Part E: Journal of
Process Mechanical Engineering, 2024, doi: 10.1177/09544089241259347.'
mla: 'Zirngibl, C., et al. “Influence of Process Variations on Clinch Joint Characteristics
Considering the Effect of the Nominal Tool Design.” Proceedings of the Institution
of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering,
SAGE Publications, 2024, doi:10.1177/09544089241259347.'
short: 'C. Zirngibl, S. Goetz, S. Wartzack, Proceedings of the Institution of Mechanical
Engineers, Part E: Journal of Process Mechanical Engineering (2024).'
date_created: 2024-06-18T07:00:26Z
date_updated: 2024-06-18T07:06:38Z
department:
- _id: '630'
doi: 10.1177/09544089241259347
language:
- iso: eng
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: 'Proceedings of the Institution of Mechanical Engineers, Part E: Journal
of Process Mechanical Engineering'
publication_identifier:
issn:
- 0954-4089
- 2041-3009
publication_status: published
publisher: SAGE Publications
status: public
title: Influence of process variations on clinch joint characteristics considering
the effect of the nominal tool design
type: journal_article
user_id: '107109'
year: '2024'
...
---
_id: '56682'
author:
- first_name: Jonathan-Markus
full_name: Einwag, Jonathan-Markus
last_name: Einwag
- first_name: Stefan
full_name: Goetz, Stefan
last_name: Goetz
- first_name: Sandro
full_name: Wartzack, Sandro
last_name: Wartzack
citation:
ama: 'Einwag J-M, Goetz S, Wartzack S. Approach for the Reliable and Virtual Design
of Mechanical Joints in an Uncertain Environment. In: DS 133: Proceedings of
the 35th Symposium Design for X (DFX2024). The Design Society; 2024. doi:10.35199/dfx2024.23'
apa: 'Einwag, J.-M., Goetz, S., & Wartzack, S. (2024). Approach for the Reliable
and Virtual Design of Mechanical Joints in an Uncertain Environment. DS 133:
Proceedings of the 35th Symposium Design for X (DFX2024). https://doi.org/10.35199/dfx2024.23'
bibtex: '@inproceedings{Einwag_Goetz_Wartzack_2024, title={Approach for the Reliable
and Virtual Design of Mechanical Joints in an Uncertain Environment}, DOI={10.35199/dfx2024.23}, booktitle={DS
133: Proceedings of the 35th Symposium Design for X (DFX2024)}, publisher={The
Design Society}, author={Einwag, Jonathan-Markus and Goetz, Stefan and Wartzack,
Sandro}, year={2024} }'
chicago: 'Einwag, Jonathan-Markus, Stefan Goetz, and Sandro Wartzack. “Approach
for the Reliable and Virtual Design of Mechanical Joints in an Uncertain Environment.”
In DS 133: Proceedings of the 35th Symposium Design for X (DFX2024). The
Design Society, 2024. https://doi.org/10.35199/dfx2024.23.'
ieee: 'J.-M. Einwag, S. Goetz, and S. Wartzack, “Approach for the Reliable and Virtual
Design of Mechanical Joints in an Uncertain Environment,” 2024, doi: 10.35199/dfx2024.23.'
mla: 'Einwag, Jonathan-Markus, et al. “Approach for the Reliable and Virtual Design
of Mechanical Joints in an Uncertain Environment.” DS 133: Proceedings of the
35th Symposium Design for X (DFX2024), The Design Society, 2024, doi:10.35199/dfx2024.23.'
short: 'J.-M. Einwag, S. Goetz, S. Wartzack, in: DS 133: Proceedings of the 35th
Symposium Design for X (DFX2024), The Design Society, 2024.'
date_created: 2024-10-18T07:59:48Z
date_updated: 2024-10-18T08:16:58Z
department:
- _id: '43'
- _id: '157'
doi: 10.35199/dfx2024.23
language:
- iso: eng
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: 'DS 133: Proceedings of the 35th Symposium Design for X (DFX2024)'
publication_status: published
publisher: The Design Society
status: public
title: Approach for the Reliable and Virtual Design of Mechanical Joints in an Uncertain
Environment
type: conference
user_id: '107109'
year: '2024'
...
---
_id: '58342'
author:
- first_name: Christoph
full_name: Bode, Christoph
last_name: Bode
- first_name: Stefan
full_name: Goetz, Stefan
last_name: Goetz
- first_name: Sandro
full_name: Wartzack, Sandro
last_name: Wartzack
citation:
ama: Bode C, Goetz S, Wartzack S. On the transferability of nominal surrogate models
to uncertainty consideration of clinch joint characteristics. Procedia CIRP.
2024;129:151-156. doi:10.1016/j.procir.2024.10.027
apa: Bode, C., Goetz, S., & Wartzack, S. (2024). On the transferability of nominal
surrogate models to uncertainty consideration of clinch joint characteristics.
Procedia CIRP, 129, 151–156. https://doi.org/10.1016/j.procir.2024.10.027
bibtex: '@article{Bode_Goetz_Wartzack_2024, title={On the transferability of nominal
surrogate models to uncertainty consideration of clinch joint characteristics},
volume={129}, DOI={10.1016/j.procir.2024.10.027},
journal={Procedia CIRP}, publisher={Elsevier BV}, author={Bode, Christoph and
Goetz, Stefan and Wartzack, Sandro}, year={2024}, pages={151–156} }'
chicago: 'Bode, Christoph, Stefan Goetz, and Sandro Wartzack. “On the Transferability
of Nominal Surrogate Models to Uncertainty Consideration of Clinch Joint Characteristics.”
Procedia CIRP 129 (2024): 151–56. https://doi.org/10.1016/j.procir.2024.10.027.'
ieee: 'C. Bode, S. Goetz, and S. Wartzack, “On the transferability of nominal surrogate
models to uncertainty consideration of clinch joint characteristics,” Procedia
CIRP, vol. 129, pp. 151–156, 2024, doi: 10.1016/j.procir.2024.10.027.'
mla: Bode, Christoph, et al. “On the Transferability of Nominal Surrogate Models
to Uncertainty Consideration of Clinch Joint Characteristics.” Procedia CIRP,
vol. 129, Elsevier BV, 2024, pp. 151–56, doi:10.1016/j.procir.2024.10.027.
short: C. Bode, S. Goetz, S. Wartzack, Procedia CIRP 129 (2024) 151–156.
date_created: 2025-01-23T13:53:59Z
date_updated: 2025-10-01T14:32:26Z
department:
- _id: '157'
doi: 10.1016/j.procir.2024.10.027
intvolume: ' 129'
language:
- iso: eng
page: 151-156
project:
- _id: '132'
name: 'TRR 285 - B: TRR 285 - Project Area B'
- _id: '130'
name: 'TRR 285: Methodenentwicklung zur mechanischen Fügbarkeit in wandlungsfähigen
Prozessketten'
- _id: '144'
name: TRR 285 - Subproject B05
publication: Procedia CIRP
publication_identifier:
issn:
- 2212-8271
publication_status: published
publisher: Elsevier BV
status: public
title: On the transferability of nominal surrogate models to uncertainty consideration
of clinch joint characteristics
type: journal_article
user_id: '107109'
volume: 129
year: '2024'
...
---
_id: '60304'
abstract:
- lang: eng
text: The focus towards multi-material and lightweight assemblies, driven by legal
requirements on reducing emissions and energy consumptions, reveals important
drawbacks and disadvantages of established joining processes, such as welding.
In this context, mechanical joining technologies, such as clinching, are becoming
more and more relevant especially in the automotive industry. However, the availability
of only few standards and almost none systematic design methods causes a still
very time- and cost-intensive assembly development process considering mainly
expert knowledge and a considerable amount of experimental studies. Motivated
by this, the presented work introduces a novel approach for the methodical design
and dimensioning of mechanically clinched assemblies. Therefore, the utilization
of regression models, such as machine learning algorithms, combined with manufacturing
knowledge ensures a reliable estimation of individual clinched joint characteristics.
In addition, the implementation of an engineering workbench enables the following
data-driven and knowledge-based generation of high-quality initial assembly designs
already in early product development phases. In a subsequent analysis and adjustment,
these designs are being improved while guaranteeing joining safety and loading
conformity. The presented results indicate that the methodological approach can
pave the way to a more systematic design process of mechanical joining assemblies,
which can significantly shorten the required number of iteration loops and therefore
the product development time.
author:
- first_name: Christoph
full_name: Zirngibl, Christoph
last_name: Zirngibl
- first_name: Sven
full_name: Martin, Sven
last_name: Martin
- first_name: Christian
full_name: Steinfelder, Christian
last_name: Steinfelder
- first_name: Benjamin
full_name: Schleich, Benjamin
last_name: Schleich
- first_name: Thomas
full_name: Tröster, Thomas
last_name: Tröster
- first_name: Alexander
full_name: Brosius, Alexander
last_name: Brosius
- first_name: Sandro
full_name: Wartzack, Sandro
last_name: Wartzack
citation:
ama: 'Zirngibl C, Martin S, Steinfelder C, et al. Methodical approach for the design
and dimensioning of mechanical clinched assemblies. In: Materials Research
Proceedings. Vol 25. Materials Research Forum LLC; 2023. doi:10.21741/9781644902417-23'
apa: Zirngibl, C., Martin, S., Steinfelder, C., Schleich, B., Tröster, T., Brosius,
A., & Wartzack, S. (2023). Methodical approach for the design and dimensioning
of mechanical clinched assemblies. Materials Research Proceedings, 25.
https://doi.org/10.21741/9781644902417-23
bibtex: '@inproceedings{Zirngibl_Martin_Steinfelder_Schleich_Tröster_Brosius_Wartzack_2023,
title={Methodical approach for the design and dimensioning of mechanical clinched
assemblies}, volume={25}, DOI={10.21741/9781644902417-23},
booktitle={Materials Research Proceedings}, publisher={Materials Research Forum
LLC}, author={Zirngibl, Christoph and Martin, Sven and Steinfelder, Christian
and Schleich, Benjamin and Tröster, Thomas and Brosius, Alexander and Wartzack,
Sandro}, year={2023} }'
chicago: Zirngibl, Christoph, Sven Martin, Christian Steinfelder, Benjamin Schleich,
Thomas Tröster, Alexander Brosius, and Sandro Wartzack. “Methodical Approach for
the Design and Dimensioning of Mechanical Clinched Assemblies.” In Materials
Research Proceedings, Vol. 25. Materials Research Forum LLC, 2023. https://doi.org/10.21741/9781644902417-23.
ieee: 'C. Zirngibl et al., “Methodical approach for the design and dimensioning
of mechanical clinched assemblies,” in Materials Research Proceedings,
Erlangen-Nürnberg, 2023, vol. 25, doi: 10.21741/9781644902417-23.'
mla: Zirngibl, Christoph, et al. “Methodical Approach for the Design and Dimensioning
of Mechanical Clinched Assemblies.” Materials Research Proceedings, vol.
25, Materials Research Forum LLC, 2023, doi:10.21741/9781644902417-23.
short: 'C. Zirngibl, S. Martin, C. Steinfelder, B. Schleich, T. Tröster, A. Brosius,
S. Wartzack, in: Materials Research Proceedings, Materials Research Forum LLC,
2023.'
conference:
end_date: 2023-04-05
location: Erlangen-Nürnberg
name: 20th International Conference on Sheet Metal
start_date: 2023-04-02
date_created: 2025-06-23T08:08:23Z
date_updated: 2025-06-23T08:15:07Z
department:
- _id: '630'
doi: 10.21741/9781644902417-23
intvolume: ' 25'
keyword:
- Joining
- Structural Analysis
- Machine Learning
language:
- iso: eng
project:
- _id: '130'
grant_number: '418701707'
name: 'TRR 285: TRR 285: Methodenentwicklung zur mechanischen Fügbarkeit in wandlungsfähigen
Prozessketten'
- _id: '132'
name: 'TRR 285 - B: TRR 285 - Project Area B'
- _id: '140'
name: 'TRR 285 – B01: TRR 285 - Subproject B01'
- _id: '144'
name: 'TRR 285 – B05: TRR 285 - Subproject B05'
publication: Materials Research Proceedings
publication_identifier:
issn:
- 2474-395X
publication_status: published
publisher: Materials Research Forum LLC
status: public
title: Methodical approach for the design and dimensioning of mechanical clinched
assemblies
type: conference
user_id: '104464'
volume: 25
year: '2023'
...
---
_id: '34415'
abstract:
- lang: eng
text: Challenges in the development of resource-efficient lightweight designs, such
as emission and cost targets in production, lead to an increasing demand for environmentally
friendly and fast joining processes. Therefore, cold-forming mechanical joining
techniques provide an energy-efficient alternative in comparison to established
processes, such as spot welding. However, to ensure a sufficient reliability of
the product design, not only the selection of an appropriate manufacturing and
joining method, but also the suitable dimensioning and validation of the entire
joining process is a crucial step. In this context, thermal processes offer a
large number of design principles while mechanical joining methods mainly require
extensive experimental tests and the inclusion of expert knowledge. Although few
contributions already investigated the data-based analysis of mechanical joints,
a system for the requirement- and manufacturing-oriented dimensioning of joining
components, such as different profiles and blanks, in combination with the estimation
of joint properties is not available yet. Motivated by this lack, this contribution
introduces an engineering workbench for the support of design engineers in the
early development phases of the knowledge and data-based design of mechanical
joining connections using clinching as an example. In this regard, the approach
is demonstrated involving a similar material and sheet thickness combination with
static loads.
author:
- first_name: Christoph
full_name: Zirngibl, Christoph
last_name: Zirngibl
- first_name: Christopher
full_name: Sauer, Christopher
last_name: Sauer
- first_name: Benjamin
full_name: Schleich, Benjamin
last_name: Schleich
- first_name: Sandro
full_name: Wartzack, Sandro
last_name: Wartzack
citation:
ama: 'Zirngibl C, Sauer C, Schleich B, Wartzack S. Knowledge and Data-Based Design
and Dimensioning of Mechanical Joining Connections. In: Volume 2: 42nd Computers
and Information in Engineering Conference (CIE). American Society of Mechanical
Engineers; 2022. doi:10.1115/detc2022-89172'
apa: 'Zirngibl, C., Sauer, C., Schleich, B., & Wartzack, S. (2022). Knowledge
and Data-Based Design and Dimensioning of Mechanical Joining Connections. Volume
2: 42nd Computers and Information in Engineering Conference (CIE). https://doi.org/10.1115/detc2022-89172'
bibtex: '@inproceedings{Zirngibl_Sauer_Schleich_Wartzack_2022, title={Knowledge
and Data-Based Design and Dimensioning of Mechanical Joining Connections}, DOI={10.1115/detc2022-89172}, booktitle={Volume
2: 42nd Computers and Information in Engineering Conference (CIE)}, publisher={American
Society of Mechanical Engineers}, author={Zirngibl, Christoph and Sauer, Christopher
and Schleich, Benjamin and Wartzack, Sandro}, year={2022} }'
chicago: 'Zirngibl, Christoph, Christopher Sauer, Benjamin Schleich, and Sandro
Wartzack. “Knowledge and Data-Based Design and Dimensioning of Mechanical Joining
Connections.” In Volume 2: 42nd Computers and Information in Engineering Conference
(CIE). American Society of Mechanical Engineers, 2022. https://doi.org/10.1115/detc2022-89172.'
ieee: 'C. Zirngibl, C. Sauer, B. Schleich, and S. Wartzack, “Knowledge and Data-Based
Design and Dimensioning of Mechanical Joining Connections,” 2022, doi: 10.1115/detc2022-89172.'
mla: 'Zirngibl, Christoph, et al. “Knowledge and Data-Based Design and Dimensioning
of Mechanical Joining Connections.” Volume 2: 42nd Computers and Information
in Engineering Conference (CIE), American Society of Mechanical Engineers,
2022, doi:10.1115/detc2022-89172.'
short: 'C. Zirngibl, C. Sauer, B. Schleich, S. Wartzack, in: Volume 2: 42nd Computers
and Information in Engineering Conference (CIE), American Society of Mechanical
Engineers, 2022.'
date_created: 2022-12-14T12:27:27Z
date_updated: 2022-12-14T12:29:33Z
doi: 10.1115/detc2022-89172
language:
- iso: eng
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: 'Volume 2: 42nd Computers and Information in Engineering Conference (CIE)'
publication_status: published
publisher: American Society of Mechanical Engineers
status: public
title: Knowledge and Data-Based Design and Dimensioning of Mechanical Joining Connections
type: conference
user_id: '7850'
year: '2022'
...
---
_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. AI. 2022;3(4):990-1006.
doi:10.3390/ai3040059
apa: Zirngibl, C., Schleich, B., & Wartzack, S. (2022). Estimation of Clinch
Joint Characteristics Based on Limited Input Data Using Pre-Trained Metamodels.
AI, 3(4), 990–1006. https://doi.org/10.3390/ai3040059
bibtex: '@article{Zirngibl_Schleich_Wartzack_2022, title={Estimation of Clinch Joint
Characteristics Based on Limited Input Data Using Pre-Trained Metamodels}, volume={3},
DOI={10.3390/ai3040059}, 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.” AI 3, no. 4 (2022): 990–1006. https://doi.org/10.3390/ai3040059.'
ieee: 'C. Zirngibl, B. Schleich, and S. Wartzack, “Estimation of Clinch Joint Characteristics
Based on Limited Input Data Using Pre-Trained Metamodels,” AI, vol. 3,
no. 4, pp. 990–1006, 2022, doi: 10.3390/ai3040059.'
mla: Zirngibl, Christoph, et al. “Estimation of Clinch Joint Characteristics Based
on Limited Input Data Using Pre-Trained Metamodels.” AI, vol. 3, no. 4,
MDPI AG, 2022, pp. 990–1006, doi:10.3390/ai3040059.
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'
...
---
_id: '34249'
abstract:
- lang: eng
text: The trend towards lightweight design, driven by increasingly stringent emission
targets, poses challenges to conventional joining processes due to the different
mechanical properties of the joining partners used to manufacture multi-material
systems. For this reason, new versatile joining processes are in demand for joining
dissimilar materials. In this regard, pin joining with cold extruded pin structures
is a relatively new, two-stage joining process for joining materials such as high-strength
steel and aluminium as well as steel and fibre-reinforced plastic to multi-material
systems, without the need for auxiliary elements. Due to the novelty of the process,
there are currently only a few studies on the robustness of this joining process
available. Thus, limited statements on the stability of the joining process considering
uncertain process conditions, such as varying material properties or friction
values, can be provided. Motivated by this, the presented work investigates the
influence of different uncertain process parameters on the pin extrusion as well
as on the joining process itself, carrying out a systematic robustness analysis.
Therefore, the methodical approach covers the complete process chain of pin joining,
including the load-bearing capacity of the joint by means of numerical simulation
and data-driven methods. Thereby, a deeper understanding of the pin joining process
is generated and the versatility of the novel joining process is increased. Additionally,
the provision of manufacturing recommendations for the forming of pin joints leads
to a significant decrease in the failure probability caused by ploughing or buckling
effects.
article_number: '122'
author:
- first_name: David
full_name: Römisch, David
last_name: Römisch
- 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
- first_name: Marion
full_name: Merklein, Marion
last_name: Merklein
citation:
ama: Römisch D, Zirngibl C, Schleich B, Wartzack S, Merklein M. Robustness Analysis
of Pin Joining. Journal of Manufacturing and Materials Processing. 2022;6(5).
doi:10.3390/jmmp6050122
apa: Römisch, D., Zirngibl, C., Schleich, B., Wartzack, S., & Merklein, M. (2022).
Robustness Analysis of Pin Joining. Journal of Manufacturing and Materials
Processing, 6(5), Article 122. https://doi.org/10.3390/jmmp6050122
bibtex: '@article{Römisch_Zirngibl_Schleich_Wartzack_Merklein_2022, title={Robustness
Analysis of Pin Joining}, volume={6}, DOI={10.3390/jmmp6050122},
number={5122}, journal={Journal of Manufacturing and Materials Processing}, publisher={MDPI
AG}, author={Römisch, David and Zirngibl, Christoph and Schleich, Benjamin and
Wartzack, Sandro and Merklein, Marion}, year={2022} }'
chicago: Römisch, David, Christoph Zirngibl, Benjamin Schleich, Sandro Wartzack,
and Marion Merklein. “Robustness Analysis of Pin Joining.” Journal of Manufacturing
and Materials Processing 6, no. 5 (2022). https://doi.org/10.3390/jmmp6050122.
ieee: 'D. Römisch, C. Zirngibl, B. Schleich, S. Wartzack, and M. Merklein, “Robustness
Analysis of Pin Joining,” Journal of Manufacturing and Materials Processing,
vol. 6, no. 5, Art. no. 122, 2022, doi: 10.3390/jmmp6050122.'
mla: Römisch, David, et al. “Robustness Analysis of Pin Joining.” Journal of
Manufacturing and Materials Processing, vol. 6, no. 5, 122, MDPI AG, 2022,
doi:10.3390/jmmp6050122.
short: D. Römisch, C. Zirngibl, B. Schleich, S. Wartzack, M. Merklein, Journal of
Manufacturing and Materials Processing 6 (2022).
date_created: 2022-12-06T19:03:30Z
date_updated: 2023-01-02T11:01:05Z
department:
- _id: '630'
doi: 10.3390/jmmp6050122
intvolume: ' 6'
issue: '5'
keyword:
- Industrial and Manufacturing Engineering
- Mechanical Engineering
- Mechanics of Materials
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://www.mdpi.com/2504-4494/6/5/122
oa: '1'
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'
- _id: '133'
name: 'TRR 285 - C: TRR 285 - Project Area C'
- _id: '145'
name: 'TRR 285 – C01: TRR 285 - Subproject C01'
publication: Journal of Manufacturing and Materials Processing
publication_identifier:
issn:
- 2504-4494
publication_status: published
publisher: MDPI AG
status: public
title: Robustness Analysis of Pin Joining
type: journal_article
user_id: '14931'
volume: 6
year: '2022'
...
---
_id: '34414'
abstract:
- lang: eng
text: 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.
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. Robust estimation of clinch joint characteristics
based on data-driven methods. The International Journal of Advanced Manufacturing
Technology. Published online 2022. doi:10.1007/s00170-022-10441-7
apa: Zirngibl, C., Schleich, B., & Wartzack, S. (2022). Robust estimation of
clinch joint characteristics based on data-driven methods. The International
Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/s00170-022-10441-7
bibtex: '@article{Zirngibl_Schleich_Wartzack_2022, title={Robust estimation of clinch
joint characteristics based on data-driven methods}, DOI={10.1007/s00170-022-10441-7},
journal={The International Journal of Advanced Manufacturing Technology}, publisher={Springer
Science and Business Media LLC}, author={Zirngibl, Christoph and Schleich, Benjamin
and Wartzack, Sandro}, year={2022} }'
chicago: Zirngibl, Christoph, Benjamin Schleich, and Sandro Wartzack. “Robust Estimation
of Clinch Joint Characteristics Based on Data-Driven Methods.” The International
Journal of Advanced Manufacturing Technology, 2022. https://doi.org/10.1007/s00170-022-10441-7.
ieee: 'C. Zirngibl, B. Schleich, and S. Wartzack, “Robust estimation of clinch joint
characteristics based on data-driven methods,” The International Journal of
Advanced Manufacturing Technology, 2022, doi: 10.1007/s00170-022-10441-7.'
mla: Zirngibl, Christoph, et al. “Robust Estimation of Clinch Joint Characteristics
Based on Data-Driven Methods.” The International Journal of Advanced Manufacturing
Technology, Springer Science and Business Media LLC, 2022, doi:10.1007/s00170-022-10441-7.
short: C. Zirngibl, B. Schleich, S. Wartzack, The International Journal of Advanced
Manufacturing Technology (2022).
date_created: 2022-12-14T12:24:29Z
date_updated: 2023-01-02T11:14:26Z
department:
- _id: '630'
doi: 10.1007/s00170-022-10441-7
keyword:
- Industrial and Manufacturing Engineering
- Computer Science Applications
- Mechanical Engineering
- Software
- Control and Systems Engineering
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://link.springer.com/article/10.1007/s00170-022-10441-7
oa: '1'
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: The International Journal of Advanced Manufacturing Technology
publication_identifier:
issn:
- 0268-3768
- 1433-3015
publication_status: published
publisher: Springer Science and Business Media LLC
status: public
title: Robust estimation of clinch joint characteristics based on data-driven methods
type: journal_article
user_id: '14931'
year: '2022'
...
---
_id: '30100'
abstract:
- lang: eng
text: Since the application of mechanical joining methods, such as clinching or
riveting, offers a robust solution for the generation of advanced multi-material
connections, the use in the field of lightweight designs (e.g. automotive industry)
is steadily increasing. Therefore, not only the design of an individual joint
is required but also the dimensioning of the entire joining connection is crucial.
However, in comparison to thermal joining techniques, such as spot welding, the
evaluation of the joints’ resistance against defined requirements (e.g. types
of load, minimal amount of load cycles) mainly relies on the consideration of
expert knowledge, a few design principles and a small amount of experimental data.
Since this generally implies the involvement of several domains, such as the material
characterization or the part design, a tremendous amount of data and knowledge
is separately generated for a certain dimensioning process. Nevertheless, the
lack of formalization and standardization in representing the gained knowledge
leads to a difficult and inconsistent reuse, sharing or searching of already existing
information. Thus, this contribution presents a specific ontology for the provision
of cross-domain knowledge about mechanical joining processes and highlights two
potential use cases of this ontology in the design of clinched and pin joints.
author:
- first_name: Christoph
full_name: Zirngibl, Christoph
last_name: Zirngibl
- first_name: Patricia
full_name: Kügler, Patricia
last_name: Kügler
- first_name: Julian
full_name: Popp, Julian
last_name: Popp
- first_name: Christian Roman
full_name: Bielak, Christian Roman
id: '34782'
last_name: Bielak
- first_name: Mathias
full_name: Bobbert, Mathias
id: '7850'
last_name: Bobbert
- first_name: Dietmar
full_name: Drummer, Dietmar
last_name: Drummer
- first_name: Gerson
full_name: Meschut, Gerson
id: '32056'
last_name: Meschut
orcid: 0000-0002-2763-1246
- first_name: Sandro
full_name: Wartzack, Sandro
last_name: Wartzack
- first_name: Benjamin
full_name: Schleich, Benjamin
last_name: Schleich
citation:
ama: Zirngibl C, Kügler P, Popp J, et al. Provision of cross-domain knowledge in
mechanical joining using ontologies. Production Engineering. Published
online 2022. doi:10.1007/s11740-022-01117-y
apa: Zirngibl, C., Kügler, P., Popp, J., Bielak, C. R., Bobbert, M., Drummer, D.,
Meschut, G., Wartzack, S., & Schleich, B. (2022). Provision of cross-domain
knowledge in mechanical joining using ontologies. Production Engineering.
https://doi.org/10.1007/s11740-022-01117-y
bibtex: '@article{Zirngibl_Kügler_Popp_Bielak_Bobbert_Drummer_Meschut_Wartzack_Schleich_2022,
title={Provision of cross-domain knowledge in mechanical joining using ontologies},
DOI={10.1007/s11740-022-01117-y},
journal={Production Engineering}, publisher={Springer Science and Business Media
LLC}, author={Zirngibl, Christoph and Kügler, Patricia and Popp, Julian and Bielak,
Christian Roman and Bobbert, Mathias and Drummer, Dietmar and Meschut, Gerson
and Wartzack, Sandro and Schleich, Benjamin}, year={2022} }'
chicago: Zirngibl, Christoph, Patricia Kügler, Julian Popp, Christian Roman Bielak,
Mathias Bobbert, Dietmar Drummer, Gerson Meschut, Sandro Wartzack, and Benjamin
Schleich. “Provision of Cross-Domain Knowledge in Mechanical Joining Using Ontologies.”
Production Engineering, 2022. https://doi.org/10.1007/s11740-022-01117-y.
ieee: 'C. Zirngibl et al., “Provision of cross-domain knowledge in mechanical
joining using ontologies,” Production Engineering, 2022, doi: 10.1007/s11740-022-01117-y.'
mla: Zirngibl, Christoph, et al. “Provision of Cross-Domain Knowledge in Mechanical
Joining Using Ontologies.” Production Engineering, Springer Science and
Business Media LLC, 2022, doi:10.1007/s11740-022-01117-y.
short: C. Zirngibl, P. Kügler, J. Popp, C.R. Bielak, M. Bobbert, D. Drummer, G.
Meschut, S. Wartzack, B. Schleich, Production Engineering (2022).
date_created: 2022-02-25T07:19:45Z
date_updated: 2023-04-27T07:42:19Z
department:
- _id: '157'
doi: 10.1007/s11740-022-01117-y
keyword:
- Industrial and Manufacturing Engineering
- Mechanical Engineering
language:
- iso: eng
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'
- _id: '133'
name: 'TRR 285 - C: TRR 285 - Project Area C'
- _id: '145'
name: 'TRR 285 – C01: TRR 285 - Subproject C01'
- _id: '131'
name: 'TRR 285 - A: TRR 285 - Project Area A'
- _id: '135'
name: 'TRR 285 – A01: TRR 285 - Subproject A01'
publication: Production Engineering
publication_identifier:
issn:
- 0944-6524
- 1863-7353
publication_status: published
publisher: Springer Science and Business Media LLC
quality_controlled: '1'
status: public
title: Provision of cross-domain knowledge in mechanical joining using ontologies
type: journal_article
user_id: '7850'
year: '2022'
...
---
_id: '34244'
author:
- first_name: Fabian
full_name: Kappe, Fabian
last_name: Kappe
- first_name: Christoph
full_name: Zirngibl, Christoph
last_name: Zirngibl
- first_name: Benjamin
full_name: Schleich, Benjamin
last_name: Schleich
- first_name: Mathias
full_name: Bobbert, Mathias
last_name: Bobbert
- first_name: Sandro
full_name: Wartzack, Sandro
last_name: Wartzack
- first_name: Gerson
full_name: Meschut, Gerson
last_name: Meschut
citation:
ama: Kappe F, Zirngibl C, Schleich B, Bobbert M, Wartzack S, Meschut G. Determining
the influence of different process parameters on the versatile self-piercing riveting
process using numerical methods. Journal of Manufacturing Processes. 2022;84:1438-1448.
doi:10.1016/j.jmapro.2022.11.019
apa: Kappe, F., Zirngibl, C., Schleich, B., Bobbert, M., Wartzack, S., & Meschut,
G. (2022). Determining the influence of different process parameters on the versatile
self-piercing riveting process using numerical methods. Journal of Manufacturing
Processes, 84, 1438–1448. https://doi.org/10.1016/j.jmapro.2022.11.019
bibtex: '@article{Kappe_Zirngibl_Schleich_Bobbert_Wartzack_Meschut_2022, title={Determining
the influence of different process parameters on the versatile self-piercing riveting
process using numerical methods}, volume={84}, DOI={10.1016/j.jmapro.2022.11.019},
journal={Journal of Manufacturing Processes}, publisher={Elsevier BV}, author={Kappe,
Fabian and Zirngibl, Christoph and Schleich, Benjamin and Bobbert, Mathias and
Wartzack, Sandro and Meschut, Gerson}, year={2022}, pages={1438–1448} }'
chicago: 'Kappe, Fabian, Christoph Zirngibl, Benjamin Schleich, Mathias Bobbert,
Sandro Wartzack, and Gerson Meschut. “Determining the Influence of Different Process
Parameters on the Versatile Self-Piercing Riveting Process Using Numerical Methods.”
Journal of Manufacturing Processes 84 (2022): 1438–48. https://doi.org/10.1016/j.jmapro.2022.11.019.'
ieee: 'F. Kappe, C. Zirngibl, B. Schleich, M. Bobbert, S. Wartzack, and G. Meschut,
“Determining the influence of different process parameters on the versatile self-piercing
riveting process using numerical methods,” Journal of Manufacturing Processes,
vol. 84, pp. 1438–1448, 2022, doi: 10.1016/j.jmapro.2022.11.019.'
mla: Kappe, Fabian, et al. “Determining the Influence of Different Process Parameters
on the Versatile Self-Piercing Riveting Process Using Numerical Methods.” Journal
of Manufacturing Processes, vol. 84, Elsevier BV, 2022, pp. 1438–48, doi:10.1016/j.jmapro.2022.11.019.
short: F. Kappe, C. Zirngibl, B. Schleich, M. Bobbert, S. Wartzack, G. Meschut,
Journal of Manufacturing Processes 84 (2022) 1438–1448.
date_created: 2022-12-06T13:57:46Z
date_updated: 2023-04-27T08:53:36Z
department:
- _id: '157'
- _id: '630'
doi: 10.1016/j.jmapro.2022.11.019
intvolume: ' 84'
keyword:
- Industrial and Manufacturing Engineering
- Management Science and Operations Research
- Strategy and Management
language:
- iso: eng
page: 1438-1448
project:
- _id: '130'
grant_number: '418701707'
name: 'TRR 285: TRR 285'
- _id: '133'
name: 'TRR 285 - C: TRR 285 - Project Area C'
- _id: '146'
name: 'TRR 285 – C02: TRR 285 - Subproject C02'
- _id: '132'
name: 'TRR 285 - B: TRR 285 - Project Area B'
- _id: '144'
name: 'TRR 285 – B05: TRR 285 - Subproject B05'
publication: Journal of Manufacturing Processes
publication_identifier:
issn:
- 1526-6125
publication_status: published
publisher: Elsevier BV
quality_controlled: '1'
status: public
title: Determining the influence of different process parameters on the versatile
self-piercing riveting process using numerical methods
type: journal_article
user_id: '66459'
volume: 84
year: '2022'
...
---
_id: '30650'
abstract:
- lang: eng
text: Due to increasingly strict emission targets and regulatory requirements, especially
for companies in the transport industry, the demand for multi-material-systems
is continuously rising in order to lower energy consumption. In this context,
mechanical joining processes offer an environmentally friendly and flexible alternative
to established joining methods, especially in the field of lightweight design.
For example, cold-formed cylindrical pin structures show high potentials in joining
multi-material-systems without auxiliary elements. The pin structures are joined
either by pressing them directly into the joining partner or by caulking with
a pre-punched part. However, to evaluate the strength of the joint and to ensure
the joining reliability for versatile processes, such as changing joining partners
or batch variations, engineering designers currently have only limited design
principles available compared to thermal joining processes. Consequently, the
design of an optimal pin joint requires cost- and time-intensive experimental
investigations and adjustments to design or process parameters. As a solution,
data-driven methods offer procedures for structuring data and identifying dependencies
between varying process parameters and resulting pin structure characteristics.
Motivated by this, the paper presents an approach for the data-driven analysis
of cold-formed pin structures and offers a deeper understanding of how versatile
processes affect the pin characteristics. Therefore, the application of an intelligent
design of experiment in combination with several machine learning methods enable
the setup of a best-fitting meta-model. Resulting, the determination of a mathematical
model provides the opportunity to accurately estimate the pin height considering
only relevant geometrical and process parameters with a prediction quality of
95 %.
author:
- first_name: D.
full_name: Römisch, D.
last_name: Römisch
- first_name: C.
full_name: Zirngibl, C.
last_name: Zirngibl
- first_name: B.
full_name: Schleich, B.
last_name: Schleich
- first_name: S.
full_name: Wartzack, S.
last_name: Wartzack
- first_name: M.
full_name: Merklein, M.
last_name: Merklein
citation:
ama: 'Römisch D, Zirngibl C, Schleich B, Wartzack S, Merklein M. Data-driven analysis
of cold-formed pin structure characteristics in the context of versatile joining
processes. IOP Conference Series: Materials Science and Engineering. 2021;1157:012077.
doi:10.1088/1757-899X/1157/1/012077'
apa: 'Römisch, D., Zirngibl, C., Schleich, B., Wartzack, S., & Merklein, M.
(2021). Data-driven analysis of cold-formed pin structure characteristics in the
context of versatile joining processes. IOP Conference Series: Materials Science
and Engineering, 1157, 012077. https://doi.org/10.1088/1757-899X/1157/1/012077'
bibtex: '@article{Römisch_Zirngibl_Schleich_Wartzack_Merklein_2021, title={Data-driven
analysis of cold-formed pin structure characteristics in the context of versatile
joining processes}, volume={1157}, DOI={10.1088/1757-899X/1157/1/012077},
journal={IOP Conference Series: Materials Science and Engineering}, author={Römisch,
D. and Zirngibl, C. and Schleich, B. and Wartzack, S. and Merklein, M.}, year={2021},
pages={012077} }'
chicago: 'Römisch, D., C. Zirngibl, B. Schleich, S. Wartzack, and M. Merklein. “Data-Driven
Analysis of Cold-Formed Pin Structure Characteristics in the Context of Versatile
Joining Processes.” IOP Conference Series: Materials Science and Engineering
1157 (2021): 012077. https://doi.org/10.1088/1757-899X/1157/1/012077.'
ieee: 'D. Römisch, C. Zirngibl, B. Schleich, S. Wartzack, and M. Merklein, “Data-driven
analysis of cold-formed pin structure characteristics in the context of versatile
joining processes,” IOP Conference Series: Materials Science and Engineering,
vol. 1157, p. 012077, 2021, doi: 10.1088/1757-899X/1157/1/012077.'
mla: 'Römisch, D., et al. “Data-Driven Analysis of Cold-Formed Pin Structure Characteristics
in the Context of Versatile Joining Processes.” IOP Conference Series: Materials
Science and Engineering, vol. 1157, 2021, p. 012077, doi:10.1088/1757-899X/1157/1/012077.'
short: 'D. Römisch, C. Zirngibl, B. Schleich, S. Wartzack, M. Merklein, IOP Conference
Series: Materials Science and Engineering 1157 (2021) 012077.'
date_created: 2022-03-28T12:48:01Z
date_updated: 2022-03-29T15:45:44Z
doi: 10.1088/1757-899X/1157/1/012077
intvolume: ' 1157'
language:
- iso: eng
page: '012077'
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'
- _id: '133'
name: 'TRR 285 - C: TRR 285 - Project Area C'
- _id: '145'
name: 'TRR 285 – C01: TRR 285 - Subproject C01'
publication: 'IOP Conference Series: Materials Science and Engineering'
status: public
title: Data-driven analysis of cold-formed pin structure characteristics in the context
of versatile joining processes
type: journal_article
user_id: '68518'
volume: 1157
year: '2021'
...
---
_id: '30696'
author:
- first_name: C.
full_name: Zirngibl, C.
last_name: Zirngibl
- first_name: B.
full_name: Schleich, B.
last_name: Schleich
- first_name: S.
full_name: Wartzack, S.
last_name: Wartzack
citation:
ama: Zirngibl C, Schleich B, Wartzack S. Approach for the automated and data-based
design of mechanical joints. Proceedings of the Design Society. 2021;1:521.
doi:10.1017/pds.2021.52
apa: Zirngibl, C., Schleich, B., & Wartzack, S. (2021). Approach for the automated
and data-based design of mechanical joints. Proceedings of the Design Society,
1, 521. https://doi.org/10.1017/pds.2021.52
bibtex: '@article{Zirngibl_Schleich_Wartzack_2021, title={Approach for the automated
and data-based design of mechanical joints}, volume={1}, DOI={10.1017/pds.2021.52},
journal={Proceedings of the Design Society}, author={Zirngibl, C. and Schleich,
B. and Wartzack, S.}, year={2021}, pages={521} }'
chicago: 'Zirngibl, C., B. Schleich, and S. Wartzack. “Approach for the Automated
and Data-Based Design of Mechanical Joints.” Proceedings of the Design Society
1 (2021): 521. https://doi.org/10.1017/pds.2021.52.'
ieee: 'C. Zirngibl, B. Schleich, and S. Wartzack, “Approach for the automated and
data-based design of mechanical joints,” Proceedings of the Design Society,
vol. 1, p. 521, 2021, doi: 10.1017/pds.2021.52.'
mla: Zirngibl, C., et al. “Approach for the Automated and Data-Based Design of Mechanical
Joints.” Proceedings of the Design Society, vol. 1, 2021, p. 521, doi:10.1017/pds.2021.52.
short: C. Zirngibl, B. Schleich, S. Wartzack, Proceedings of the Design Society
1 (2021) 521.
date_created: 2022-03-29T09:12:58Z
date_updated: 2023-01-02T11:19:35Z
department:
- _id: '630'
doi: 10.1017/pds.2021.52
intvolume: ' 1'
language:
- iso: eng
page: '521'
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: Proceedings of the Design Society
status: public
title: Approach for the automated and data-based design of mechanical joints
type: journal_article
user_id: '14931'
volume: 1
year: '2021'
...
---
_id: '30700'
author:
- first_name: C.
full_name: Zirngibl, C.
last_name: Zirngibl
- first_name: F.
full_name: Dworschak, F.
last_name: Dworschak
- first_name: B.
full_name: Schleich, B.
last_name: Schleich
- first_name: S.
full_name: Wartzack, S.
last_name: Wartzack
citation:
ama: Zirngibl C, Dworschak F, Schleich B, Wartzack S. Application of reinforcement
learning for the optimization of clinch joint characteristics. Production Engineering.
Published online 2021. doi:10.1007/s11740-021-01098-4
apa: Zirngibl, C., Dworschak, F., Schleich, B., & Wartzack, S. (2021). Application
of reinforcement learning for the optimization of clinch joint characteristics.
Production Engineering. https://doi.org/10.1007/s11740-021-01098-4
bibtex: '@article{Zirngibl_Dworschak_Schleich_Wartzack_2021, title={Application
of reinforcement learning for the optimization of clinch joint characteristics},
DOI={10.1007/s11740-021-01098-4},
journal={Production Engineering}, author={Zirngibl, C. and Dworschak, F. and Schleich,
B. and Wartzack, S.}, year={2021} }'
chicago: Zirngibl, C., F. Dworschak, B. Schleich, and S. Wartzack. “Application
of Reinforcement Learning for the Optimization of Clinch Joint Characteristics.”
Production Engineering, 2021. https://doi.org/10.1007/s11740-021-01098-4.
ieee: 'C. Zirngibl, F. Dworschak, B. Schleich, and S. Wartzack, “Application of
reinforcement learning for the optimization of clinch joint characteristics,”
Production Engineering, 2021, doi: 10.1007/s11740-021-01098-4.'
mla: Zirngibl, C., et al. “Application of Reinforcement Learning for the Optimization
of Clinch Joint Characteristics.” Production Engineering, 2021, doi:10.1007/s11740-021-01098-4.
short: C. Zirngibl, F. Dworschak, B. Schleich, S. Wartzack, Production Engineering
(2021).
date_created: 2022-03-29T09:19:07Z
date_updated: 2023-01-02T11:19:55Z
department:
- _id: '630'
doi: 10.1007/s11740-021-01098-4
language:
- iso: eng
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: Production Engineering
status: public
title: Application of reinforcement learning for the optimization of clinch joint
characteristics
type: journal_article
user_id: '14931'
year: '2021'
...
---
_id: '30695'
abstract:
- lang: eng
text: 'Due to their cost-efficiency and environmental friendliness, the demand of
mechanical joining processes is constantly rising. However, the dimensioning and
design of joints and suitable processes are mainly based on expert knowledge and
few experimental data. Therefore, the performance of numerical and experimental
studies enables the generation of optimized joining geometries. However, the manual
evaluation of the results of such studies is often highly time-consuming. As a
novel solution, image segmentation and machine learning algorithm provide methods
to automate the analysis process. Motivated by this, the paper presents an approach
for the automated analysis of geometrical characteristics using clinching as an
example. '
author:
- first_name: C.
full_name: Zirngibl, C.
last_name: Zirngibl
- first_name: B.
full_name: Schleich, B.
last_name: Schleich
citation:
ama: Zirngibl C, Schleich B. Approach for the automated analysis of geometrical
clinch joint characteristics. Key Engineering Materials. 2021;883 KEM:105.
doi:10.4028/www.scientific.net/KEM.883.105
apa: Zirngibl, C., & Schleich, B. (2021). Approach for the automated analysis
of geometrical clinch joint characteristics. Key Engineering Materials,
883 KEM, 105. https://doi.org/10.4028/www.scientific.net/KEM.883.105
bibtex: '@article{Zirngibl_Schleich_2021, title={Approach for the automated analysis
of geometrical clinch joint characteristics}, volume={883 KEM}, DOI={10.4028/www.scientific.net/KEM.883.105},
journal={Key Engineering Materials}, author={Zirngibl, C. and Schleich, B.}, year={2021},
pages={105} }'
chicago: 'Zirngibl, C., and B. Schleich. “Approach for the Automated Analysis of
Geometrical Clinch Joint Characteristics.” Key Engineering Materials 883
KEM (2021): 105. https://doi.org/10.4028/www.scientific.net/KEM.883.105.'
ieee: 'C. Zirngibl and B. Schleich, “Approach for the automated analysis of geometrical
clinch joint characteristics,” Key Engineering Materials, vol. 883 KEM,
p. 105, 2021, doi: 10.4028/www.scientific.net/KEM.883.105.'
mla: Zirngibl, C., and B. Schleich. “Approach for the Automated Analysis of Geometrical
Clinch Joint Characteristics.” Key Engineering Materials, vol. 883 KEM,
2021, p. 105, doi:10.4028/www.scientific.net/KEM.883.105.
short: C. Zirngibl, B. Schleich, Key Engineering Materials 883 KEM (2021) 105.
date_created: 2022-03-29T09:09:51Z
date_updated: 2023-01-02T11:51:41Z
department:
- _id: '630'
doi: 10.4028/www.scientific.net/KEM.883.105
language:
- iso: eng
page: '105'
project:
- _id: '144'
name: 'TRR 285 – B05: TRR 285 - Subproject B05'
- _id: '130'
grant_number: '418701707'
name: 'TRR 285: TRR 285'
- _id: '132'
name: 'TRR 285 - B: TRR 285 - Project Area B'
publication: Key Engineering Materials
status: public
title: Approach for the automated analysis of geometrical clinch joint characteristics
type: journal_article
user_id: '14931'
volume: 883 KEM
year: '2021'
...
---
_id: '30710'
author:
- first_name: C.
full_name: Zirngibl, C.
last_name: Zirngibl
- first_name: B.
full_name: Schleich, B.
last_name: Schleich
- first_name: S.
full_name: Wartzack, S.
last_name: Wartzack
citation:
ama: Zirngibl C, Schleich B, Wartzack S. Potentiale datengestützter Methoden zur
Gestaltung und Optimierung mechanischer Fügeverbindungen. Proceedings of the
31st Symposium Design for X (DFX2020). Published online 2020. doi:10.35199/dfx2020.8
apa: Zirngibl, C., Schleich, B., & Wartzack, S. (2020). Potentiale datengestützter
Methoden zur Gestaltung und Optimierung mechanischer Fügeverbindungen. Proceedings
of the 31st Symposium Design for X (DFX2020). https://doi.org/10.35199/dfx2020.8
bibtex: '@article{Zirngibl_Schleich_Wartzack_2020, title={Potentiale datengestützter
Methoden zur Gestaltung und Optimierung mechanischer Fügeverbindungen}, DOI={10.35199/dfx2020.8}, journal={Proceedings
of the 31st Symposium Design for X (DFX2020)}, author={Zirngibl, C. and Schleich,
B. and Wartzack, S.}, year={2020} }'
chicago: Zirngibl, C., B. Schleich, and S. Wartzack. “Potentiale Datengestützter
Methoden Zur Gestaltung Und Optimierung Mechanischer Fügeverbindungen.” Proceedings
of the 31st Symposium Design for X (DFX2020), 2020. https://doi.org/10.35199/dfx2020.8.
ieee: 'C. Zirngibl, B. Schleich, and S. Wartzack, “Potentiale datengestützter Methoden
zur Gestaltung und Optimierung mechanischer Fügeverbindungen,” Proceedings
of the 31st Symposium Design for X (DFX2020), 2020, doi: 10.35199/dfx2020.8.'
mla: Zirngibl, C., et al. “Potentiale Datengestützter Methoden Zur Gestaltung Und
Optimierung Mechanischer Fügeverbindungen.” Proceedings of the 31st Symposium
Design for X (DFX2020), 2020, doi:10.35199/dfx2020.8.
short: C. Zirngibl, B. Schleich, S. Wartzack, Proceedings of the 31st Symposium
Design for X (DFX2020) (2020).
date_created: 2022-03-29T09:34:55Z
date_updated: 2023-01-02T12:00:27Z
department:
- _id: '630'
doi: 10.35199/dfx2020.8
language:
- iso: eng
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: Proceedings of the 31st Symposium Design for X (DFX2020)
status: public
title: Potentiale datengestützter Methoden zur Gestaltung und Optimierung mechanischer
Fügeverbindungen
type: journal_article
user_id: '14931'
year: '2020'
...