---
_id: '58491'
abstract:
- lang: eng
text: Similar to bulk metal forming, clinch joining is characterised by
large plastic deformations and a variety of different 3D stress states, including
severe compression. However, inherent to plastic forming is the nucleation and
growth of defects, whose detrimental effects on the material behaviour can be
described by continuum damage models and eventually lead to material failure.
As the damage evolution strongly depends on the stress state, a stress-state-dependent
model is utilised to correctly track the accumulation. To formulate and parameterise
this model, besides classical experiments, so-called modified punch tests are
also integrated herein to enhance the calibration of the failure model by capturing
a larger range of stress states and metal-forming-specific loading conditions.
Moreover, when highly ductile materials are considered, such as the dual-phase
steel HCT590X and the aluminium alloy EN AW-6014 T4 investigated here, strong
necking and localisation might occur prior to fracture. This can alter the stress
state and affect the actual strain at failure. This influence is captured by coupling
plasticity and damage to incorporate the damage-induced softening effect. Its
relative importance is shown by conducting inverse parameter identifications to
determine damage and failure parameters for both mentioned ductile metals based
on up to 12 different experiments.
article_number: '157'
author:
- first_name: Johannes
full_name: Friedlein, Johannes
last_name: Friedlein
- first_name: Max
full_name: Böhnke, Max
last_name: Böhnke
- first_name: Malte
full_name: Schlichter, Malte
last_name: Schlichter
- first_name: Mathias
full_name: Bobbert, Mathias
last_name: Bobbert
- first_name: Gerson
full_name: Meschut, Gerson
last_name: Meschut
- first_name: Julia
full_name: Mergheim, Julia
last_name: Mergheim
- first_name: Paul
full_name: Steinmann, Paul
last_name: Steinmann
citation:
ama: Friedlein J, Böhnke M, Schlichter M, et al. Material Parameter Identification
for a Stress-State-Dependent Ductile Damage and Failure Model Applied to Clinch
Joining. Journal of Manufacturing and Materials Processing. 2024;8(4).
doi:10.3390/jmmp8040157
apa: Friedlein, J., Böhnke, M., Schlichter, M., Bobbert, M., Meschut, G., Mergheim,
J., & Steinmann, P. (2024). Material Parameter Identification for a Stress-State-Dependent
Ductile Damage and Failure Model Applied to Clinch Joining. Journal of Manufacturing
and Materials Processing, 8(4), Article 157. https://doi.org/10.3390/jmmp8040157
bibtex: '@article{Friedlein_Böhnke_Schlichter_Bobbert_Meschut_Mergheim_Steinmann_2024,
title={Material Parameter Identification for a Stress-State-Dependent Ductile
Damage and Failure Model Applied to Clinch Joining}, volume={8}, DOI={10.3390/jmmp8040157},
number={4157}, journal={Journal of Manufacturing and Materials Processing}, publisher={MDPI
AG}, author={Friedlein, Johannes and Böhnke, Max and Schlichter, Malte and Bobbert,
Mathias and Meschut, Gerson and Mergheim, Julia and Steinmann, Paul}, year={2024}
}'
chicago: Friedlein, Johannes, Max Böhnke, Malte Schlichter, Mathias Bobbert, Gerson
Meschut, Julia Mergheim, and Paul Steinmann. “Material Parameter Identification
for a Stress-State-Dependent Ductile Damage and Failure Model Applied to Clinch
Joining.” Journal of Manufacturing and Materials Processing 8, no. 4 (2024).
https://doi.org/10.3390/jmmp8040157.
ieee: 'J. Friedlein et al., “Material Parameter Identification for a Stress-State-Dependent
Ductile Damage and Failure Model Applied to Clinch Joining,” Journal of Manufacturing
and Materials Processing, vol. 8, no. 4, Art. no. 157, 2024, doi: 10.3390/jmmp8040157.'
mla: Friedlein, Johannes, et al. “Material Parameter Identification for a Stress-State-Dependent
Ductile Damage and Failure Model Applied to Clinch Joining.” Journal of Manufacturing
and Materials Processing, vol. 8, no. 4, 157, MDPI AG, 2024, doi:10.3390/jmmp8040157.
short: J. Friedlein, M. Böhnke, M. Schlichter, M. Bobbert, G. Meschut, J. Mergheim,
P. Steinmann, Journal of Manufacturing and Materials Processing 8 (2024).
date_created: 2025-01-31T16:59:13Z
date_updated: 2025-01-31T17:03:34Z
doi: 10.3390/jmmp8040157
intvolume: ' 8'
issue: '4'
keyword:
- ductile damage
- stress-state dependency
- failure
- parameter identification
- punch test
- clinching
language:
- iso: eng
project:
- _id: '130'
grant_number: '418701707'
name: 'TRR 285: TRR 285: Methodenentwicklung zur mechanischen Fügbarkeit in wandlungsfähigen
Prozessketten'
- _id: '131'
name: 'TRR 285 - A: TRR 285 - Project Area A'
- _id: '139'
name: 'TRR 285 – A05: TRR 285 - Subproject A05'
publication: Journal of Manufacturing and Materials Processing
publication_identifier:
issn:
- 2504-4494
publication_status: published
publisher: MDPI AG
status: public
title: Material Parameter Identification for a Stress-State-Dependent Ductile Damage
and Failure Model Applied to Clinch Joining
type: journal_article
user_id: '84990'
volume: 8
year: '2024'
...
---
_id: '59237'
abstract:
- lang: eng
text: Batch and process fluctuations during the fabrication of sheet metal components
result in discrepancies in the resulting component properties, affecting subsequent
process steps and potentially leading to production rejects. Consequently, the
identification of deviations and knowledge of the effects of fluctuations are
crucial for achieving consistently high product quality, reducing waste and thus
increasing resource efficiency of production processes through countermeasures
derived from this. The approach presented to address this is the use of data-driven
metamodeling to map entire process chains and predict process parameters in order
to compensate for process and batch fluctuation. The investigated process chain
consists of the sub-processes deep drawing, clamping and clinching. For each process
step, relevant input and output variables are identified, numerical simulation
models are created, and subsequently validated. Variant simulations of the sub-processes
are conducted and evaluated to generate a database for the metamodeling of the
individual process steps. Machine learning techniques are utilized for the automated
selection and optimization of learning methods to create models that depict the
relationships between input and output variables. Finally, the models for the
sub-processes are linked together to form a superordinate metamodel for the entire
process chain, with the aim to make inline-process adaptations possible.
15th Forming Technology Forum. ; 2024.'
apa: Neumann, J., Kappis, L., Lontsi, S. T.-N., Ludwig, J.-P., Ramaiya, U. B., Scharr,
C., Vallaster, E., Flügge, W., Meschut, G., & Merklein, M. (2024). An approach
for a metamodel-based consideration of a process chain when mechanically joining
sheet metal components. 15th Forming Technology Forum.
bibtex: '@inproceedings{Neumann_Kappis_Lontsi_Ludwig_Ramaiya_Scharr_Vallaster_Flügge_Meschut_Merklein_2024,
title={An approach for a metamodel-based consideration of a process chain when
mechanically joining sheet metal components}, booktitle={15th Forming Technology
Forum}, author={Neumann, Jonas and Kappis, Lukas and Lontsi, Seraphin Tsi-Nda
and Ludwig, Jean-Patrick and Ramaiya, Umang Bharatkumar and Scharr, Christian
and Vallaster, Eva and Flügge, Wilko and Meschut, Gerson and Merklein, Marion},
year={2024} }'
chicago: Neumann, Jonas, Lukas Kappis, Seraphin Tsi-Nda Lontsi, Jean-Patrick Ludwig,
Umang Bharatkumar Ramaiya, Christian Scharr, Eva Vallaster, Wilko Flügge, Gerson
Meschut, and Marion Merklein. “An Approach for a Metamodel-Based Consideration
of a Process Chain When Mechanically Joining Sheet Metal Components.” In 15th
Forming Technology Forum, 2024.
ieee: J. Neumann et al., “An approach for a metamodel-based consideration
of a process chain when mechanically joining sheet metal components,” 2024.
mla: Neumann, Jonas, et al. “An Approach for a Metamodel-Based Consideration of
a Process Chain When Mechanically Joining Sheet Metal Components.” 15th Forming
Technology Forum, 2024.
short: 'J. Neumann, L. Kappis, S.T.-N. Lontsi, J.-P. Ludwig, U.B. Ramaiya, C. Scharr,
E. Vallaster, W. Flügge, G. Meschut, M. Merklein, in: 15th Forming Technology
Forum, 2024.'
date_created: 2025-04-02T07:03:13Z
date_updated: 2025-05-13T07:23:13Z
language:
- iso: eng
publication: 15th Forming Technology Forum
status: public
title: An approach for a metamodel-based consideration of a process chain when mechanically
joining sheet metal components
type: conference
user_id: '76631'
year: '2024'
...
---
_id: '60047'
abstract:
- lang: eng
text: "Abstract\r\n Purpose\r\n
\ Cardiopulmonary exercise testing (CPET) is considered
the gold standard for assessing cardiorespiratory fitness. To ensure consistent
performance of each test, it is necessary to adapt the power increase of the test
protocol to the physical characteristics of each individual. This study aimed
to use machine learning models to determine individualized ramp protocols based
on non-exercise features. We hypothesized that machine learning models will predict
peak oxygen uptake ($$\\dot{V}$$\r\n \r\n
\ V\r\n ˙\r\n
\ \r\n O2peak)
and peak power output (PPO) more accurately than conventional multiple linear
regression (MLR).\r\n \r\n Methods\r\n
\ The cross-sectional study was conducted with 274 (♀168,
♂106) participants who performed CPET on a cycle ergometer. Machine learning models
and multiple linear regression were used to predict $$\\dot{V}$$\r\n \r\n
\ V\r\n ˙\r\n
\ \r\n O2peak
and PPO using non-exercise features. The accuracy of the models was compared using
criteria such as root mean square error (RMSE). Shapley additive explanation (SHAP)
was applied to determine the feature importance.\r\n \r\n
\ Results\r\n The
most accurate machine learning model was the random forest (RMSE: 6.52 ml/kg/min
[95% CI 5.21–8.17]) for $$\\dot{V}$$\r\n \r\n
\ V\r\n ˙\r\n
\ \r\n O2peak
prediction and the gradient boosting regression (RMSE: 43watts [95% CI 35–52])
for PPO prediction. Compared to the MLR, the machine learning models reduced the
RMSE by up to 28% and 22% for prediction of $$\\dot{V}$$\r\n \r\n
\ V\r\n ˙\r\n
\ \r\n O2peak
and PPO, respectively. Furthermore, SHAP ranked body composition data such as
skeletal muscle mass and extracellular water as the most impactful features.\r\n
\ \r\n Conclusion\r\n
\ Machine learning models predict $$\\dot{V}$$\r\n \r\n
\ V\r\n ˙\r\n
\ \r\n O2peak
and PPO more accurately than MLR and can be used to individualize CPET protocols.
Features that provide information about the participant's body composition contribute
most to the improvement of these predictions.\r\n \r\n
\ Trial registration number\r\n DRKS00031401
(6 March 2023, retrospectively registered).\r\n "
author:
- first_name: Charlotte
full_name: Wenzel, Charlotte
last_name: Wenzel
- first_name: Thomas
full_name: Liebig, Thomas
last_name: Liebig
- first_name: Adrian
full_name: Swoboda, Adrian
last_name: Swoboda
- first_name: Rika
full_name: Smolareck, Rika
last_name: Smolareck
- first_name: Marit Lea
full_name: Schlagheck, Marit Lea
id: '117661'
last_name: Schlagheck
orcid: '0000-0002-8913-6080 '
- first_name: David
full_name: Walzik, David
last_name: Walzik
- first_name: Andreas
full_name: Groll, Andreas
last_name: Groll
- first_name: Richie P.
full_name: Goulding, Richie P.
last_name: Goulding
- first_name: Philipp
full_name: Zimmer, Philipp
last_name: Zimmer
citation:
ama: Wenzel C, Liebig T, Swoboda A, et al. Machine learning predicts peak oxygen
uptake and peak power output for customizing cardiopulmonary exercise testing
using non-exercise features. European Journal of Applied Physiology. 2024;124(11):3421-3431.
doi:10.1007/s00421-024-05543-x
apa: Wenzel, C., Liebig, T., Swoboda, A., Smolareck, R., Schlagheck, M. L., Walzik,
D., Groll, A., Goulding, R. P., & Zimmer, P. (2024). Machine learning predicts
peak oxygen uptake and peak power output for customizing cardiopulmonary exercise
testing using non-exercise features. European Journal of Applied Physiology,
124(11), 3421–3431. https://doi.org/10.1007/s00421-024-05543-x
bibtex: '@article{Wenzel_Liebig_Swoboda_Smolareck_Schlagheck_Walzik_Groll_Goulding_Zimmer_2024,
title={Machine learning predicts peak oxygen uptake and peak power output for
customizing cardiopulmonary exercise testing using non-exercise features}, volume={124},
DOI={10.1007/s00421-024-05543-x},
number={11}, journal={European Journal of Applied Physiology}, publisher={Springer
Science and Business Media LLC}, author={Wenzel, Charlotte and Liebig, Thomas
and Swoboda, Adrian and Smolareck, Rika and Schlagheck, Marit Lea and Walzik,
David and Groll, Andreas and Goulding, Richie P. and Zimmer, Philipp}, year={2024},
pages={3421–3431} }'
chicago: 'Wenzel, Charlotte, Thomas Liebig, Adrian Swoboda, Rika Smolareck, Marit
Lea Schlagheck, David Walzik, Andreas Groll, Richie P. Goulding, and Philipp Zimmer.
“Machine Learning Predicts Peak Oxygen Uptake and Peak Power Output for Customizing
Cardiopulmonary Exercise Testing Using Non-Exercise Features.” European Journal
of Applied Physiology 124, no. 11 (2024): 3421–31. https://doi.org/10.1007/s00421-024-05543-x.'
ieee: 'C. Wenzel et al., “Machine learning predicts peak oxygen uptake and
peak power output for customizing cardiopulmonary exercise testing using non-exercise
features,” European Journal of Applied Physiology, vol. 124, no. 11, pp.
3421–3431, 2024, doi: 10.1007/s00421-024-05543-x.'
mla: Wenzel, Charlotte, et al. “Machine Learning Predicts Peak Oxygen Uptake and
Peak Power Output for Customizing Cardiopulmonary Exercise Testing Using Non-Exercise
Features.” European Journal of Applied Physiology, vol. 124, no. 11, Springer
Science and Business Media LLC, 2024, pp. 3421–31, doi:10.1007/s00421-024-05543-x.
short: C. Wenzel, T. Liebig, A. Swoboda, R. Smolareck, M.L. Schlagheck, D. Walzik,
A. Groll, R.P. Goulding, P. Zimmer, European Journal of Applied Physiology 124
(2024) 3421–3431.
date_created: 2025-05-27T07:54:52Z
date_updated: 2025-06-02T09:34:21Z
doi: 10.1007/s00421-024-05543-x
extern: '1'
intvolume: ' 124'
issue: '11'
language:
- iso: eng
page: 3421-3431
publication: European Journal of Applied Physiology
publication_identifier:
issn:
- 1439-6319
- 1439-6327
publication_status: published
publisher: Springer Science and Business Media LLC
status: public
title: Machine learning predicts peak oxygen uptake and peak power output for customizing
cardiopulmonary exercise testing using non-exercise features
type: journal_article
user_id: '117661'
volume: 124
year: '2024'
...
---
_id: '60176'
abstract:
- lang: eng
text: 'AbstractAimTo
investigate the associations of the Dietary Approaches to Stop Hypertension (DASH)
score with subcutaneous (SAT) and visceral (VAT) adipose tissue volume and hepatic
lipid content (HLC) in people with diabetes and to examine whether changes in
the DASH diet were associated with changes in these outcomes.MethodsIn
total, 335 participants with recent‐onset type 1 diabetes (T1D) and type 2 diabetes
(T2D) from the German Diabetes Study were included in the cross‐sectional analysis,
and 111 participants in the analysis of changes during the 5‐year follow‐up. Associations
between the DASH score and VAT, SAT and HLC and their changes were investigated
using multivariable linear regression models by diabetes type. The proportion
mediated by changes in potential mediators was determined using mediation analysis.ResultsA
higher baseline DASH score was associated with lower HLC, especially in people
with T2D (per 5 points: −1.5% [−2.7%; −0.3%]). Over 5 years, a 5‐point increase
in the DASH score was associated with decreased VAT in people with T2D (−514 [−800;
−228] cm3). Similar, but imprecise, associations were observed
for VAT changes in people with T1D (−403 [−861; 55] cm3)
and for HLC in people with T2D (−1.3% [−2.8%; 0.3%]). Body mass index and waist
circumference changes explained 8%‐48% of the associations between DASH and VAT
changes in both groups. In people with T2D, adipose tissue insulin resistance
index (Adipo‐IR) changes explained 47% of the association between DASH and HLC
changes.ConclusionsA
shift to a DASH‐like diet was associated with favourable VAT and HLC changes,
which were partly explained by changes in anthropometric measures and Adipo‐IR.'
author:
- first_name: Edyta
full_name: Schaefer, Edyta
last_name: Schaefer
- first_name: Alexander
full_name: Lang, Alexander
last_name: Lang
- first_name: Yuliya
full_name: Kupriyanova, Yuliya
last_name: Kupriyanova
- first_name: Kálmán B.
full_name: Bódis, Kálmán B.
last_name: Bódis
- first_name: Katharina S.
full_name: Weber, Katharina S.
last_name: Weber
- first_name: Anette
full_name: Buyken, Anette
id: '65985'
last_name: Buyken
- first_name: Janett
full_name: Barbaresko, Janett
last_name: Barbaresko
- first_name: Theresa
full_name: Kössler, Theresa
last_name: Kössler
- first_name: Sabine
full_name: Kahl, Sabine
last_name: Kahl
- first_name: Oana‐Patricia
full_name: Zaharia, Oana‐Patricia
last_name: Zaharia
- first_name: Julia
full_name: Szendroedi, Julia
last_name: Szendroedi
- first_name: Christian
full_name: Herder, Christian
last_name: Herder
- first_name: Vera B.
full_name: Schrauwen‐Hinderling, Vera B.
last_name: Schrauwen‐Hinderling
- first_name: Robert
full_name: Wagner, Robert
last_name: Wagner
- first_name: Oliver
full_name: Kuss, Oliver
last_name: Kuss
- first_name: Michael
full_name: Roden, Michael
last_name: Roden
- first_name: Sabrina
full_name: Schlesinger, Sabrina
last_name: Schlesinger
citation:
ama: Schaefer E, Lang A, Kupriyanova Y, et al. Adherence to the Dietary Approaches
to Stop Hypertension (DASH) diet is associated with lower visceral and hepatic
lipid content in recent‐onset type 1 diabetes and type 2 diabetes. Diabetes,
Obesity and Metabolism. 2024;26(10):4281-4292. doi:10.1111/dom.15772
apa: Schaefer, E., Lang, A., Kupriyanova, Y., Bódis, K. B., Weber, K. S., Buyken,
A., Barbaresko, J., Kössler, T., Kahl, S., Zaharia, O., Szendroedi, J., Herder,
C., Schrauwen‐Hinderling, V. B., Wagner, R., Kuss, O., Roden, M., & Schlesinger,
S. (2024). Adherence to the Dietary Approaches to Stop Hypertension (DASH) diet
is associated with lower visceral and hepatic lipid content in recent‐onset type
1 diabetes and type 2 diabetes. Diabetes, Obesity and Metabolism, 26(10),
4281–4292. https://doi.org/10.1111/dom.15772
bibtex: '@article{Schaefer_Lang_Kupriyanova_Bódis_Weber_Buyken_Barbaresko_Kössler_Kahl_Zaharia_et
al._2024, title={Adherence to the Dietary Approaches to Stop Hypertension (DASH)
diet is associated with lower visceral and hepatic lipid content in recent‐onset
type 1 diabetes and type 2 diabetes}, volume={26}, DOI={10.1111/dom.15772},
number={10}, journal={Diabetes, Obesity and Metabolism}, publisher={Wiley}, author={Schaefer,
Edyta and Lang, Alexander and Kupriyanova, Yuliya and Bódis, Kálmán B. and Weber,
Katharina S. and Buyken, Anette and Barbaresko, Janett and Kössler, Theresa and
Kahl, Sabine and Zaharia, Oana‐Patricia and et al.}, year={2024}, pages={4281–4292}
}'
chicago: 'Schaefer, Edyta, Alexander Lang, Yuliya Kupriyanova, Kálmán B. Bódis,
Katharina S. Weber, Anette Buyken, Janett Barbaresko, et al. “Adherence to the
Dietary Approaches to Stop Hypertension (DASH) Diet Is Associated with Lower Visceral
and Hepatic Lipid Content in Recent‐onset Type 1 Diabetes and Type 2 Diabetes.”
Diabetes, Obesity and Metabolism 26, no. 10 (2024): 4281–92. https://doi.org/10.1111/dom.15772.'
ieee: 'E. Schaefer et al., “Adherence to the Dietary Approaches to Stop Hypertension
(DASH) diet is associated with lower visceral and hepatic lipid content in recent‐onset
type 1 diabetes and type 2 diabetes,” Diabetes, Obesity and Metabolism,
vol. 26, no. 10, pp. 4281–4292, 2024, doi: 10.1111/dom.15772.'
mla: Schaefer, Edyta, et al. “Adherence to the Dietary Approaches to Stop Hypertension
(DASH) Diet Is Associated with Lower Visceral and Hepatic Lipid Content in Recent‐onset
Type 1 Diabetes and Type 2 Diabetes.” Diabetes, Obesity and Metabolism,
vol. 26, no. 10, Wiley, 2024, pp. 4281–92, doi:10.1111/dom.15772.
short: E. Schaefer, A. Lang, Y. Kupriyanova, K.B. Bódis, K.S. Weber, A. Buyken,
J. Barbaresko, T. Kössler, S. Kahl, O. Zaharia, J. Szendroedi, C. Herder, V.B.
Schrauwen‐Hinderling, R. Wagner, O. Kuss, M. Roden, S. Schlesinger, Diabetes,
Obesity and Metabolism 26 (2024) 4281–4292.
date_created: 2025-06-11T08:39:22Z
date_updated: 2025-06-11T08:42:19Z
department:
- _id: '22'
doi: 10.1111/dom.15772
intvolume: ' 26'
issue: '10'
language:
- iso: eng
page: 4281-4292
publication: Diabetes, Obesity and Metabolism
publication_identifier:
issn:
- 1462-8902
- 1463-1326
publication_status: published
publisher: Wiley
status: public
title: Adherence to the Dietary Approaches to Stop Hypertension (DASH) diet is associated
with lower visceral and hepatic lipid content in recent‐onset type 1 diabetes and
type 2 diabetes
type: journal_article
user_id: '92491'
volume: 26
year: '2024'
...
---
_id: '64104'
author:
- first_name: Christian
full_name: Scheideler, Christian
id: '20792'
last_name: Scheideler
- first_name: 'Kristian '
full_name: 'Hinnenthal , Kristian '
last_name: 'Hinnenthal '
- first_name: David Jan
full_name: Liedtke, David Jan
id: '55557'
last_name: Liedtke
citation:
ama: 'Scheideler C, Hinnenthal K, Liedtke DJ. Efficient Shape Formation by 3D Hybrid
Programmable Matter: An Algorithm for Low Diameter Intermediate Structures. SAND
2024: 15:1-15:20. In: ; 2024.'
apa: 'Scheideler, C., Hinnenthal , K., & Liedtke, D. J. (2024). Efficient
Shape Formation by 3D Hybrid Programmable Matter: An Algorithm for Low Diameter
Intermediate Structures. SAND 2024: 15:1-15:20.'
bibtex: '@inproceedings{Scheideler_Hinnenthal _Liedtke_2024, place={CoRR abs/2401.17734
(2024)}, title={Efficient Shape Formation by 3D Hybrid Programmable Matter: An
Algorithm for Low Diameter Intermediate Structures. SAND 2024: 15:1-15:20}, author={Scheideler,
Christian and Hinnenthal , Kristian and Liedtke, David Jan}, year={2024} }'
chicago: 'Scheideler, Christian, Kristian Hinnenthal , and David Jan Liedtke. “Efficient
Shape Formation by 3D Hybrid Programmable Matter: An Algorithm for Low Diameter
Intermediate Structures. SAND 2024: 15:1-15:20.” CoRR abs/2401.17734 (2024), 2024.'
ieee: 'C. Scheideler, K. Hinnenthal , and D. J. Liedtke, “Efficient Shape Formation
by 3D Hybrid Programmable Matter: An Algorithm for Low Diameter Intermediate Structures.
SAND 2024: 15:1-15:20,” 2024.'
mla: 'Scheideler, Christian, et al. Efficient Shape Formation by 3D Hybrid Programmable
Matter: An Algorithm for Low Diameter Intermediate Structures. SAND 2024: 15:1-15:20.
2024.'
short: 'C. Scheideler, K. Hinnenthal , D.J. Liedtke, in: CoRR abs/2401.17734 (2024),
2024.'
date_created: 2026-02-10T10:12:18Z
date_updated: 2026-02-11T09:12:05Z
department:
- _id: '34'
- _id: '7'
- _id: '79'
language:
- iso: eng
place: CoRR abs/2401.17734 (2024)
status: public
title: 'Efficient Shape Formation by 3D Hybrid Programmable Matter: An Algorithm for
Low Diameter Intermediate Structures. SAND 2024: 15:1-15:20'
type: conference
user_id: '15578'
year: '2024'
...
---
_id: '64106'
author:
- first_name: Christian
full_name: Scheideler, Christian
id: '20792'
last_name: Scheideler
- first_name: 'Irina '
full_name: 'Kostitsyna, Irina '
last_name: Kostitsyna
- first_name: David Jan
full_name: Liedtke, David Jan
id: '55557'
last_name: Liedtke
citation:
ama: 'Scheideler C, Kostitsyna I, Liedtke DJ. Universal Coating by 3D Hybrid Programmable
Matter. In: ; 2024.'
apa: Scheideler, C., Kostitsyna, I., & Liedtke, D. J. (2024). Universal Coating
by 3D Hybrid Programmable Matter.
bibtex: '@inproceedings{Scheideler_Kostitsyna_Liedtke_2024, place={SIROCCO 2024:
384-401}, title={Universal Coating by 3D Hybrid Programmable Matter.}, author={Scheideler,
Christian and Kostitsyna, Irina and Liedtke, David Jan}, year={2024} }'
chicago: 'Scheideler, Christian, Irina Kostitsyna, and David Jan Liedtke. “Universal
Coating by 3D Hybrid Programmable Matter.” SIROCCO 2024: 384-401, 2024.'
ieee: C. Scheideler, I. Kostitsyna, and D. J. Liedtke, “Universal Coating by 3D
Hybrid Programmable Matter.,” 2024.
mla: Scheideler, Christian, et al. Universal Coating by 3D Hybrid Programmable
Matter. 2024.
short: 'C. Scheideler, I. Kostitsyna, D.J. Liedtke, in: SIROCCO 2024: 384-401, 2024.'
date_created: 2026-02-10T10:21:39Z
date_updated: 2026-02-11T09:12:11Z
department:
- _id: '34'
- _id: '7'
- _id: '79'
language:
- iso: eng
place: 'SIROCCO 2024: 384-401'
status: public
title: Universal Coating by 3D Hybrid Programmable Matter.
type: conference
user_id: '15578'
year: '2024'
...
---
_id: '64002'
abstract:
- lang: eng
text: The production of formaldehyde on industrial scale requires huge amounts of
energy due to the involvement of reforming processes in combination with the demand
in the megaton scale. Hence, a direct route for the transformation of (bio)methane
to formaldehyde would decrease costs and puts less pressure on the environment.
Herein, we report on the use of zinc modified silicas as possible support materials
for vanadium catalysts and the resulting consequences for the performance in the
selective oxidation of methane to formaldehyde. After optimization of the Zn content
and reaction conditions, a remarkably high space-time yield of 12.4 kgCH2O·kgcat−1·h−1
was achieved. As a result of the extensive characterization by means of UV–vis,
Raman, XANES and NMR spectroscopy it was found that vanadium is in the vicinity
of highly dispersed zinc atoms which promote the formation of active vanadium
species as supposed by theoretical calculations. This work presents a further
step of catalyst development towards direct industrial methane conversion which
may help to overcome current limitations in the future.
author:
- first_name: Benny
full_name: Kunkel, Benny
last_name: Kunkel
- first_name: Dominik
full_name: Seeburg, Dominik
last_name: Seeburg
- first_name: Anke
full_name: Kabelitz, Anke
last_name: Kabelitz
- first_name: Steffen
full_name: Witte, Steffen
last_name: Witte
- first_name: Torsten
full_name: Gutmann, Torsten
id: '118165'
last_name: Gutmann
- first_name: Hergen
full_name: Breitzke, Hergen
last_name: Breitzke
- first_name: Gerd
full_name: Buntkowsky, Gerd
last_name: Buntkowsky
- first_name: Ana Guilherme
full_name: Buzanich, Ana Guilherme
last_name: Buzanich
- first_name: Sebastian
full_name: Wohlrab, Sebastian
last_name: Wohlrab
citation:
ama: Kunkel B, Seeburg D, Kabelitz A, et al. Highly productive V/Zn-SiO2 catalysts
for the selective oxidation of methane. Catalysis Today. 2024;432:114643.
doi:10.1016/j.cattod.2024.114643
apa: Kunkel, B., Seeburg, D., Kabelitz, A., Witte, S., Gutmann, T., Breitzke, H.,
Buntkowsky, G., Buzanich, A. G., & Wohlrab, S. (2024). Highly productive V/Zn-SiO2
catalysts for the selective oxidation of methane. Catalysis Today, 432,
114643. https://doi.org/10.1016/j.cattod.2024.114643
bibtex: '@article{Kunkel_Seeburg_Kabelitz_Witte_Gutmann_Breitzke_Buntkowsky_Buzanich_Wohlrab_2024,
title={Highly productive V/Zn-SiO2 catalysts for the selective oxidation of methane},
volume={432}, DOI={10.1016/j.cattod.2024.114643},
journal={Catalysis Today}, author={Kunkel, Benny and Seeburg, Dominik and Kabelitz,
Anke and Witte, Steffen and Gutmann, Torsten and Breitzke, Hergen and Buntkowsky,
Gerd and Buzanich, Ana Guilherme and Wohlrab, Sebastian}, year={2024}, pages={114643}
}'
chicago: 'Kunkel, Benny, Dominik Seeburg, Anke Kabelitz, Steffen Witte, Torsten
Gutmann, Hergen Breitzke, Gerd Buntkowsky, Ana Guilherme Buzanich, and Sebastian
Wohlrab. “Highly Productive V/Zn-SiO2 Catalysts for the Selective Oxidation of
Methane.” Catalysis Today 432 (2024): 114643. https://doi.org/10.1016/j.cattod.2024.114643.'
ieee: 'B. Kunkel et al., “Highly productive V/Zn-SiO2 catalysts for the selective
oxidation of methane,” Catalysis Today, vol. 432, p. 114643, 2024, doi:
10.1016/j.cattod.2024.114643.'
mla: Kunkel, Benny, et al. “Highly Productive V/Zn-SiO2 Catalysts for the Selective
Oxidation of Methane.” Catalysis Today, vol. 432, 2024, p. 114643, doi:10.1016/j.cattod.2024.114643.
short: B. Kunkel, D. Seeburg, A. Kabelitz, S. Witte, T. Gutmann, H. Breitzke, G.
Buntkowsky, A.G. Buzanich, S. Wohlrab, Catalysis Today 432 (2024) 114643.
date_created: 2026-02-07T15:53:56Z
date_updated: 2026-02-17T16:15:41Z
doi: 10.1016/j.cattod.2024.114643
extern: '1'
intvolume: ' 432'
keyword:
- Formaldehyde
- Local coordination
- SBA-15
- Vanadium oxo species
- XANES
- Zinc doped silica
language:
- iso: eng
page: '114643'
publication: Catalysis Today
status: public
title: Highly productive V/Zn-SiO2 catalysts for the selective oxidation of methane
type: journal_article
user_id: '100715'
volume: 432
year: '2024'
...
---
_id: '58223'
abstract:
- lang: eng
text: The Shapley value (SV) is a prevalent approach of allocating credit to machine
learning (ML) entities to understand black box ML models. Enriching such interpretations
with higher-order interactions is inevitable for complex systems, where the Shapley
Interaction Index (SII) is a direct axiomatic extension of the SV. While it is
well-known that the SV yields an optimal approximation of any game via a weighted
least square (WLS) objective, an extension of this result to SII has been a long-standing
open problem, which even led to the proposal of an alternative index. In this
work, we characterize higher-order SII as a solution to a WLS problem, which constructs
an optimal approximation via SII and k-Shapley values (k-SII). We prove this representation
for the SV and pairwise SII and give empirically validated conjectures for higher
orders. As a result, we propose KernelSHAP-IQ, a direct extension of KernelSHAP
for SII, and demonstrate state-of-the-art performance for feature interactions.
author:
- first_name: Fabian
full_name: Fumagalli, Fabian
last_name: Fumagalli
- first_name: Maximilian
full_name: Muschalik, Maximilian
last_name: Muschalik
- first_name: Patrick
full_name: Kolpaczki, Patrick
last_name: Kolpaczki
- first_name: Eyke
full_name: Hüllermeier, Eyke
last_name: Hüllermeier
- first_name: Barbara
full_name: Hammer, Barbara
last_name: Hammer
citation:
ama: 'Fumagalli F, Muschalik M, Kolpaczki P, Hüllermeier E, Hammer B. KernelSHAP-IQ:
Weighted Least Square Optimization for Shapley Interactions. In: Proceedings
of the 41st International Conference on Machine Learning (ICML). Vol 235.
Proceedings of Machine Learning Research. PMLR; 2024:14308–14342.'
apa: 'Fumagalli, F., Muschalik, M., Kolpaczki, P., Hüllermeier, E., & Hammer,
B. (2024). KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions.
Proceedings of the 41st International Conference on Machine Learning (ICML),
235, 14308–14342.'
bibtex: '@inproceedings{Fumagalli_Muschalik_Kolpaczki_Hüllermeier_Hammer_2024, series={Proceedings
of Machine Learning Research}, title={KernelSHAP-IQ: Weighted Least Square Optimization
for Shapley Interactions}, volume={235}, booktitle={Proceedings of the 41st International
Conference on Machine Learning (ICML)}, publisher={PMLR}, author={Fumagalli, Fabian
and Muschalik, Maximilian and Kolpaczki, Patrick and Hüllermeier, Eyke and Hammer,
Barbara}, year={2024}, pages={14308–14342}, collection={Proceedings of Machine
Learning Research} }'
chicago: 'Fumagalli, Fabian, Maximilian Muschalik, Patrick Kolpaczki, Eyke Hüllermeier,
and Barbara Hammer. “KernelSHAP-IQ: Weighted Least Square Optimization for Shapley
Interactions.” In Proceedings of the 41st International Conference on Machine
Learning (ICML), 235:14308–14342. Proceedings of Machine Learning Research.
PMLR, 2024.'
ieee: 'F. Fumagalli, M. Muschalik, P. Kolpaczki, E. Hüllermeier, and B. Hammer,
“KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions,”
in Proceedings of the 41st International Conference on Machine Learning (ICML),
2024, vol. 235, pp. 14308–14342.'
mla: 'Fumagalli, Fabian, et al. “KernelSHAP-IQ: Weighted Least Square Optimization
for Shapley Interactions.” Proceedings of the 41st International Conference
on Machine Learning (ICML), vol. 235, PMLR, 2024, pp. 14308–14342.'
short: 'F. Fumagalli, M. Muschalik, P. Kolpaczki, E. Hüllermeier, B. Hammer, in:
Proceedings of the 41st International Conference on Machine Learning (ICML), PMLR,
2024, pp. 14308–14342.'
date_created: 2025-01-16T16:12:16Z
date_updated: 2025-09-11T16:27:05Z
department:
- _id: '660'
intvolume: ' 235'
language:
- iso: eng
page: 14308–14342
project:
- _id: '109'
name: 'TRR 318: TRR 318 - Erklärbarkeit konstruieren'
- _id: '117'
name: 'TRR 318 - C: TRR 318 - Project Area C'
- _id: '126'
name: 'TRR 318 - C3: TRR 318 - Subproject C3'
publication: Proceedings of the 41st International Conference on Machine Learning
(ICML)
publisher: PMLR
series_title: Proceedings of Machine Learning Research
status: public
title: 'KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions'
type: conference
user_id: '93420'
volume: 235
year: '2024'
...
---
_id: '61834'
abstract:
- lang: eng
text: Abstract3D printing or additive manufacturing
(AM) possesses enormous potential to benefit the manufacturing industry. Presently,
rotary draw bending (RDB) is one of the most commonly used cold-forming industrial
process for bending metal tubes. Pressure die is a fundamental forming tool in
RDB processes, and it is conventionally made by various grades of comparatively
expensive alloy steels. This research presents a novel design of a pressure die
which can be 3D printed by using inexpensive polymeric filaments. In this research
paper, the 3D-printed pressure die is named as “FFF-pressure die.” The material
used to fabricate the FFF-pressure die is a thermoplastic polymer known as “ecoPLA.”
The mechanical properties of ecoPLA are studied in relation to the process conditions
of a RDB process. Firstly, an initial feasibility of using the FFF-pressure die
in a RDB process is obtained by conducting a quick static stress analysis with
actual process conditions. After initial feasibility, a complete RDB process is
developed and simulated with actual process conditions and material properties.
The FFF-pressure die is then practically fabricated by FFF 3D printer and experimentally
tested on an industrial RDB machine. The results of practical experiments are
compared with the simulation results. In order to make a comparison of the FFF-pressure
die with the conventional metal pressure die, the simulation and practical process
is also conducted with the conventional metal pressure die. A performance and
cost comparison is made between the polymeric FFF-pressure die and the conventional
metal pressure die. Von Mises stresses, contact forces, failure risk, and elastic
deformations are analyzed. The advantages and limitations of using the FFF-pressure
die in a RDB process are discussed in the end. This research intends to widen
the avenue of using cost-effective and lightweight forming tools in metal forming
industries.
author:
- first_name: Muhammad Ali
full_name: Kaleem, Muhammad Ali
last_name: Kaleem
- first_name: Rainer
full_name: Steinheimer, Rainer
last_name: Steinheimer
- first_name: Peter
full_name: Frohn-Sörensen, Peter
last_name: Frohn-Sörensen
- first_name: Steffen
full_name: Gabsa, Steffen
id: '106786'
last_name: Gabsa
- first_name: Bernd
full_name: Engel, Bernd
last_name: Engel
citation:
ama: Kaleem MA, Steinheimer R, Frohn-Sörensen P, Gabsa S, Engel B. Additive manufacturing
of polymeric pressure die for rotary draw bending process. The International
Journal of Advanced Manufacturing Technology. 2024;134(3-4):1789-1804. doi:10.1007/s00170-024-14221-3
apa: Kaleem, M. A., Steinheimer, R., Frohn-Sörensen, P., Gabsa, S., & Engel,
B. (2024). Additive manufacturing of polymeric pressure die for rotary draw bending
process. The International Journal of Advanced Manufacturing Technology,
134(3–4), 1789–1804. https://doi.org/10.1007/s00170-024-14221-3
bibtex: '@article{Kaleem_Steinheimer_Frohn-Sörensen_Gabsa_Engel_2024, title={Additive
manufacturing of polymeric pressure die for rotary draw bending process}, volume={134},
DOI={10.1007/s00170-024-14221-3},
number={3–4}, journal={The International Journal of Advanced Manufacturing Technology},
publisher={Springer Science and Business Media LLC}, author={Kaleem, Muhammad
Ali and Steinheimer, Rainer and Frohn-Sörensen, Peter and Gabsa, Steffen and Engel,
Bernd}, year={2024}, pages={1789–1804} }'
chicago: 'Kaleem, Muhammad Ali, Rainer Steinheimer, Peter Frohn-Sörensen, Steffen
Gabsa, and Bernd Engel. “Additive Manufacturing of Polymeric Pressure Die for
Rotary Draw Bending Process.” The International Journal of Advanced Manufacturing
Technology 134, no. 3–4 (2024): 1789–1804. https://doi.org/10.1007/s00170-024-14221-3.'
ieee: 'M. A. Kaleem, R. Steinheimer, P. Frohn-Sörensen, S. Gabsa, and B. Engel,
“Additive manufacturing of polymeric pressure die for rotary draw bending process,”
The International Journal of Advanced Manufacturing Technology, vol. 134,
no. 3–4, pp. 1789–1804, 2024, doi: 10.1007/s00170-024-14221-3.'
mla: Kaleem, Muhammad Ali, et al. “Additive Manufacturing of Polymeric Pressure
Die for Rotary Draw Bending Process.” The International Journal of Advanced
Manufacturing Technology, vol. 134, no. 3–4, Springer Science and Business
Media LLC, 2024, pp. 1789–804, doi:10.1007/s00170-024-14221-3.
short: M.A. Kaleem, R. Steinheimer, P. Frohn-Sörensen, S. Gabsa, B. Engel, The International
Journal of Advanced Manufacturing Technology 134 (2024) 1789–1804.
date_created: 2025-10-15T09:02:15Z
date_updated: 2025-10-15T13:08:16Z
doi: 10.1007/s00170-024-14221-3
intvolume: ' 134'
issue: 3-4
language:
- iso: eng
page: 1789-1804
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: Additive manufacturing of polymeric pressure die for rotary draw bending process
type: journal_article
user_id: '106786'
volume: 134
year: '2024'
...
---
_id: '62770'
abstract:
- lang: eng
text: AbstractThe open-source parameter identification
tool ADAPT (A diversely applicable parameter identification Tool) is integrated
with a machine learning-based approach for start value prediction in order to
calibrate a Gurson–Tvergaard–Needleman (GTN) and a Lemaitre damage model. As representative
example case-hardened steel 16MnCrS5 is elaborated. An artificial neural network
(ANN) is initially trained by using load–displacement curves derived from simulations
of a boundary value problem—instead of using data generated for homogeneous states
of deformation at material point or one-element level—with varying material parameter
combinations. The ANN is then employed so as to predict sets of material parameters
that already provide close solutions to the experiment. These predicted parameter
sets serve as starting values for a subsequent multi-objective parameter identification
by using ADAPT. ADAPT allows for the consideration of input data from multiple
scales, including integral data such as load–displacement curves, full-field data
such as displacement and strain fields, and high-resolution experimental void
data at the micro-scale. The influence of each data set on prediction quality
is analyzed. Using various types of input data introduces additional information,
enhancing prediction accuracy. The validation is carried out with respect to experimental
void measurements of forward rod extruded parts. The results demonstrate, by incorporating
void measurements in the optimization process, that it is possible to improve
the quantitative prediction of ductile damage in the sense of void area fractions
by factor 28 in forward rod extrusion.
author:
- first_name: Jan
full_name: Gerlach, Jan
last_name: Gerlach
- first_name: Robin
full_name: Schulte, Robin
last_name: Schulte
- first_name: Alexander
full_name: Schowtjak, Alexander
last_name: Schowtjak
- first_name: Till
full_name: Clausmeyer, Till
last_name: Clausmeyer
- first_name: Richard
full_name: Ostwald, Richard
id: '106876'
last_name: Ostwald
orcid: 0000-0003-2147-8444
- first_name: A. Erman
full_name: Tekkaya, A. Erman
last_name: Tekkaya
- first_name: Andreas
full_name: Menzel, Andreas
last_name: Menzel
citation:
ama: Gerlach J, Schulte R, Schowtjak A, et al. Enhancing damage prediction in bulk
metal forming through machine learning-assisted parameter identification. Archive
of Applied Mechanics. 2024;94(8):2217-2242. doi:10.1007/s00419-024-02634-1
apa: Gerlach, J., Schulte, R., Schowtjak, A., Clausmeyer, T., Ostwald, R., Tekkaya,
A. E., & Menzel, A. (2024). Enhancing damage prediction in bulk metal forming
through machine learning-assisted parameter identification. Archive of Applied
Mechanics, 94(8), 2217–2242. https://doi.org/10.1007/s00419-024-02634-1
bibtex: '@article{Gerlach_Schulte_Schowtjak_Clausmeyer_Ostwald_Tekkaya_Menzel_2024,
title={Enhancing damage prediction in bulk metal forming through machine learning-assisted
parameter identification}, volume={94}, DOI={10.1007/s00419-024-02634-1},
number={8}, journal={Archive of Applied Mechanics}, publisher={Springer Science
and Business Media LLC}, author={Gerlach, Jan and Schulte, Robin and Schowtjak,
Alexander and Clausmeyer, Till and Ostwald, Richard and Tekkaya, A. Erman and
Menzel, Andreas}, year={2024}, pages={2217–2242} }'
chicago: 'Gerlach, Jan, Robin Schulte, Alexander Schowtjak, Till Clausmeyer, Richard
Ostwald, A. Erman Tekkaya, and Andreas Menzel. “Enhancing Damage Prediction in
Bulk Metal Forming through Machine Learning-Assisted Parameter Identification.”
Archive of Applied Mechanics 94, no. 8 (2024): 2217–42. https://doi.org/10.1007/s00419-024-02634-1.'
ieee: 'J. Gerlach et al., “Enhancing damage prediction in bulk metal forming
through machine learning-assisted parameter identification,” Archive of Applied
Mechanics, vol. 94, no. 8, pp. 2217–2242, 2024, doi: 10.1007/s00419-024-02634-1.'
mla: Gerlach, Jan, et al. “Enhancing Damage Prediction in Bulk Metal Forming through
Machine Learning-Assisted Parameter Identification.” Archive of Applied Mechanics,
vol. 94, no. 8, Springer Science and Business Media LLC, 2024, pp. 2217–42, doi:10.1007/s00419-024-02634-1.
short: J. Gerlach, R. Schulte, A. Schowtjak, T. Clausmeyer, R. Ostwald, A.E. Tekkaya,
A. Menzel, Archive of Applied Mechanics 94 (2024) 2217–2242.
date_created: 2025-12-03T12:46:31Z
date_updated: 2025-12-03T12:50:41Z
department:
- _id: '952'
- _id: '321'
doi: 10.1007/s00419-024-02634-1
intvolume: ' 94'
issue: '8'
language:
- iso: eng
page: 2217-2242
publication: Archive of Applied Mechanics
publication_identifier:
issn:
- 0939-1533
- 1432-0681
publication_status: published
publisher: Springer Science and Business Media LLC
quality_controlled: '1'
status: public
title: Enhancing damage prediction in bulk metal forming through machine learning-assisted
parameter identification
type: journal_article
user_id: '85414'
volume: 94
year: '2024'
...
---
_id: '62942'
abstract:
- lang: eng
text: AbstractNanostructured bilayer thin films
with superhydrophobic and superhydrophilic surfaces were prepared using Ti6Al4V
alloy substrates which allowed for the comparative analysis of polyvinyl acetate
(PVAc) particle adsorption as a function of the interface structure. The PVAc
particles were obtained from emulsion polymerization of vinyl acetate. A superhydrophilic
TiO2 nanofiber-based 3D network was created on the Ti6Al4V
alloy substrate by application of a hydrothermal method. Subsequent UV-grafting
of ultra-thin polydimethylsiloxane (PDMS) layers resulted in a superhydrophobic
surface. The modification steps were followed via Diffuse Reflectance Infrared
Fourier Transform Spectroscopy, X-ray Photoelectron Spectroscopy, Field Emission-Scanning
Electron Microscopy, contact angle and Electrochemical Impedance Spectroscopy.
A mechanism for the adsorption of PVAc at the two electrolyte/substrate interfaces
could be revealed.
article_number: '294'
author:
- first_name: Vanessa
full_name: Neßlinger, Vanessa
id: '54649'
last_name: Neßlinger
orcid: 0000-0001-9416-1646
- first_name: Jan
full_name: Atlanov, Jan
last_name: Atlanov
- first_name: Guido
full_name: Grundmeier, Guido
id: '194'
last_name: Grundmeier
citation:
ama: Neßlinger V, Atlanov J, Grundmeier G. Interactions of polyvinyl acetate dispersions
with nanostructured superhydrophilic and superhydrophobic Ti6Al4V alloy surfaces.
Discover Applied Sciences. 2024;6(6). doi:10.1007/s42452-024-05916-z
apa: Neßlinger, V., Atlanov, J., & Grundmeier, G. (2024). Interactions of polyvinyl
acetate dispersions with nanostructured superhydrophilic and superhydrophobic
Ti6Al4V alloy surfaces. Discover Applied Sciences, 6(6), Article
294. https://doi.org/10.1007/s42452-024-05916-z
bibtex: '@article{Neßlinger_Atlanov_Grundmeier_2024, title={Interactions of polyvinyl
acetate dispersions with nanostructured superhydrophilic and superhydrophobic
Ti6Al4V alloy surfaces}, volume={6}, DOI={10.1007/s42452-024-05916-z},
number={6294}, journal={Discover Applied Sciences}, publisher={Springer Science
and Business Media LLC}, author={Neßlinger, Vanessa and Atlanov, Jan and Grundmeier,
Guido}, year={2024} }'
chicago: Neßlinger, Vanessa, Jan Atlanov, and Guido Grundmeier. “Interactions of
Polyvinyl Acetate Dispersions with Nanostructured Superhydrophilic and Superhydrophobic
Ti6Al4V Alloy Surfaces.” Discover Applied Sciences 6, no. 6 (2024). https://doi.org/10.1007/s42452-024-05916-z.
ieee: 'V. Neßlinger, J. Atlanov, and G. Grundmeier, “Interactions of polyvinyl acetate
dispersions with nanostructured superhydrophilic and superhydrophobic Ti6Al4V
alloy surfaces,” Discover Applied Sciences, vol. 6, no. 6, Art. no. 294,
2024, doi: 10.1007/s42452-024-05916-z.'
mla: Neßlinger, Vanessa, et al. “Interactions of Polyvinyl Acetate Dispersions with
Nanostructured Superhydrophilic and Superhydrophobic Ti6Al4V Alloy Surfaces.”
Discover Applied Sciences, vol. 6, no. 6, 294, Springer Science and Business
Media LLC, 2024, doi:10.1007/s42452-024-05916-z.
short: V. Neßlinger, J. Atlanov, G. Grundmeier, Discover Applied Sciences 6 (2024).
date_created: 2025-12-08T08:32:26Z
date_updated: 2025-12-08T08:33:00Z
department:
- _id: '302'
doi: 10.1007/s42452-024-05916-z
intvolume: ' 6'
issue: '6'
language:
- iso: eng
project:
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: Discover Applied Sciences
publication_identifier:
issn:
- 3004-9261
publication_status: published
publisher: Springer Science and Business Media LLC
status: public
title: Interactions of polyvinyl acetate dispersions with nanostructured superhydrophilic
and superhydrophobic Ti6Al4V alloy surfaces
type: journal_article
user_id: '54649'
volume: 6
year: '2024'
...
---
_id: '53146'
author:
- first_name: Thomas
full_name: Berger, Thomas
id: '77457'
last_name: Berger
- first_name: Dario
full_name: Dennstädt, Dario
id: '98033'
last_name: Dennstädt
- first_name: 'L. '
full_name: 'Lanza, L. '
last_name: Lanza
- first_name: 'K. '
full_name: 'Worthmann, K. '
last_name: Worthmann
citation:
ama: Berger T, Dennstädt D, Lanza L, Worthmann K. Robust Funnel Model Predictive
Control for Output Tracking with Prescribed Performance. SIAM Journal on Control
and Optimization. Published online 2024.
apa: Berger, T., Dennstädt, D., Lanza, L., & Worthmann, K. (2024). Robust Funnel
Model Predictive Control for Output Tracking with Prescribed Performance. SIAM
Journal on Control and Optimization.
bibtex: '@article{Berger_Dennstädt_Lanza_Worthmann_2024, title={Robust Funnel Model
Predictive Control for Output Tracking with Prescribed Performance}, journal={SIAM
Journal on Control and Optimization}, author={Berger, Thomas and Dennstädt, Dario
and Lanza, L. and Worthmann, K. }, year={2024} }'
chicago: Berger, Thomas, Dario Dennstädt, L. Lanza, and K. Worthmann. “Robust
Funnel Model Predictive Control for Output Tracking with Prescribed Performance.”
SIAM Journal on Control and Optimization, 2024.
ieee: T. Berger, D. Dennstädt, L. Lanza, and K. Worthmann, “Robust Funnel Model
Predictive Control for Output Tracking with Prescribed Performance,” SIAM Journal
on Control and Optimization, 2024.
mla: Berger, Thomas, et al. “Robust Funnel Model Predictive Control for Output Tracking
with Prescribed Performance.” SIAM Journal on Control and Optimization,
2024.
short: T. Berger, D. Dennstädt, L. Lanza, K. Worthmann, SIAM Journal on Control
and Optimization (2024).
date_created: 2024-04-03T10:08:01Z
date_updated: 2026-01-05T20:52:17Z
department:
- _id: '618'
language:
- iso: eng
publication: SIAM Journal on Control and Optimization
status: public
title: Robust Funnel Model Predictive Control for Output Tracking with Prescribed
Performance
type: journal_article
user_id: '77457'
year: '2024'
...
---
_id: '63497'
author:
- first_name: Nikolas
full_name: Förster, Nikolas
last_name: Förster
- first_name: Oliver
full_name: Wallscheid, Oliver
last_name: Wallscheid
- first_name: Frank
full_name: Schafmeister, Frank
last_name: Schafmeister
citation:
ama: 'Förster N, Wallscheid O, Schafmeister F. Dual-Active Bridge Sequential Pareto
Optimization for Fast Pre-Design and Final Component Selection. In: 2024 IEEE
Design Methodologies Conference (DMC). ; 2024:1-8. doi:10.1109/DMC62632.2024.10812131'
apa: Förster, N., Wallscheid, O., & Schafmeister, F. (2024). Dual-Active Bridge
Sequential Pareto Optimization for Fast Pre-Design and Final Component Selection.
2024 IEEE Design Methodologies Conference (DMC), 1–8. https://doi.org/10.1109/DMC62632.2024.10812131
bibtex: '@inproceedings{Förster_Wallscheid_Schafmeister_2024, title={Dual-Active
Bridge Sequential Pareto Optimization for Fast Pre-Design and Final Component
Selection}, DOI={10.1109/DMC62632.2024.10812131},
booktitle={2024 IEEE Design Methodologies Conference (DMC)}, author={Förster,
Nikolas and Wallscheid, Oliver and Schafmeister, Frank}, year={2024}, pages={1–8}
}'
chicago: Förster, Nikolas, Oliver Wallscheid, and Frank Schafmeister. “Dual-Active
Bridge Sequential Pareto Optimization for Fast Pre-Design and Final Component
Selection.” In 2024 IEEE Design Methodologies Conference (DMC), 1–8, 2024.
https://doi.org/10.1109/DMC62632.2024.10812131.
ieee: 'N. Förster, O. Wallscheid, and F. Schafmeister, “Dual-Active Bridge Sequential
Pareto Optimization for Fast Pre-Design and Final Component Selection,” in 2024
IEEE Design Methodologies Conference (DMC), 2024, pp. 1–8, doi: 10.1109/DMC62632.2024.10812131.'
mla: Förster, Nikolas, et al. “Dual-Active Bridge Sequential Pareto Optimization
for Fast Pre-Design and Final Component Selection.” 2024 IEEE Design Methodologies
Conference (DMC), 2024, pp. 1–8, doi:10.1109/DMC62632.2024.10812131.
short: 'N. Förster, O. Wallscheid, F. Schafmeister, in: 2024 IEEE Design Methodologies
Conference (DMC), 2024, pp. 1–8.'
date_created: 2026-01-06T08:06:24Z
date_updated: 2026-01-06T08:07:50Z
department:
- _id: '52'
doi: 10.1109/DMC62632.2024.10812131
keyword:
- MOSFET
- Thermal resistance
- Surface resistance
- Bridge circuits
- Zero voltage switching
- Pareto optimization
- Capacitance
- Numerical simulation
- Optimization
- Resistance heating
- Pareto Optimization
- Dual-Active Bridge
- ZVS
- Inductor Optimization
- Transformer Optimization
- Heat Sink Optimization
language:
- iso: eng
page: 1-8
publication: 2024 IEEE Design Methodologies Conference (DMC)
status: public
title: Dual-Active Bridge Sequential Pareto Optimization for Fast Pre-Design and Final
Component Selection
type: conference
user_id: '83383'
year: '2024'
...
---
_id: '57190'
abstract:
- lang: eng
text: This paper deals with the modeling of a soft sensor for detecting α’-martensite
evolution from the micromagnetic signals that are measured during the reverse
flow forming of metastable AISI 304L austenitic steel. This model can be prospectively
used inside a closed-loop property-controlled flow forming process. To achieve
this, optimization by means of a non-linear regression of experimental data was
carried out. To collect the experimental data, specimens were produced by flow
forming seamless tubes at room temperature. Using a combination of production
parameters (like the infeed depth and feed rate), specimens with different α’-martensite
contents and wall-thickness reductions were produced. An equation to compute α’-martensite
from both specific production-process parameters and micromagnetic Barkhausen
noise (MBN) measurements was obtained using numerical methods. In this process,
the behavior of the quantity of interest (namely, the α’-martensite content) was
mathematically evaluated with respect to non-destructive MBN data and the feed
rate that was used to produce the components. A combination of exponential and
potential functions was defined as the ansatz functions of the model. The obtained
model was validated online and offline during the real flow forming of workpieces,
obtaining average deviations of up to 7% α’-martensite with respect to the model.
The implementation of the soft sensor model for property-controlled production
represents an important milestone for producing high-added-value components on
the basis of a well-understood process-microstructure-property relationship.
author:
- first_name: 'Julian '
full_name: 'Rozo Vasquez, Julian '
last_name: Rozo Vasquez
- first_name: Lukas
full_name: Kersting, Lukas
last_name: Kersting
- first_name: Bahman
full_name: Arian, Bahman
id: '36287'
last_name: Arian
- first_name: Werner
full_name: Homberg, Werner
id: '233'
last_name: Homberg
- first_name: Ansgar
full_name: Trächtler, Ansgar
id: '552'
last_name: Trächtler
- first_name: Frank
full_name: Walther, Frank
last_name: Walther
citation:
ama: 'Rozo Vasquez J, Kersting L, Arian B, Homberg W, Trächtler A, Walther F. Soft
Sensor Model of Phase Transformation During Flow Forming of Metastable Austenitic
Steel AISI 304L. In: Lecture Notes in Mechanical Engineering. Springer
International Publishing; 2024. doi:10.1007/978-3-031-58006-2_10'
apa: Rozo Vasquez, J., Kersting, L., Arian, B., Homberg, W., Trächtler, A., &
Walther, F. (2024). Soft Sensor Model of Phase Transformation During Flow Forming
of Metastable Austenitic Steel AISI 304L. In Lecture Notes in Mechanical Engineering.
Springer International Publishing. https://doi.org/10.1007/978-3-031-58006-2_10
bibtex: '@inbook{Rozo Vasquez_Kersting_Arian_Homberg_Trächtler_Walther_2024, place={Cham},
title={Soft Sensor Model of Phase Transformation During Flow Forming of Metastable
Austenitic Steel AISI 304L}, DOI={10.1007/978-3-031-58006-2_10},
booktitle={Lecture Notes in Mechanical Engineering}, publisher={Springer International
Publishing}, author={Rozo Vasquez, Julian and Kersting, Lukas and Arian, Bahman
and Homberg, Werner and Trächtler, Ansgar and Walther, Frank}, year={2024} }'
chicago: 'Rozo Vasquez, Julian , Lukas Kersting, Bahman Arian, Werner Homberg, Ansgar
Trächtler, and Frank Walther. “Soft Sensor Model of Phase Transformation During
Flow Forming of Metastable Austenitic Steel AISI 304L.” In Lecture Notes in
Mechanical Engineering. Cham: Springer International Publishing, 2024. https://doi.org/10.1007/978-3-031-58006-2_10.'
ieee: 'J. Rozo Vasquez, L. Kersting, B. Arian, W. Homberg, A. Trächtler, and F.
Walther, “Soft Sensor Model of Phase Transformation During Flow Forming of Metastable
Austenitic Steel AISI 304L,” in Lecture Notes in Mechanical Engineering,
Cham: Springer International Publishing, 2024.'
mla: Rozo Vasquez, Julian, et al. “Soft Sensor Model of Phase Transformation During
Flow Forming of Metastable Austenitic Steel AISI 304L.” Lecture Notes in Mechanical
Engineering, Springer International Publishing, 2024, doi:10.1007/978-3-031-58006-2_10.
short: 'J. Rozo Vasquez, L. Kersting, B. Arian, W. Homberg, A. Trächtler, F. Walther,
in: Lecture Notes in Mechanical Engineering, Springer International Publishing,
Cham, 2024.'
date_created: 2024-11-18T10:24:06Z
date_updated: 2024-11-18T10:39:03Z
department:
- _id: '153'
- _id: '241'
- _id: '156'
doi: 10.1007/978-3-031-58006-2_10
language:
- iso: eng
place: Cham
publication: Lecture Notes in Mechanical Engineering
publication_identifier:
isbn:
- '9783031580055'
- '9783031580062'
issn:
- 2195-4356
- 2195-4364
publication_status: published
publisher: Springer International Publishing
quality_controlled: '1'
status: public
title: Soft Sensor Model of Phase Transformation During Flow Forming of Metastable
Austenitic Steel AISI 304L
type: book_chapter
user_id: '41470'
year: '2024'
...
---
_id: '57185'
author:
- first_name: Fabian
full_name: Reiling, Fabian
last_name: Reiling
- first_name: Christian
full_name: Henke, Christian
last_name: Henke
- first_name: Matthias
full_name: Hunstig, Matthias
last_name: Hunstig
- first_name: Stefan
full_name: Gröger, Stefan
last_name: Gröger
- first_name: Ansgar
full_name: Trächtler, Ansgar
id: '552'
last_name: Trächtler
citation:
ama: 'Reiling F, Henke C, Hunstig M, Gröger S, Trächtler A. Batch constrained multi-objective
Bayesian optimization using the example of ultrasonic wire bonding. In: 2024
IEEE International Conference on Advanced Intelligent Mechatronics (AIM).
IEEE; 2024. doi:10.1109/aim55361.2024.10637123'
apa: Reiling, F., Henke, C., Hunstig, M., Gröger, S., & Trächtler, A. (2024).
Batch constrained multi-objective Bayesian optimization using the example of ultrasonic
wire bonding. 2024 IEEE International Conference on Advanced Intelligent Mechatronics
(AIM). https://doi.org/10.1109/aim55361.2024.10637123
bibtex: '@inproceedings{Reiling_Henke_Hunstig_Gröger_Trächtler_2024, title={Batch
constrained multi-objective Bayesian optimization using the example of ultrasonic
wire bonding}, DOI={10.1109/aim55361.2024.10637123},
booktitle={2024 IEEE International Conference on Advanced Intelligent Mechatronics
(AIM)}, publisher={IEEE}, author={Reiling, Fabian and Henke, Christian and Hunstig,
Matthias and Gröger, Stefan and Trächtler, Ansgar}, year={2024} }'
chicago: Reiling, Fabian, Christian Henke, Matthias Hunstig, Stefan Gröger, and
Ansgar Trächtler. “Batch Constrained Multi-Objective Bayesian Optimization Using
the Example of Ultrasonic Wire Bonding.” In 2024 IEEE International Conference
on Advanced Intelligent Mechatronics (AIM). IEEE, 2024. https://doi.org/10.1109/aim55361.2024.10637123.
ieee: 'F. Reiling, C. Henke, M. Hunstig, S. Gröger, and A. Trächtler, “Batch constrained
multi-objective Bayesian optimization using the example of ultrasonic wire bonding,”
2024, doi: 10.1109/aim55361.2024.10637123.'
mla: Reiling, Fabian, et al. “Batch Constrained Multi-Objective Bayesian Optimization
Using the Example of Ultrasonic Wire Bonding.” 2024 IEEE International Conference
on Advanced Intelligent Mechatronics (AIM), IEEE, 2024, doi:10.1109/aim55361.2024.10637123.
short: 'F. Reiling, C. Henke, M. Hunstig, S. Gröger, A. Trächtler, in: 2024 IEEE
International Conference on Advanced Intelligent Mechatronics (AIM), IEEE, 2024.'
date_created: 2024-11-18T10:18:18Z
date_updated: 2024-11-18T10:40:23Z
department:
- _id: '153'
- _id: '241'
doi: 10.1109/aim55361.2024.10637123
language:
- iso: eng
publication: 2024 IEEE International Conference on Advanced Intelligent Mechatronics
(AIM)
publication_status: published
publisher: IEEE
quality_controlled: '1'
status: public
title: Batch constrained multi-objective Bayesian optimization using the example of
ultrasonic wire bonding
type: conference
user_id: '41470'
year: '2024'
...
---
_id: '57176'
abstract:
- lang: eng
text: Incremental nonlinear dynamic inversion (INDI) is a widely used approach to
controlling UAVs with highly nonlinear dynamics. One key element of INDI-based
controllers is the control allocation realizing pseudo controls using available
actuators. However, the tracking of commanded pseudo controls is not the only
objective considered during control allocation. Since the approach only works
locally due to linearization and the solution is often ambiguous, additional aspects
like control efforts or penalizing the deviation of certain states must be considered.
Conducting the control allocation by solving a quadratic program this results
in a considerable number of weighting parameters, which must be tuned during control
design. Currently, this is conducted manually and is therefore time consuming.
An automated approach for tuning these parameters is therefore highly beneficial.
Thus, this paper presents and evaluates a model-based approach automatically tuning
the control allocation parameters of a tiltrotor VTOL using an optimization algorithm.
This optimization algorithm searches for optimal parameters minimizing a cost
functional that reflects the design target. This cost functional is calculated
based on a test mission for the VTOL which is conducted within a simulation environment.
The test mission represents the common operating range of the VTOL. The simulation
environment consists of an aircraft model as well as a model of the INDI-based
controller which is dependent on the control allocation parameters. On this basis,
model-based optimization is conducted and the optimal parameters are identified.
Finally, successful real-world tests on a 4-degrees-of-freedom testbench using
the identified parameters are presented. Since the control allocation parameters
can significantly influence the aircraft’s stability, the 4-DOF testbench for
the aircraft is required for rapid validation of the parameters at a minimum amount
of risk.
article_number: '187'
author:
- first_name: Mark
full_name: Henkenjohann, Mark
last_name: Henkenjohann
- first_name: Udo
full_name: Nolte, Udo
last_name: Nolte
- first_name: Fabian
full_name: Sion, Fabian
last_name: Sion
- first_name: Christian
full_name: Henke, Christian
last_name: Henke
- first_name: Ansgar
full_name: Trächtler, Ansgar
id: '552'
last_name: Trächtler
citation:
ama: Henkenjohann M, Nolte U, Sion F, Henke C, Trächtler A. Parameter Tuning Approach
for Incremental Nonlinear Dynamic Inversion-Based Flight Controllers. Actuators.
2024;13(5). doi:10.3390/act13050187
apa: Henkenjohann, M., Nolte, U., Sion, F., Henke, C., & Trächtler, A. (2024).
Parameter Tuning Approach for Incremental Nonlinear Dynamic Inversion-Based Flight
Controllers. Actuators, 13(5), Article 187. https://doi.org/10.3390/act13050187
bibtex: '@article{Henkenjohann_Nolte_Sion_Henke_Trächtler_2024, title={Parameter
Tuning Approach for Incremental Nonlinear Dynamic Inversion-Based Flight Controllers},
volume={13}, DOI={10.3390/act13050187},
number={5187}, journal={Actuators}, publisher={MDPI AG}, author={Henkenjohann,
Mark and Nolte, Udo and Sion, Fabian and Henke, Christian and Trächtler, Ansgar},
year={2024} }'
chicago: Henkenjohann, Mark, Udo Nolte, Fabian Sion, Christian Henke, and Ansgar
Trächtler. “Parameter Tuning Approach for Incremental Nonlinear Dynamic Inversion-Based
Flight Controllers.” Actuators 13, no. 5 (2024). https://doi.org/10.3390/act13050187.
ieee: 'M. Henkenjohann, U. Nolte, F. Sion, C. Henke, and A. Trächtler, “Parameter
Tuning Approach for Incremental Nonlinear Dynamic Inversion-Based Flight Controllers,”
Actuators, vol. 13, no. 5, Art. no. 187, 2024, doi: 10.3390/act13050187.'
mla: Henkenjohann, Mark, et al. “Parameter Tuning Approach for Incremental Nonlinear
Dynamic Inversion-Based Flight Controllers.” Actuators, vol. 13, no. 5,
187, MDPI AG, 2024, doi:10.3390/act13050187.
short: M. Henkenjohann, U. Nolte, F. Sion, C. Henke, A. Trächtler, Actuators 13
(2024).
date_created: 2024-11-18T10:09:58Z
date_updated: 2024-11-18T10:42:19Z
department:
- _id: '153'
- _id: '241'
doi: 10.3390/act13050187
intvolume: ' 13'
issue: '5'
language:
- iso: eng
publication: Actuators
publication_identifier:
issn:
- 2076-0825
publication_status: published
publisher: MDPI AG
quality_controlled: '1'
status: public
title: Parameter Tuning Approach for Incremental Nonlinear Dynamic Inversion-Based
Flight Controllers
type: journal_article
user_id: '41470'
volume: 13
year: '2024'
...
---
_id: '57173'
abstract:
- lang: eng
text: Manufacturing processes benefit from property control enabling reproducibility,
application oriented outcomes, and efficient part production. In reverse flow
forming, state of the art practices focus primarily on geometry control, neglecting
property control. Given the intricacies of the process involving the interaction
of tool and machine behavior, process parameters, properties of semi finished
products and temperatures, incorporating process control becomes an imperative
for producing components with predefined properties. The property controlled within
this reverse flow forming process is the local α’ martensite content. Therefore,
process strategies to actively influence the α’ martensite content must be implemented.
In this study seamless AISI 304L steel tubes are used, where α’ martensite formation
is strain and/or temperature induced through phase transformation within the
process. This paper presents innovative process strategies, methods, and specially
developed mechanical and thermal actuator systems to locally increase or suppress
the α’ martensite content. The use and implementation of these approaches and
tools allows the creation of unique optically invisible microstructure profiles
containing 3D gradings, implying a radial grading of α’ martensite. The locally
implemented α’ martensite, forming these 3D gradings, offers potential applications
for functional or sensory purposes. This paper extends beyond theoretical concepts,
providing tangible component outcomes.
author:
- first_name: Bahman
full_name: Arian, Bahman
id: '36287'
last_name: Arian
- first_name: Werner
full_name: Homberg, Werner
id: '233'
last_name: Homberg
- first_name: Lukas
full_name: Kersting, Lukas
last_name: Kersting
- first_name: Ansgar
full_name: Trächtler, Ansgar
id: '552'
last_name: Trächtler
- first_name: Julian
full_name: Rozo Vasquez, Julian
last_name: Rozo Vasquez
- first_name: Frank
full_name: Walther, Frank
last_name: Walther
citation:
ama: 'Arian B, Homberg W, Kersting L, Trächtler A, Rozo Vasquez J, Walther F. α’-martensite
grading techniques in reverse flow forming of AISI 304L. In: Materials Research
Proceedings. Vol 44. Materials Research Forum LLC; 2024. doi:10.21741/9781644903254-76'
apa: Arian, B., Homberg, W., Kersting, L., Trächtler, A., Rozo Vasquez, J., &
Walther, F. (2024). α’-martensite grading techniques in reverse flow forming of
AISI 304L. Materials Research Proceedings, 44. https://doi.org/10.21741/9781644903254-76
bibtex: '@inproceedings{Arian_Homberg_Kersting_Trächtler_Rozo Vasquez_Walther_2024,
title={α’-martensite grading techniques in reverse flow forming of AISI 304L},
volume={44}, DOI={10.21741/9781644903254-76},
booktitle={Materials Research Proceedings}, publisher={Materials Research Forum
LLC}, author={Arian, Bahman and Homberg, Werner and Kersting, Lukas and Trächtler,
Ansgar and Rozo Vasquez, Julian and Walther, Frank}, year={2024} }'
chicago: Arian, Bahman, Werner Homberg, Lukas Kersting, Ansgar Trächtler, Julian
Rozo Vasquez, and Frank Walther. “α’-Martensite Grading Techniques in Reverse
Flow Forming of AISI 304L.” In Materials Research Proceedings, Vol. 44.
Materials Research Forum LLC, 2024. https://doi.org/10.21741/9781644903254-76.
ieee: 'B. Arian, W. Homberg, L. Kersting, A. Trächtler, J. Rozo Vasquez, and F.
Walther, “α’-martensite grading techniques in reverse flow forming of AISI 304L,”
in Materials Research Proceedings, 2024, vol. 44, doi: 10.21741/9781644903254-76.'
mla: Arian, Bahman, et al. “α’-Martensite Grading Techniques in Reverse Flow Forming
of AISI 304L.” Materials Research Proceedings, vol. 44, Materials Research
Forum LLC, 2024, doi:10.21741/9781644903254-76.
short: 'B. Arian, W. Homberg, L. Kersting, A. Trächtler, J. Rozo Vasquez, F. Walther,
in: Materials Research Proceedings, Materials Research Forum LLC, 2024.'
date_created: 2024-11-18T10:06:17Z
date_updated: 2024-11-18T10:42:55Z
department:
- _id: '241'
- _id: '153'
- _id: '156'
doi: 10.21741/9781644903254-76
intvolume: ' 44'
language:
- iso: eng
publication: Materials Research Proceedings
publication_identifier:
issn:
- 2474-395X
publication_status: published
publisher: Materials Research Forum LLC
quality_controlled: '1'
status: public
title: α’-martensite grading techniques in reverse flow forming of AISI 304L
type: conference
user_id: '41470'
volume: 44
year: '2024'
...
---
_id: '57111'
author:
- first_name: Martin Miroslavov
full_name: Mihaylov, Martin Miroslavov
id: '42449'
last_name: Mihaylov
- first_name: Christian
full_name: Kress, Christian
id: '13256'
last_name: Kress
- first_name: J. Christoph
full_name: Scheytt, J. Christoph
id: '37144'
last_name: Scheytt
orcid: '0000-0002-5950-6618 '
citation:
ama: 'Mihaylov MM, Kress C, Scheytt JC. Simulation and Optimization of Low-Loss
Photonic Coupling Structures for TFLN Integrated Circuits for Quantum Applications.
In: ; 2024.'
apa: Mihaylov, M. M., Kress, C., & Scheytt, J. C. (2024). Simulation and
Optimization of Low-Loss Photonic Coupling Structures for TFLN Integrated Circuits
for Quantum Applications. Quantum Photonics Spotlight , Paderborn.
bibtex: '@inproceedings{Mihaylov_Kress_Scheytt_2024, title={Simulation and Optimization
of Low-Loss Photonic Coupling Structures for TFLN Integrated Circuits for Quantum
Applications}, author={Mihaylov, Martin Miroslavov and Kress, Christian and Scheytt,
J. Christoph}, year={2024} }'
chicago: Mihaylov, Martin Miroslavov, Christian Kress, and J. Christoph Scheytt.
“Simulation and Optimization of Low-Loss Photonic Coupling Structures for TFLN
Integrated Circuits for Quantum Applications,” 2024.
ieee: M. M. Mihaylov, C. Kress, and J. C. Scheytt, “Simulation and Optimization
of Low-Loss Photonic Coupling Structures for TFLN Integrated Circuits for Quantum
Applications,” presented at the Quantum Photonics Spotlight , Paderborn, 2024.
mla: Mihaylov, Martin Miroslavov, et al. Simulation and Optimization of Low-Loss
Photonic Coupling Structures for TFLN Integrated Circuits for Quantum Applications.
2024.
short: 'M.M. Mihaylov, C. Kress, J.C. Scheytt, in: 2024.'
conference:
end_date: 2024,10,10
location: Paderborn
name: 'Quantum Photonics Spotlight '
start_date: 2024,10,08
date_created: 2024-11-15T13:47:10Z
date_updated: 2024-11-15T14:01:51Z
department:
- _id: '58'
- _id: '623'
language:
- iso: eng
status: public
title: Simulation and Optimization of Low-Loss Photonic Coupling Structures for TFLN
Integrated Circuits for Quantum Applications
type: conference_abstract
user_id: '42449'
year: '2024'
...
---
_id: '57429'
author:
- first_name: Bettina
full_name: Krueger, Bettina
id: '49428'
last_name: Krueger
orcid: 0000-0001-5351-1785
- first_name: Bianca
full_name: Stutz, Bianca
id: '77099'
last_name: Stutz
- first_name: Rasmus
full_name: Jakobsmeyer, Rasmus
id: '9583'
last_name: Jakobsmeyer
orcid: 0000-0002-9385-0834
- first_name: Claus
full_name: Reinsberger, Claus
id: '48978'
last_name: Reinsberger
- first_name: Anette E.
full_name: Buyken, Anette E.
id: '65985'
last_name: Buyken
citation:
ama: Krueger B, Stutz B, Jakobsmeyer R, Reinsberger C, Buyken AE. Relevance of high
glycaemic index breakfast for heart rate variability among collegiate students
with early and late chronotypes. Chronobiology International. Published
online 2024:1-10. doi:10.1080/07420528.2024.2428203
apa: Krueger, B., Stutz, B., Jakobsmeyer, R., Reinsberger, C., & Buyken, A.
E. (2024). Relevance of high glycaemic index breakfast for heart rate variability
among collegiate students with early and late chronotypes. Chronobiology International,
1–10. https://doi.org/10.1080/07420528.2024.2428203
bibtex: '@article{Krueger_Stutz_Jakobsmeyer_Reinsberger_Buyken_2024, title={Relevance
of high glycaemic index breakfast for heart rate variability among collegiate
students with early and late chronotypes}, DOI={10.1080/07420528.2024.2428203},
journal={Chronobiology International}, publisher={Informa UK Limited}, author={Krueger,
Bettina and Stutz, Bianca and Jakobsmeyer, Rasmus and Reinsberger, Claus and Buyken,
Anette E.}, year={2024}, pages={1–10} }'
chicago: Krueger, Bettina, Bianca Stutz, Rasmus Jakobsmeyer, Claus Reinsberger,
and Anette E. Buyken. “Relevance of High Glycaemic Index Breakfast for Heart Rate
Variability among Collegiate Students with Early and Late Chronotypes.” Chronobiology
International, 2024, 1–10. https://doi.org/10.1080/07420528.2024.2428203.
ieee: 'B. Krueger, B. Stutz, R. Jakobsmeyer, C. Reinsberger, and A. E. Buyken, “Relevance
of high glycaemic index breakfast for heart rate variability among collegiate
students with early and late chronotypes,” Chronobiology International,
pp. 1–10, 2024, doi: 10.1080/07420528.2024.2428203.'
mla: Krueger, Bettina, et al. “Relevance of High Glycaemic Index Breakfast for Heart
Rate Variability among Collegiate Students with Early and Late Chronotypes.” Chronobiology
International, Informa UK Limited, 2024, pp. 1–10, doi:10.1080/07420528.2024.2428203.
short: B. Krueger, B. Stutz, R. Jakobsmeyer, C. Reinsberger, A.E. Buyken, Chronobiology
International (2024) 1–10.
date_created: 2024-11-26T10:51:33Z
date_updated: 2024-11-26T10:52:50Z
department:
- _id: '35'
- _id: '22'
doi: 10.1080/07420528.2024.2428203
language:
- iso: eng
page: 1-10
publication: Chronobiology International
publication_identifier:
issn:
- 0742-0528
- 1525-6073
publication_status: published
publisher: Informa UK Limited
status: public
title: Relevance of high glycaemic index breakfast for heart rate variability among
collegiate students with early and late chronotypes
type: journal_article
user_id: '49428'
year: '2024'
...
---
_id: '54356'
abstract:
- lang: eng
text: "Although there are numerous design and control methodologies for the LLC
resonant converter,\r\nthey often do not consider decentralized control strategies
to operate them as isolated DC-DC converters within a\r\ncascaded H-bridge. The
total output power of all LLC converters must be constant to supply a load such
as a wa-\r\nter electrolyzer. However, each individual LLC converter can vary
its output power as long as the total output\r\npower remains constant. This opens
new possibilities in increasing the system efficiency and robustness. Usually,\r\nthe
DC-link voltage of each module capacitor shows a 2nd harmonic voltage ripple.
However, the total stored energy\r\nin all DC-link capacitors is constant within
a grid period for a balanced three-phase system. By controlling each\r\nLLC converter’s
output power locally to be proportional to the energy stored in its DC-link capacitor,
modules with\r\na lower instantaneous DC-link voltage transfer less power to the
load than modules with a higher DC-link voltage.\r\nAs a result, a higher efficiency,
voltage gain and lower peak resonant capacitor voltage can be achieved with the\r\nsame
components. The 22.2kW experimental prototype of the LLC converter reaches an
efficiency of over 97% at\r\nresonance which is similar to the precalculated value."
author:
- first_name: Roland
full_name: Unruh, Roland
id: '34289'
last_name: Unruh
- first_name: Joachim
full_name: Böcker, Joachim
id: '66'
last_name: Böcker
orcid: 0000-0002-8480-7295
- first_name: Frank
full_name: Schafmeister, Frank
id: '71291'
last_name: Schafmeister
citation:
ama: 'Unruh R, Böcker J, Schafmeister F. Experimentally Verified 22 kW, 40 kHz LLC
Resonant Converter Design with new Control for a 1 MW Cascaded H-Bridge Converter.
In: ECCE Europe 2024; IEEE Energy Conversion Congress & Exposition Europe.
IEEE. doi:https://doi.org/10.1109/ECCEEurope62508.2024.10751954'
apa: Unruh, R., Böcker, J., & Schafmeister, F. (n.d.). Experimentally Verified
22 kW, 40 kHz LLC Resonant Converter Design with new Control for a 1 MW Cascaded
H-Bridge Converter. ECCE Europe 2024; IEEE Energy Conversion Congress &
Exposition Europe. ECCE Europe 2024, Darmstadt, Germany. https://doi.org/10.1109/ECCEEurope62508.2024.10751954
bibtex: '@inproceedings{Unruh_Böcker_Schafmeister, place={Darmstadt}, title={Experimentally
Verified 22 kW, 40 kHz LLC Resonant Converter Design with new Control for a 1
MW Cascaded H-Bridge Converter}, DOI={https://doi.org/10.1109/ECCEEurope62508.2024.10751954},
booktitle={ECCE Europe 2024; IEEE Energy Conversion Congress & Exposition
Europe}, publisher={IEEE}, author={Unruh, Roland and Böcker, Joachim and Schafmeister,
Frank} }'
chicago: 'Unruh, Roland, Joachim Böcker, and Frank Schafmeister. “Experimentally
Verified 22 KW, 40 KHz LLC Resonant Converter Design with New Control for a 1
MW Cascaded H-Bridge Converter.” In ECCE Europe 2024; IEEE Energy Conversion
Congress & Exposition Europe. Darmstadt: IEEE, n.d. https://doi.org/10.1109/ECCEEurope62508.2024.10751954.'
ieee: 'R. Unruh, J. Böcker, and F. Schafmeister, “Experimentally Verified 22 kW,
40 kHz LLC Resonant Converter Design with new Control for a 1 MW Cascaded H-Bridge
Converter,” presented at the ECCE Europe 2024, Darmstadt, Germany, doi: https://doi.org/10.1109/ECCEEurope62508.2024.10751954.'
mla: Unruh, Roland, et al. “Experimentally Verified 22 KW, 40 KHz LLC Resonant Converter
Design with New Control for a 1 MW Cascaded H-Bridge Converter.” ECCE Europe
2024; IEEE Energy Conversion Congress & Exposition Europe, IEEE, doi:https://doi.org/10.1109/ECCEEurope62508.2024.10751954.
short: 'R. Unruh, J. Böcker, F. Schafmeister, in: ECCE Europe 2024; IEEE Energy
Conversion Congress & Exposition Europe, IEEE, Darmstadt, n.d.'
conference:
end_date: 2024-09-06
location: Darmstadt, Germany
name: ECCE Europe 2024
start_date: 2024-09-02
date_created: 2024-05-19T14:26:29Z
date_updated: 2024-11-28T14:16:05Z
department:
- _id: '52'
doi: https://doi.org/10.1109/ECCEEurope62508.2024.10751954
keyword:
- Cascaded H-Bridge
- Converter Losses
- Decentralized Control
- Full-Bridge Converter
- LLC Resonant Converter
language:
- iso: eng
main_file_link:
- url: https://ieeexplore.ieee.org/abstract/document/10751954
place: Darmstadt
publication: ECCE Europe 2024; IEEE Energy Conversion Congress & Exposition Europe
publication_identifier:
isbn:
- 979-8-3503-6444-6
publication_status: accepted
publisher: IEEE
status: public
title: Experimentally Verified 22 kW, 40 kHz LLC Resonant Converter Design with new
Control for a 1 MW Cascaded H-Bridge Converter
type: conference
user_id: '34289'
year: '2024'
...