@inproceedings{61492,
abstract = {{This paper deals with the development and results of a prediction framework for traffic light control systems as well as the usage and benefits of such predictions in green light optimal speed advisory (GLOSA) scenarios.
Various machine learning methods like support vector machines, neural networks or reinforcement learning were evaluated for their applicability in the prediction context and compared based on their efficiency and most importantly accuracy. The resulting prediction framework uses decision tree ensemble models combined with certain model knowledge to forecast different control strategies. This method was chosen due to its best performance in various test scenarios. Very high accuracy and fidelity were achieved for standard control methods like fixed-time, time-of-day-based and 'ordinary' traffic-based programs. Only for the more sophisticated model predictive control which was tested lower accuracies were achieved.
For the upcoming GLOSA application the penetration of equipped vehicles was varied for different traffic scenarios and control strategies. Results showcase high potentials for enhancing urban mobility and reducing environmental impact by lower emissions and waiting times. However, it is also clear from the studies presented in this contribution that the coordination of the control strategy with the GLOSA vehicles is of enormous importance.}},
author = {{Malena, Kevin and Link, Christopher and Gausemeier, Sandra and Trächtler, Ansgar}},
booktitle = {{2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC)}},
keywords = {{ML, Prediction, Tree Ensembles, GLOSA}},
location = {{Gold Coast (Australia)}},
publisher = {{IEEE}},
title = {{{ML-based Prediction Framework for varying Traffic Signal Control Strategies and its GLOSA-application}}},
volume = {{28}},
year = {{2026}},
}
@inproceedings{66545,
abstract = {{The key to achieving energy efficiency and reducing carbon emissions in transportation lies in the effectiveness of vehicles and their associated mechatronic systems. A notable example of a mechatronic system is the headlamp at the front of a vehicle, which emits adaptive headlights, such as the well-known adaptive cornering lights or glare-free high beam. The rise of these and other Advanced Driver Assistance Systems (ADAS) has led to a proliferation of sensor data. However, the full potential of ADAS sensor data for increasing energy efficiency has not yet been exploited in the past. This study demonstrates that utilizing the data to dynamically adapt headlights to the upcoming driving scenario leads to a substantial reduction in power consumption. This objective is pursued by focusing on the predominant carbon footprint driver, namely the low beam.}},
author = {{Fittkau, Niklas and Bußemas, Leon and Malena, Kevin and Gausemeier, Sandra and Trächtler, Ansgar}},
booktitle = {{2026 IEEE Intelligent Vehicles Symposium (IV)}},
keywords = {{intelligent headlights, energy efficiency, ecodriving, decarbonization, matrix LED headlights, adaptive realtime control, ADAS systems, feedforward control, simulation}},
location = {{Detroit}},
publisher = {{IEEE}},
title = {{{Intelligent headlights boost energy efficiency and drive decarbonization}}},
volume = {{37}},
year = {{2026}},
}
@inproceedings{67194,
abstract = {{Efficient thermal management in vehicles is becoming increasingly critical due to stricter energy efficiency requirements and rising passenger expectations for comfort. Conventional heating, ventilation, and air conditioning (HVAC) systems rely on numerous air outlet temperature sensors, which add significant costs at scale. This study introduces a hybrid virtual sensor concept that combines physics-based mathematical modeling with data-driven methods to address this challenge. A detailed physical model predicts air outlet temperatures, while neural networks capture unmodeled heat exchanges and environmental disturbances. Two hybrid strategies are explored: one augments the physical model with a neural network to learn residual heat flows, and the other employs an adiabatic physical model enhanced by a neural network that maps the total environmental heat exchange, supported by a disturbance observer for accurate heat flow estimation. Evaluation using measurement data from a real test vehicle in a simplified two-vent scenario demonstrates that hybrid virtual sensors can provide reliable temperature estimates, reduce reliance on hardware sensors, and maintain high control performance, highlighting the potential of integrating physics-based and machine learning methods in vehicle climate control systems.}},
author = {{Timmermann, Julia Magdalena and Biemelt, Patrick and Li, Changcheng and Trächtler, Ansgar and Pankratz, Eduard}},
booktitle = {{2026 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)}},
isbn = {{979-8-3195-3612-9}},
issn = {{ 2159-6255 }},
location = {{Genova, Italy}},
publisher = {{IEEE}},
title = {{{Hybrid modeling as a virtual sensor concept for automotive HVAC outlet temperature estimation}}},
doi = {{10.1109/aim65483.2026.11658345}},
year = {{2026}},
}
@inproceedings{67208,
abstract = {{Produktfälschungen stellen die Industrie vor große Herausforderungen, da sie das Risiko eines Reputationsverlustes für die Produzenten bergen sowie im schlimmsten Fall die Sicherheit der Produktnutzer gefährden. Ein möglicher Ansatz, um Bauteilfälschungen z.B. in der Luft- und Raumfahrtindustrie sowie im Automotive-Bereich künftig zu verhindern, besteht darin, einen unverwechselbaren, unumkehrbaren „Identifier“ in das Originalbauteil zu integrieren. Dies könnte beispielsweise ein unsichtbarer magnetischer Bar- oder QR-Code sein, der während der Fertigung direkt in das Bauteil integriert wird. Die gezielte Beeinflussung der magnetischen Eigenschaften während des Fertigungsprozesses von Bauteilen durch die Umformtechnik stellt nach aktuellem Stand der Technik eine anspruchsvolle Herausforderung dar. Ein vielversprechender Forschungsansatz zur Lösung dieses Problems ist das Konzept der sogenannten Eigenschaftsregelung. Bei diesem Konzept werden die Bauteileigenschaften mittels intelligenter Sensoren direkt während der Fertigung gemessen und durch die Maschinenaktorik in den Prozess zurückgekoppelt. Dies ermöglicht eine dynamische Anpassung der Fertigungsparameter, um die gewünschten Eigenschaften des Bauteils zu erreichen. Dieser Beitrag beleuchtet am Beispiel des Drückwalzprozesses für metastabil-austenitischer Edelstähle, wie mit einer solchen Eigenschaftsregelung definierte und gradierte Eigenschaftsstrukturen z.B. zur Verhinderung von Produktfälschungen hergestellt werden können. Eine Hauptherausforderung bei der Entwicklung der Eigenschaftsregelung für das Drückwalzen metastabil-austenitischer Edelstähle ist insbesondere die Online-Messbarkeit des Gefüges in-situ im Prozess. Hierzu ist innerhalb der Eigenschaftsregelung eine indirekte, zerstörungsfreie Messung mittels einer neuartigen, intelligenten Softsensorik unabdingbar. Diese Sensorik besitzt eine charakteristische Dynamik und Ortsauflösung, welche mittelbar die Auflösung der geregelt erzeugten Gradierungen beeinflusst, d.h. die Schärfe der Abgrenzung zwischen martensitischen und nicht-martensitischen Bereichen. Dieser Aspekt wird Rahmen des vorliegenden Beitrags im Rahmen einer Simulationsstudie näher beleuchtet und ein Quervergleich zwischen Sensordynamik, Regelungsbandbreite und resultierenden und der erreichbaren Gradierungsauflösung gezogen. Insbesondere der letzte Aspekt ist von hoher Relevanz für einen etwaigen zukünftigen Technologietransfer der smarten, funktionsintegrierten Strukturen von der Forschung in die industrielle Anwendung.}},
author = {{Kersting, Lukas and Arian, Bahman and Rozo Vasquez, Julian and Trächtler, Ansgar and Homberg, Werner and Walther, Frank}},
editor = {{Herold, Sven and Monner, Hans Peter}},
location = {{Darmstadt}},
pages = {{71--81}},
title = {{{Das Drückwalzen mit Eigenschaftsregelung - mittels intelligenter Sensorik zu smarten, funktionsintegrierten Gradierungsstrukturen}}},
year = {{2026}},
}
@article{67171,
abstract = {{Flow forming of metastable austenites is an innovative, incremental metal forming process with special capabilities due to the TRIP effect. However, the TRIP effect during flow forming is significantly affected by disturbances and especially batch fluctuations leading to process uncertainty. This aspect is further analyzed and quantified in this paper to give insights on how to minimize the impact of uncertainty. For this purpose, semifinished parts and resulting flow forming workpieces are systemically characterized concerning their properties and the property uncertainty supported by mathematical methods like correlation analysis and error propagation. A result is that the most influencing impact factor on the strain induced α’-martensite volume fraction as a material property are batch fluctuations, specifically the variations of the chemical composition. Those especially appear from batch to batch, but also within a batch accompanied by e.g. temperature effects. To counter this challenge, different methods from control theory like closed-loop property control and adaptive control can be applied to flow forming. Thus, uncertainty will be reduced to increase process robustness and to enable industrial exploitation of the TRIP effect in flow forming of metastable austenitic steels.}},
author = {{Kersting, Lukas and Koyuncu, Mustafa and Arian, Bahman and Rozo Vasquez, Julian and Trächtler, Ansgar and Homberg, Werner and Walther, Frank}},
issn = {{1662-9779}},
journal = {{Solid State Phenomena}},
pages = {{253--266}},
publisher = {{Trans Tech Publications, Ltd.}},
title = {{{Reducing the Uncertainty in Flow Forming of Metastable Austenites}}},
doi = {{10.4028/p-gad5rr}},
volume = {{389}},
year = {{2026}},
}
@inproceedings{67206,
abstract = {{Eigenschaftsregelungen sind zurzeit in der Forschung für umformende Fertigungsprozesse viel diskutierte mechatronische Systeme, mit denen die mechanischen Eigenschaften wie z.B. Festigkeit und Härte, die mikrostrukturellen Eigenschaften wie z.B. Korngröße und Gefügeanteile, oder auch die Bauteilgeometrie eines zu fertigenden Bauteils definiert eingestellt werden können. Ein wichtiger Aspekt bei dem Entwurf eines solchen mechatronischen Systems stellt aus regelungs- und automatisierungstechnischer Sicht die spezifische Dynamik der einzelnen charakteristischen Teilsysteme des mechatronischen Kreises (Umformprozess, Eigenschaftssensorik, Eigenschaftsregler, Umformmaschine) vor dem Hintergrund der Echtzeitfähigkeit dar. Diese wird im Rahmen dieses Beitrags am Beispiel des Drückwalzens metastabil-austenitische Edelstähle tiefergehend untersucht, auch, um neue Forschungsimpulse für die Weiterentwicklung von Eigenschaftsregelungssystem und einen etwaigen Industrietransfer zu liefern.}},
author = {{Kersting, Lukas and Arian, Bahman and Rozo Vasquez, Julian and Trächtler, Ansgar and Homberg, Werner and Walther, Frank}},
editor = {{Bertram, Torsten and Corves, Burkhard and Beitelschmidt, Michael}},
location = {{Berlin}},
pages = {{39--44}},
title = {{{Der Eigenschaftsgeregelte Drückwalzprozess als neues mechatronisches System – Analyse im Hinblick auf Echtzeitaspekte}}},
year = {{2026}},
}
@inproceedings{67235,
abstract = {{Die Asset Administration Shell (AAS) gilt als ein vielversprechender Ansatz für die industrielle Umsetzung eines digitalen Zwillings und somit für eine Erhöhung des Automatisierungsgrades. Eine innovative Anwendung der AAS stellt hierbei die hybride, datengetriebene Werkzeugauslegung von Umformprozessen dar. Der hybride Ansatz bezieht hierbei neben klassischen Modelldaten ebenfalls reale Prozessdaten sowie Erfahrungswissen mit ein. Hierdurch entsteht ein heterogener Datensatz, bestehend aus einer hohen Anzahl an Daten mit unterschiedlichen Modalitäten. Dieser Beitrag untersucht anhand des Stanzbiegeprozesses eine Automatisierungsarchitektur mittels AAS-Einbindung, die die heterogenen Datensätze automatisiert akquiriert, verarbeitet und maschinenlesbar zur Verfügung stellt. Hierdurch wird einem Machine Learning Modul ermöglicht, die Wirkzusammenhänge zwischen Prozessparametern und Messdaten zu erlernen und anhand dessen eine optimierte Werkzeugauslegung für spezifische Produktgeometrien auszugeben. Ein Fokus liegt auf der Schnittstelle der heterogenen Prozessdatenrepräsentation, mit der sowohl Anwender als auch ML-Modul interagieren. Diesbezüglich werden zwei Ansätze analysiert und hinsichtlich ihrer Performanz sowie Skalierbarkeit bewertet, um so die Basis für ein anwenderorientiertes Assistenzsystems zur Werkzeugauslegung für Umformprozesse zu bilden.}},
author = {{Peters, Henning and Kersting, Lukas and Trächtler, Ansgar}},
booktitle = {{Entwurf komplexer Automatisierungssysteme (EKA)}},
editor = {{Underberg, Lisa}},
location = {{Magdeburg}},
publisher = {{Otto von Guericke University Library}},
title = {{{Automatisierung der Verarbeitung heterogener Daten für eine datengetriebene Werkzeugauslegung von Stanzbiegeprozessen mittels Asset Administration Shell}}},
doi = {{10.25673/123346}},
year = {{2026}},
}
@article{67233,
abstract = {{A common challenge of punch-bending is the control and compensation of process deviations which are induced, e.g., by different material batches. The resulting deviations are currently countered iteratively using expert knowledge in the design phase as well as during the production process. However, this is not always successful due to the complex interactions between semi-finished product properties and forming tools. To automate the optimization of tool geometries and process adjustments, an accurate prediction model of correlations between process/tool parameters and product properties is necessary. In that regard, an application programming interface (API) aligned with the Asset Administration Shell (AAS) specification is developed utilizing a hybrid data-driven approach. It enables the transformation of various data formats e.g., from sensor and simulation data into a machine-readable structure. The API is linked to a digital twin and a database, to automatically gather requested information and provide the new structured data to a machine learning (ML) model. To validate the developed method, hybrid punch-bending data is automatically transferred to an ML model which then returns tool geometry suggestions for a defined product geometry.}},
author = {{Peters, Henning and Mazur, Andreas and Chaudhary, Brijesh and Kersting, Lukas and Trächtler, Ansgar and Hammer, Barbara}},
issn = {{1662-9795}},
journal = {{Key Engineering Materials}},
location = {{Thessaloniki}},
pages = {{43--54}},
publisher = {{Trans Tech Publications, Ltd.}},
title = {{{Method of an Automated Tool Design of Punch-Bending Processes Using the Asset Administration Shell}}},
doi = {{10.4028/p-yfy9oq}},
volume = {{1049}},
year = {{2026}},
}
@inproceedings{61975,
author = {{Bita, Isaac Mpidi and Hermelingmeier, Dominik and Gröger, Stefan and Hovemann, Aschot and Pfeifer, Stefan and Henke, Christian and Dumitrescu, Roman and Trächtler, Ansgar}},
booktitle = {{Procedia CIRP}},
issn = {{2212-8271}},
pages = {{874--879}},
publisher = {{Elsevier BV}},
title = {{{SmartHomeFarming: Trends, Challenges, and Solutions in a Digital and Sustainable Future}}},
doi = {{10.1016/j.procir.2025.08.149}},
volume = {{136}},
year = {{2025}},
}
@article{61762,
abstract = {{Abstract
In punch-bending, products such as brackets, electronic contacts or spring elements are produced from wire-shaped semi-finished products using separation processes and several successive forming processes. Within the multi-stage straightening and bending processes, cross-stage and quantity-dependent effects have a significant influence on the quality of the end product. In order to optimize the punch-bending process with regard to the resulting component deviations and waste rate, this article presents the concept of a digital twin for an innovative hybrid model of a multi-stage punch-bending process. To ensure efficient development and implementation of the digital twin, the graphical modeling notation DSL4DPiFS is used for additional support. It makes it possible to derive the required interfaces of the Asset Administration Shell of the hybrid data-driven model.}},
author = {{Peters, Henning and Mazur, Andreas and Pandey, Ankit Kumar and Trächtler, Ansgar and Hammer, Barbara and Homberg, Werner}},
issn = {{0178-2312}},
journal = {{at - Automatisierungstechnik}},
number = {{3}},
pages = {{173--184}},
publisher = {{Walter de Gruyter GmbH}},
title = {{{Development of a digital twin for data-driven modeling of punch-bending processes using a graphical modeling notation}}},
doi = {{10.1515/auto-2024-0112}},
volume = {{73}},
year = {{2025}},
}
@inproceedings{61763,
abstract = {{Abstract. Within the punch-bending process semi-finished products of strip or wire material are formed and punched in several subsequent steps into a finished product like brackets, mounts, contacts or spring elements. In the context of those multi-stage straightening and bending processes, cross-stage and quantity-dependent effects significantly leads to undesired component deviations. To optimize the punch-bending process with regard to these component deviations and thus the waste rate, the concept of a hybrid data-driven model is presented. To automatically acquire and process this hybrid data while also enable the usage by multiple clients, a digital twin has to be developed. In this paper the communication infrastructure between the punch-bending system and the digital twin is presented, using the Asset Administration Shell as specification. This automated communication is validated using exemplary data from the punch-bending system.}},
author = {{Peters, Henning and Mazur, Andreas and Trächtler, Ansgar and Hammer, Barbara}},
booktitle = {{Materials Research Proceedings}},
issn = {{2474-395X}},
publisher = {{Materials Research Forum LLC}},
title = {{{Integration of a digital twin for data-driven modeling of punch-bending processes using the asset administration shell}}},
doi = {{10.21741/9781644903599-166}},
volume = {{54}},
year = {{2025}},
}
@article{61761,
abstract = {{Abstract
Data-driven methods are increasingly utilized in metal forming processes for monitoring and quality optimization. An adapted modeling notation DSL4DPiFS for forming processes is presented to model hardware, software, and data flow aspects to support the design and analysis of data-driven methods. DSL4DPiFS enables metal forming and automation experts to model field-level information as data sources, and the data sinks for data analysis. The notation was adapted to the requirements of selected metal forming processes and evaluated in three case studies.}},
author = {{Vogel-Heuser, Birgit and Zhang, Mingxi and Krüger, Marius and Vicaria, Alejandra and Gardill, Markus and Jiang, Yuyao and Trächtler, Ansgar and Peters, Henning and Liewald, Mathias and Schenek, Adrian and Heinzelmann, Pascal and Weyrich, Michael}},
issn = {{0178-2312}},
journal = {{at - Automatisierungstechnik}},
number = {{4}},
pages = {{232--250}},
publisher = {{Walter de Gruyter GmbH}},
title = {{{DSL4DPiFS – a graphical notation to model data pipeline deployment in forming systems}}},
doi = {{10.1515/auto-2024-0114}},
volume = {{73}},
year = {{2025}},
}
@inbook{61765,
author = {{Mazur, Andreas and Peters, Henning and Artelt, André and Koller, Lukas and Hartmann, Christoph and Trächtler, Ansgar and Hammer, Barbara}},
booktitle = {{Lecture Notes in Computer Science}},
isbn = {{9783032045546}},
issn = {{0302-9743}},
publisher = {{Springer Nature Switzerland}},
title = {{{Studying the Generalization Behavior of Surrogate Models for Punch-Bending by Generating Plausible Counterfactuals}}},
doi = {{10.1007/978-3-032-04555-3_16}},
year = {{2025}},
}
@inproceedings{59907,
abstract = {{Abstract. Flow forming is recognized for its precision in producing rotationally symmetric components, but the use of metastable austenitic stainless steel (AISI 304L) introduces challenges due to uncontrolled strain-induced α’ martensite formation. Variations in factors such as eccentricity and batch inconsistencies lead to unpredictable microstructural profiles, limiting reproducibility [1,2]. This study addresses these issues by incorporating thermal actuators for cryogenic cooling and induction heating to regulate forming temperatures, enabling control of the α’-martensite content. Experimental investigations demonstrate that local tempering during thermomechanical reverse flow forming produces discernible variations in microstructure, affecting mechanical and magnetic properties [3]. Controlled local adjustments of α’-martensite content allow for customization of properties in seamless tubes, advancing manufacturing capabilities for complex, defect-free components. The results presented demonstrate promising strategies for implementation within the context of closed-loop property control in flow forming.}},
author = {{Arian, Bahman and Homberg, Werner and Kersting, Lukas and Trächtler, Ansgar and Rozo Vasquez, Julian and Walther, Frank}},
booktitle = {{Materials Research Proceedings}},
editor = {{Carlone, Pierpaolo and Filice, Luigino and Umbrello, Domenico}},
issn = {{2474-395X}},
keywords = {{Flow Forming, Thermomechanical Forming, α’-Martensite, Property Control}},
location = {{Paestum, Italy}},
publisher = {{Materials Research Forum LLC}},
title = {{{Advanced thermomechanical flow forming: A novel approach to α’-martensite control for enhanced material properties}}},
doi = {{10.21741/9781644903599-127}},
volume = {{54}},
year = {{2025}},
}
@article{62024,
abstract = {{Abstract
This paper presents a characterization of the microstructural evolution and its correlation with the magnetic structure due to flow forming of semi-finished tubes of austenitic stainless steel AISI 304L. The plastic deformation triggers a phase transformation of the metastable austenite into α’-martensite.
Depending on the combination of production parameters, different fractions of strain-induced α’-martensite were measured by means non-destructive micromagnetic techniques and correlated with the evolution of hardness and the microstructure using electron backscatter diffraction analyses. The magneto-optical Kerr effect analysis was used as a tool to perform a qualitative analysis of the evolution of the magnetic domain structure correlated with the formation of α’-martensite. An analysis of these data allowed to derive surface magnetization hysteresis loops that were compared with integral hysteresis loops of the specimens. It was proven by both methods that the formation of martensite increases the magnetic energy and the spontaneous magnetization of the specimens. The results of this investigation contribute to a better understanding of micromagnetic sensors to monitor and control the formation of α’-martensite in a flow forming. Furthermore, various techniques have demonstrated the evolution of the magnetic properties of the material, which can be applied in applications for invisible coding of workpieces.}},
author = {{Rozo Vasquez, Julian and Tappe, Jan and Arian, Bahman and Kersting, Lukas and Homberg, Werner and Trächtler, Ansgar and Walther, Frank}},
issn = {{2195-8599}},
journal = {{Practical Metallography}},
number = {{9-10}},
pages = {{617--633}},
publisher = {{Walter de Gruyter GmbH}},
title = {{{Magneto-optical Kerr effect analysis of strain-induced martensite formation during flow forming of metastable austenitic steel AISI 304L}}},
doi = {{10.1515/pm-2025-0059}},
volume = {{62}},
year = {{2025}},
}
@inproceedings{62022,
abstract = {{Abstract. The incremental flow forming process features a large number of process parameter combinations that can be varied from pass to pass or during a pass. In the future however, a more efficient utilization of this large number of process parameter combinations and a compensation of process disturbances could be required. This is due to a rising demand for increasing the part complexity, e.g. by graded property structures or a more complex geometry. In this context, innovative approaches like closed-loop property control and optimal control are advantageous, but require fast process models of flow forming that are not state of the art. This paper thus proposes a new modelling approach of multi-pass flow forming especially taking the transfer behavior between process parameters and wall thickness evolution from pass to pass into focus. A hybrid modelling approach is developed that combines knowledge about the incremental process character with empirical data regression to a basic analytic relation. The basic relation is further extended by a multi-layer neural network to enhance the overall model accuracy. This hybrid modelling approach is finally validated using experimental data. Thus, it is shown that a suitable model structure was found in context of a future closed-loop control or optimal control for multi-pass flow forming.}},
author = {{Kersting, Lukas and Gunasagran, Sharin Kumar and Arian, Bahman and Rozo Vaszquez, Julian and Trächtler, Ansgar and Homberg, Werner and Walther, Frank}},
booktitle = {{Materials Research Proceedings}},
issn = {{2474-395X}},
publisher = {{Materials Research Forum LLC}},
title = {{{Real-time modelling of incremental multi-pass flow forming by a hybrid, data-based model}}},
doi = {{10.21741/9781644903599-140}},
volume = {{54}},
year = {{2025}},
}
@article{62023,
abstract = {{Zusammenfassung
Die Eigenschaftsregelung mit einer online-Messung der Bauteileigenschaften ist ein in der Umformtechnik viel diskutiertes, aber kaum validiertes Konzept, um den Automatisierungsgrad bei der Bauteilfertigung weiter zu erhöhen. Dieser Artikel soll helfen, die Lücke beispielhaft für den Fertigungsprozess des Drückwalzens metastabiler Austenite zu schließen. Der metastabile austenitische Edelstahl ändert hierbei während der Verformung seinen α′-Martensitgehalt und damit verbunden die magnetischen Eigenschaften. Deshalb soll über die Regelung das definierte Einstellen des α′-Martensitgehaltes möglich werden. Im Rahmen des vorliegenden Artikels wird gezeigt, wie mittels des modellbasierten Entwurfs die Eigenschaftsregelung ausgelegt und parametriert werden kann. Zudem beinhaltet der Artikel experimentelle Validierungsergebnisse der zuvor entworfenen Eigenschaftsregelung.}},
author = {{Kersting, Lukas and Arian, Bahman and Rozo Vasquez, Julian and Trächtler, Ansgar and Homberg, Werner and Walther, Frank}},
issn = {{0178-2312}},
journal = {{at - Automatisierungstechnik}},
number = {{7}},
pages = {{527--540}},
publisher = {{Walter de Gruyter GmbH}},
title = {{{Modellbasierter Entwurf und Validierung einer Eigenschaftsregelung für das Drückwalzen metastabiler Austenite}}},
doi = {{10.1515/auto-2024-0127}},
volume = {{73}},
year = {{2025}},
}
@article{62021,
author = {{Kersting, Lukas and Arian, Bahman and Rozo Vasquez, Julian and Trächtler, Ansgar and Homberg, Werner and Walther, Frank}},
issn = {{2405-8963}},
journal = {{IFAC-PapersOnLine}},
number = {{1}},
pages = {{109--114}},
publisher = {{Elsevier BV}},
title = {{{State-space modelling approach for control and observer design in property-controlled reverse flow forming}}},
doi = {{10.1016/j.ifacol.2025.03.020}},
volume = {{59}},
year = {{2025}},
}
@inproceedings{61427,
abstract = {{The carbon footprint of modern vehicles and their mechatronic systems is more
important than ever. Research by the publicly funded Nalyses project and the HELLA
company shows that the headlamps use phase makes a significant contribution to the life
cycle footprint taking into account the current electricity mix [1]. Today, functionalities
such as adaptive curve light or glare-free high beam ensure comfort and safety by
assessing the state of the vehicle and evaluating the driving scenario ahead. In future,
this evaluation will be expanded and used to adapt the headlamp to the driving scenario
in such a way that as little light as possible is emitted, but as much light as necessary. In
order to achieve this goal, an overall evaluation of the regulatory compliant energy
saving potential is crucial in a first step and leads to constraints for a dynamic adaption
while driving. In this paper, the potential is illustrated by evaluating UNECE Regulation
No. 149 and optimizing luminous intensity distributions. Depending on the different
resolutions of matrix LED headlamps, this approach can result in a significantly lower
luminous flux. On the other hand, the results are point-like distributions that raise the
question of whether the regulation still provides for sensible minimum requirements for
modern matrix LED headlamps. The results are further presented in a simulated virtual
environment with regard to the resulting luminance in different driving scenarios. We
then present an approach to integrate regulatory requirements into a control algorithm by
setting optimization constraints and saturating the control. Finally, we classify the found
luminous intensity distributions qualitatively according to common lighting criteria. In summary, although the investigated minimum distributions are by no means desirable
for drivers themselves, they form the basis on which energy-saving distributions for
illuminated areas and twilight scenarios could be adaptively controlled in the future.}},
author = {{Fittkau, Niklas and Bußemas, Leon and Malena, Kevin and Gausemeier, Sandra and Trächtler, Ansgar}},
booktitle = {{Proceedings of the 16th International Symposium on Automotive Lighting 2025}},
location = {{Darmstadt}},
title = {{{Regulatory-compliant energy-saving potential for the passing beam of matrix LED headlamps}}},
doi = {{10.26083/tuprints-00030840}},
year = {{2025}},
}
@inproceedings{63527,
author = {{Henkenjohann, Mark and Nolte, Udo and Jahneke, Julien and Reimer, Oliver and Abrams, Stefan and Sion, Fabian and Henke, Christian and Trächtler, Ansgar and Schubert, Sebastian and Pfifer, Harald}},
booktitle = {{AIAA SCITECH 2025 Forum}},
publisher = {{American Institute of Aeronautics and Astronautics}},
title = {{{Dynamic Wind Tunnel Testing of an INDI-Based Flight Controller for a Tiltrotor-VTOL}}},
doi = {{10.2514/6.2025-2083}},
year = {{2025}},
}