@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{59088, abstract = {{This paper deals with the implementation and results of the application of a multi-stage traffic light control system which includes a simulation-based traffic estimation and model predictive control. The traffic light control system incorporates a fuzzy system for traffic light phase preselection, followed by a model predictive control to optimise phase combinations and switching times. Predefined phases are selected without restrictions in the order according to a multi-objective optimisation to adapt to the traffic as freely as possible. Initially, the system is tested in simulations and compared with existing methods and analysed afterwards for its effectiveness in a prototype commissioning in field tests. Results indicate high potentials for reducing emissions and waiting times, highlighting the system's value. However, further refinement is necessary for standard implementation. This comprehensive approach demonstrates advancements in traffic management technology, showcasing the potential for enhancing urban mobility and reducing environmental impact.}}, author = {{Malena, Kevin and Link, Christopher and Gausemeier, Sandra and Trächtler, Ansgar}}, booktitle = {{2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC)}}, issn = {{2153-0017}}, keywords = {{MPC}}, location = {{Edmonton (Canada)}}, publisher = {{IEEE}}, title = {{{Implementation and Results of a Multi-Stage Model Predictive Traffic Light Control System}}}, doi = {{10.1109/itsc58415.2024.10919569}}, volume = {{27}}, year = {{2025}}, } @article{59740, abstract = {{ABSTRACTIn this contribution, we propose an innovative method for determining optimal control sequences for nonlinear systems with partially unknown dynamics, which further expands our previous work. Within the paradigm of model‐based design, the practicality and safety of commissioning feedforward controls and feedback controllers have priority. Our approach leverages probabilistic Gaussian processes to adjust for model inaccuracies from measured system data. This differs from conventional approaches that involve complicated analytical modeling and may entail a substantial time investment to acquire expertise and may prove impractical. Consequently, we address the limitations inherent in traditional design methodologies. Our research focuses on the formulation and solution of the hybrid1 optimal control problem using probabilistic state predictions and multiple shooting. This ensures adaptability, data efficiency, and resilience against uncertainties in system dynamics. These attributes are empirically substantiated through experimental validation on a chaotic and highly sensitive dynamical system—a double pendulum on a cart. Our methodology unfolds as an iterative learning process, systematically exploring diverse controls, accumulating data within each iteration, and refining the control strategy until the desired task is accomplished. The adoption of the two‐degree‐of‐freedom control structure allows for the distinct consideration of the feedforward and the feedback control signal. For the latter, we employ a time‐variant, linear quadratic regulator (LQR) designed to stabilize the system around its target trajectory. Furthermore, we integrate a probabilistic long‐term prediction through the unscented transform, enabling systematic anticipation of safety‐critical violations. Detailed insights into relevant implementation aspects are provided. To ascertain the real‐world applicability, we present an exemplary application involving a double pendulum on a cart. The objective is to bring the pendulum arms from the lower stable to the upper unstable equilibrium by horizontally moving the cart and subsequently stabilize them. In this scenario, we assume that the centrifugal forces, crucial to the system dynamics, have not been accurately modeled and must be learned from data. Solving the control task took only 5 iterations and 1 h of computation time, which surpasses our previous work [2], where we used the purely data‐driven PILCO framework and required 27 iterations and 57 h of computation time. The time of interaction with the system decreased by and the computation time is lowered by . It demonstrates significant practical applicability for commissioning control systems.}}, author = {{Hesse, Michael and Schwarzer, Luis and Timmermann, Julia and Trächtler, Ansgar}}, issn = {{1617-7061}}, journal = {{PAMM}}, number = {{2}}, publisher = {{Wiley}}, title = {{{Robust and Efficient Hybrid Optimal Control via Gaussian Process Regression and Multiple Shooting With Experimental Validation on a Double Pendulum on a Cart}}}, doi = {{10.1002/pamm.70004}}, volume = {{25}}, year = {{2025}}, } @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}}, } @inproceedings{53106, author = {{Bußemas, Leon and Fittkau, Niklas and Gausemeier, Sandra and Trächtler, Ansgar and Rüddenklau, Nico}}, booktitle = {{VDI Mechatroniktagung Dresden 2024}}, location = {{Dresden}}, pages = {{29--34}}, publisher = {{Technische Universität Dresden}}, title = {{{LiDAR-Sensormodell basierend auf zeitabhängigem Photon Mapping}}}, year = {{2024}}, } @article{56608, abstract = {{AbstractA new control method aims at the precise high dynamic control of the force signal for experimental vibration analysis, which is generated by an electrodynamic shaker. A bending beam is used as a nonlinear test object. A design and a surrogate model of the test rig are shown and parameterized based on test rig measurements. The force control algorithm using input/output linearisation is described and implemented in Matlab/Simulink for simulative validation studies. Conclusions drawn from the mathematical description of the problem as well as simulation results show that the design of the contact between shaker and test object is crucial to achieve a high control bandwidth and at the same time reduce the energy consumption of the shaker. This leads to the practical application using a novel damping contact element. Finally, experimental test rig results are presented which show a closed loop bandwidth of at least 250 Hz for sinusoidal excitation signals.}}, author = {{Lüke, Christopher and Trächtler, Ansgar}}, issn = {{0015-7899}}, journal = {{Forschung im Ingenieurwesen}}, number = {{1}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{Highly dynamic force control for experimental vibration analysis with an electrodynamic shaker Hochdynamische Kraftregelung für die experimentelle Schwingungsanalyse mit einem elektrodynamischen Shaker}}}, doi = {{10.1007/s10010-024-00757-z}}, volume = {{88}}, year = {{2024}}, } @inbook{57190, abstract = {{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 = {{Rozo Vasquez, Julian and Kersting, Lukas and Arian, Bahman and Homberg, Werner and Trächtler, Ansgar and Walther, Frank}}, booktitle = {{Lecture Notes in Mechanical Engineering}}, isbn = {{9783031580055}}, issn = {{2195-4356}}, publisher = {{Springer International Publishing}}, 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}}, year = {{2024}}, } @inproceedings{57189, abstract = {{This paper deals with micromagnetic measurements for online detection of strain-induced α’-martensite during plastic deformation of metastable austenitic steel AISI 304L. The operating principles of the sensors are Barkhausen noise (MBN) and eddy currents (EC), which are suitable for detection of microstructure evolution due to formation of ferromagnetic phases. Nevertheless, the description of the calibration and transformation models of the micromagnetic measurements into quantitative α’-martensite fractions is beyond the scope of this paper. The focus will be put on the qualification of different micromagnetic methods as well as of different measurement systems under conditions similar to the real ones during production, which is crucial for implementation of a property-controlled flow forming process. The investigation was carried out on tubular specimens produced by flow forming, which have different content of α’-martensite. To characterize the sensitivity of the sensors, different contact conditions between sensors and workpieces were reproduced. MBN sensors are suitable for detecting amount of α’-martensite, but the measurements are affected by the surface roughness. This entails that the calibration models for MBN sensors must take account of these effects. EC sensors show a closer match with the amount of α’-martensite without having major affectation by other effects.}}, author = {{Rozo Vasquez, Julian and Kanagarajah, Hanigah and Arian, Bahman and Kersting, Lukas and Homberg, Werner and Trächtler, Ansgar and Walther, Frank}}, publisher = {{Authorea, Inc.}}, title = {{{Barkhausen noise- and eddy current-based measurements for online detection of deformation-induced martensite during flow forming of metastable austenitic steel AISI 304L}}}, year = {{2024}}, } @article{57175, author = {{Bathelt, Lukas and Djakow, Eugen and Henke, Christian and Trächtler, Ansgar}}, issn = {{1877-0509}}, journal = {{Procedia Computer Science}}, pages = {{2018--2027}}, publisher = {{Elsevier BV}}, title = {{{Innovative measurement system for saber curvature observation in straightening processes}}}, doi = {{10.1016/j.procs.2024.02.024}}, volume = {{232}}, year = {{2024}}, }