@misc{56827,
  abstract     = {{Zusammenfassung: Die Erfindung betrifft ein Verkehrsleitsystem für die Steuerung von Lichtsignalanlagen aufweisend:
• Eingänge für Verkehrszustandsermittlungsverfahren abhängig von verschiedenen Sensoren, wobei die Sensoren einen aktuellen Verkehrszustand eines zugeordneten Verkehrsabschnitts ermitteln,
• Statusregister, die den aktuellen Status der gesteuerten Lichtsignalanlagen speichern,
• Phasenregister, die alle möglichen Verkehrsleitphasen, die durch die Lichtsignalanlagen angesteuert werden können, speichern,
• eine Fuzzy-Logik (F) zur Vorselektion von möglichen Verkehrsleitphasen basierend auf einem aktuellen Verkehrszustand, wobei für jede mögliche Verkehrsleitphase basierend auf dem aktuellen Verkehrszustand ein Prioritätswert ermittelt wird, wobei für die weitere Verarbeitung nur eine vorbestimmte Anzahl von vorselektierten nachfolgenden Verkehrsleitphasen anhand der Priorität ausgewählt wird,
• wobei die so vorausgewählten Verkehrsleitphasen einer modellprädiktiven Regelung mit einer Verkehrsprädiktionssimulation (MPC) zugeführt werden, wobei die Regelung basierend auf den Phasenfolgen (gebildet aus den vorselektierten Verkehrsleitphasen), dem aktuellen Verkehrszustand und dem aktuellen Status der gesteuerten Lichtsignalanlagen eine geeignete prädiktive zeitliche Phasensteuerung ermittelt, wobei die Schaltzeitpunkte der Phasenfolgen durch ein Optimierungsverfahren berechnet werden,
• wobei die so ermittelte prädiktive zeitliche Phasensteuerung keine, eine oder mehrere zu schaltenden Folgephasen mit den ermittelten Schaltzeitpunkten aufweist,
• wobei anhand der Bewertungen der einzelnen prädiktiven zeitlichen Phasensteuerungen ermittelt wird, welche der prädiktiven zeitlichen Phasensteuerungen zur weiteren Steuerung der Lichtsignalanlagen ausgewählt und verwendet wird.}},
  author       = {{Malena, Kevin and Link, Christopher and Gausemeier, Sandra and Trächtler, Ansgar}},
  title        = {{{Vorrichtung und Verfahren zur echtzeit-basierten dynamischen Verkehrszuordnung für zumindest zwei nachfolgende Fahrbahnen}}},
  year         = {{2023}},
}

@phdthesis{56937,
  abstract     = {{Ein Trend in der Produktion sind individualisierte Produkte und damit eine höhere Variantenvielfalt, die idealerweise durch effiziente Produktionsprozesse hergestellt werden. Effizienz bedeutet in diesem Fall, dass die Produkte kostengünstig und bedarfsgerecht hergestellt werden. Der Trend geht hierbei auch in der Massenproduktion zu einer flexiblen Produktion, um das gebundene Kapital zu reduzieren und zeitnah auf Kundenwünsche reagieren zu können. Dies führt zu einem häufigeren Umrüsten der Werkzeuge in den Maschinen und somit zu einem immer häufigeren Einrichten der Prozesse.In dieser Arbeit wird eine Systematik für ein adaptives Einrichtassistenzsystem entwickelt, welches den Maschinenbediener beim Einrichten mithilfe eines definierten Vorgehens durch den Prozess leitet. Der Bediener wird dabei unterstützt die momentane Produktqualität zu bewerten und bekommt einen quantitativen Vorschlag zur Variation der Justagemöglichkeiten. So kann der Einrichtprozess zielgerichteter und fehlerfreier durchgeführt werden. Ermöglicht wird dies durch die Abbildung des notwendigen Expertenwissens in datengetriebenen Modellen. Mithilfe des so abgebildeten Expertenwissens werden optimierte Einstellungen berechnet und am Werkzeug eingestellt. Es ist nicht davon auszugehen, dass das virtualisierte Expertenwissen die Realität allumfassend abbilden und alle akuten Umwelteinflüsse messtechnisch ermittelt werden können. Etwaige Abweichungen der Produktqualität werden direkt in der entwickelten Optimierung und bei Bedarf mithilfe der vorgeschlagenen Kompensationsstrategie eliminiert.Die Systematik wird anhand eines Folgeverbundprozesses validiert. Nach der Analyse des Prozesses werden die wesentlichen Komponenten für das System entwickelt und mithilfe von Versuchen und Simulationen die Funktionsfähigkeit erfolgreich nachgewiesen.}},
  author       = {{Gräler, Manuel}},
  publisher    = {{Verlagsschriftenreihe des Heinz Nixdorf Instituts}},
  title        = {{{Entwicklung adaptiver Einrichtassistenzsysteme für Produktionsprozesse}}},
  doi          = {{10.17619/UNIPB/1-1894}},
  volume       = {{417}},
  year         = {{2023}},
}

@inproceedings{34171,
  abstract     = {{State estimation when only a partial model of a considered system is available remains a major challenge in many engineering fields. This work proposes a joint, square-root unscented Kalman filter to estimate states and model uncertainties simultaneously by linear combinations of physics-motivated library functions. Using a sparsity promoting approach, a selection of those linear combinations is chosen and thus an interpretable model can be extracted. Results indicate a small estimation error compared to a traditional square-root unscented Kalman filter and exhibit the enhancement of physically meaningful models.}},
  author       = {{Götte, Ricarda-Samantha and Timmermann, Julia}},
  booktitle    = {{12th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2022)}},
  keywords     = {{joint estimation, unscented transform, Kalman filter, sparsity, data-driven, compressed sensing}},
  location     = {{Canberra, Australien}},
  number       = {{1}},
  pages        = {{85--90}},
  title        = {{{Estimating States and Model Uncertainties Jointly by a Sparsity Promoting UKF}}},
  doi          = {{https://doi.org/10.1016/j.ifacol.2023.02.015}},
  volume       = {{56}},
  year         = {{2023}},
}

@inproceedings{44326,
  abstract     = {{Low-quality models that miss relevant dynamics lead to major challenges in modelbased
state estimation. We address this issue by simultaneously estimating the system’s states
and its model inaccuracies by a square root unscented Kalman filter (SRUKF). Concretely,
we augment the state with the parameter vector of a linear combination containing suitable
functions that approximate the lacking dynamics. Presuming that only a few dynamical terms
are relevant, the parameter vector is claimed to be sparse. In Bayesian setting, properties like
sparsity are expressed by a prior distribution. One common choice for sparsity is a Laplace
distribution. However, due to disadvantages of a Laplacian prior in regards to the SRUKF,
the regularized horseshoe distribution, a Gaussian that approximately features sparsity, is
applied instead. Results exhibit small estimation errors with model improvements detected by
an automated model reduction technique.}},
  author       = {{Götte, Ricarda-Samantha and Timmermann, Julia}},
  booktitle    = {{IFAC-PapersOnLine}},
  keywords     = {{joint estimation, unscented Kalman filter, sparsity, Laplacian prior, regularized horseshoe, principal component analysis}},
  location     = {{Yokohama, Japan}},
  number       = {{2}},
  pages        = {{869--874}},
  title        = {{{Approximating a Laplacian Prior for Joint State and Model Estimation within an UKF}}},
  volume       = {{56}},
  year         = {{2023}},
}

@inproceedings{48482,
  author       = {{Götte, Ricarda-Samantha and Klusmann, Jo Noel and Timmermann, Julia}},
  booktitle    = {{Proceedings - 33. Workshop Computational Intelligence: Berlin, 23.-24. November 2023}},
  location     = {{Berlin, Germany}},
  pages        = {{113--123}},
  title        = {{{Data-driven identification of disturbances using a sliding mode observer}}},
  doi          = {{10.5445/KSP/1000162754}},
  year         = {{2023}},
}

@inproceedings{48476,
  author       = {{Hesse, Michael and Timmermann, Julia and Trächtler, Ansgar}},
  booktitle    = {{2023 European Control Conference (ECC)}},
  publisher    = {{IEEE}},
  title        = {{{Hybrid Optimal Control for Dynamical Systems using Gaussian Process Regression and Unscented Transform<sup>*</sup>}}},
  doi          = {{10.23919/ecc57647.2023.10178368}},
  year         = {{2023}},
}

@article{50070,
  author       = {{Junker, Annika and Pape, Keno Egon Friedrich and Timmermann, Julia and Trächtler, Ansgar}},
  issn         = {{2405-8963}},
  journal      = {{IFAC-PapersOnLine}},
  keywords     = {{General Medicine}},
  number       = {{3}},
  pages        = {{625--630}},
  publisher    = {{Elsevier BV}},
  title        = {{{Adaptive Koopman-Based Models for Holistic Controller and Observer Design}}},
  doi          = {{10.1016/j.ifacol.2023.12.094}},
  volume       = {{56}},
  year         = {{2023}},
}

@inproceedings{42238,
  author       = {{Junker, Annika and Fittkau, Niklas and Timmermann, Julia and Trächtler, Ansgar}},
  booktitle    = {{2022 Sixth IEEE International Conference on Robotic Computing (IRC)}},
  location     = {{Naples, Italy}},
  publisher    = {{IEEE}},
  title        = {{{Autonomous Golf Putting with Data-Driven and Physics-Based Methods}}},
  doi          = {{10.1109/irc55401.2022.00031}},
  year         = {{2023}},
}

@inproceedings{34001,
  author       = {{Arian, Bahman and Homberg, Werner and Kersting, Lukas and Trächtler, Ansgar and Rozo Vasquez, Julian}},
  booktitle    = {{36. Aachener Stahlkolloquium – Umformtechnik “Ideen Form geben“}},
  isbn         = {{978-3-95886-460-3}},
  pages        = {{333--347}},
  title        = {{{Produktkennzeichnung durch lokal definierte Einstellung von ferromagnetischen Eigenschaften beim Drückwalzen von metastabilen Stahlwerkstoffen}}},
  year         = {{2022}},
}

@inproceedings{34003,
  author       = {{Arian, Bahman and Oesterwinter, Annika and Homberg, Werner and Rozo Vasquez, Julian and Walther, Frank and Kersting, Lukas and Trächtler, Ansgar}},
  booktitle    = {{19th Int. Conference on Metal Forming 2022}},
  title        = {{{A flow forming process model to predict workpiece properties in AISI 304L}}},
  year         = {{2022}},
}

@inproceedings{33981,
  author       = {{Ehlert, Meik and Henke, Christian and Trächtler, Ansgar}},
  booktitle    = {{Proceedings of the 12th International Conference on Simulation and Modeling Methodologies, Technologies and Applications}},
  publisher    = {{SCITEPRESS - Science and Technology Publications}},
  title        = {{{Analysis of Differential Algebraic Equation Systems for Connecting Energy Storages of Generally Valid Functional Mock-up Units}}},
  doi          = {{10.5220/0011305700003274}},
  year         = {{2022}},
}

@phdthesis{42071,
  author       = {{Mertin, Sven}},
  title        = {{{Konzept für ein hierarchisches autonomes Verkehrsmanagement}}},
  year         = {{2022}},
}

@inproceedings{30263,
  abstract     = {{High-strength wire materials are usually available as strip material which is further processed in a forming process (e.g. punch-bending). For storage and transport of the semi-finished wire to the customer, the material is wound onto coils. The manufacturing and coiling process introduces plastic deformations into the wire, which lead to undesirable residual stresses and wire curvature of the semi-finished product. These residual stresses and curvatures cause variations in the material properties of the semi-finished product, which have a negative impact on the subsequent product quality. Straightening machines are used to compensate the residual stresses and the curvature in the wire. At the beginning of the straightening process, the straightening machines must be set up in such a way that residual stresses and curvatures are optimally compensated. This setup process is usually a manual and iterative process, where a lot of material is wasted until the optimal settings for the straightening machine are found.In order to reduce the amount of material waste, the operator must be supported in the setup process. In this context, a new and innovative setup assistance system was developed to support the operator during the setup process. The setup assistant system automatically detects the wire curvature by means of an optical measuring system. Based on the optically detected measuring points, the wire curvature is determined by a robust calculation algorithm. Based on a database built up through the carried out experimental and numerical research work, the optimum setting parameters for the straightening machine are suggested to the operator without lengthy trial and error. After confirmation by the operator, the roller settings are automatically adjusted by the mechatronic straightening machine. With the presented method, the conventional iterative setup procedure can be made more resource-efficient and a high straightening quality can be reproducibly achieved. }},
  author       = {{Bathelt, Lukas and Bader, Fabian and Djakow, Eugen and Henke, Christian and Trächtler, Ansgar and Homberg, Werner}},
  location     = {{Braga / Portugal}},
  title        = {{{Innovative assistance system for setting up a mechatronic straightening machine}}},
  doi          = {{https://doi.org/10.4028/p-vs07w9}},
  year         = {{2022}},
}

@inproceedings{30265,
  abstract     = {{Due to increasing globalization and rising quality requirements, the steel and metal processing industry is facing growing cost and innovation pressure. Not least because of their high lightweight potential, high-strength steel materials are meeting the growing material requirements of steel and metal processing in areas such as aerospace and medical technology. In particular, the tight tolerance limits of applicable shape and dimensional accuracies pose a challenge in the processing of high-strength steel strip materials. Improving the processability of high-strength steel materials through the use of straighteners with set-up assistance systems significantly increases the potential for competing with other materials such as aluminum or magnesium alloys. }},
  author       = {{Bader, Fabian and Bathelt, Lukas and Djakow, Eugen and Henke, Christian and Homberg, Werner and Trächtler, Ansgar}},
  location     = {{Braga / Portugal}},
  title        = {{{An approach for an innovative 3d steel strip straightening machine for curvature and saber compensation}}},
  doi          = {{https://doi.org/10.4028/p-87wvu0}},
  year         = {{2022}},
}

@article{33982,
  author       = {{Koppert, Steven and Henke, Christian and Trächtler, Ansgar and Möhringer, Stefan}},
  issn         = {{2405-8963}},
  journal      = {{IFAC-PapersOnLine}},
  keywords     = {{Control and Systems Engineering}},
  number       = {{2}},
  pages        = {{554--560}},
  publisher    = {{Elsevier BV}},
  title        = {{{Tool Wear Monitoring of a Tree Log Bandsaw using a Deep Convolutional Neural Network on challenging data}}},
  doi          = {{10.1016/j.ifacol.2022.04.252}},
  volume       = {{55}},
  year         = {{2022}},
}

@inproceedings{33978,
  author       = {{Bathelt, Lukas and Bader, Fabian and Djakow, Eugen and Henke, Christian and Trächtler, Ansgar and Homberg, Werner}},
  booktitle    = {{Fachtagung VDI MECHATRONIK 2022 }},
  location     = {{Darmstadt}},
  pages        = {{19--24}},
  title        = {{{Mechatronische Richtapparate: Intelligente Richttechnik von hochfesten Flachdrähten}}},
  year         = {{2022}},
}

@inproceedings{33469,
  author       = {{Schütz, Stefan and Schmidt, Robin and Henke, Christian and Trächtler, Ansgar}},
  booktitle    = {{2022 IEEE International Systems Conference (SysCon)}},
  location     = {{Montreal, QC, Canada}},
  pages        = {{1--8}},
  publisher    = {{IEEE}},
  title        = {{{Virtual Commissioning of the Trajectory Tracking Control of a Sensor-Guided, Kinematically Redundant Robotic Welding System on a PLC}}},
  doi          = {{10.1109/syscon53536.2022.9773878}},
  year         = {{2022}},
}

@inproceedings{33976,
  author       = {{Lenz, Cederic  and Hanke, Fabian and Henke, Christian and Trächtler, Ansgar}},
  booktitle    = {{2022 27th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA )}},
  location     = {{Stuttgart, Germany }},
  publisher    = {{IEEE}},
  title        = {{{Anomaly Detection in Hot Forming Processes using Hybrid Modeling - Part II}}},
  doi          = {{10.1109/ETFA52439.2022.9921510}},
  year         = {{2022}},
}

@article{34000,
  abstract     = {{<jats:title>Abstract</jats:title>
               <jats:p>This paper presents the characterization of the microstructure evolution during flow forming of austenitic stainless steel AISI 304L. Due to plastic deformation of metastable austenitic steel, phase transformation from γ-austenite into α’-martensite occurs. This is initiated by the formation of shear bands as product of the external stresses. By means of coupled microscopic and micromagnetic investigations, a characterization of the microstructure was carried out. In particular, this study shows the distribution of the strain-induced α’-martensite and its influence on material properties like hardness at different depths. The microstructural analyses by means of electron backscattered diffraction (EBSD) technique, evidence a higher amount of α’-martensite (ca. 23 %) close to the outer specimen surface, where the plastic deformation and the direct contact with the forming tool take place. In the middle area (ca. 1.5 mm depth from the outer surface), the portion of transformed α’-martensite drops to 7 % and in the inner surface to 2 %. These results are well correlated with microhardness and micromagnetic measurements at different depths. EBSD and atomic force microscopy (AFM) were used to make a detailed characterization of the topography and degree of deformation of the shear bands. Likewise, the mechanisms of nucleation of α’-martensite were discussed. This research contributes to the development of micromagnetic sensors to monitor the evolution of properties during flow forming. This makes them more suitable for closed-loop property control, which offers possibilities for an application-oriented and more efficient production.</jats:p>}},
  author       = {{Rozo Vasquez, Julian and Kanagarajah, Hanigah and Arian, Bahman and Kersting, Lukas and Homberg, Werner and Trächtler, Ansgar and Walther, Frank}},
  issn         = {{2195-8599}},
  journal      = {{Practical Metallography}},
  keywords     = {{Metals and Alloys, Mechanics of Materials, Condensed Matter Physics, Electronic, Optical and Magnetic Materials}},
  number       = {{11}},
  pages        = {{660--675}},
  publisher    = {{Walter de Gruyter GmbH}},
  title        = {{{Coupled microscopic and micromagnetic depth-specific analysis of plastic deformation and phase transformation of metastable austenitic steel AISI 304L by flow forming}}},
  doi          = {{10.1515/pm-2022-0064}},
  volume       = {{59}},
  year         = {{2022}},
}

@article{33999,
  abstract     = {{<jats:p>The production of complex multi-functional, high-strength parts is becoming increasingly important in the industry. Especially with small batch size, the incremental flow forming processes can be advantageous. The production of parts with complex geometry and locally graded material properties currently depicts a great challenge in the flow forming process. At this point, the usage of closed-loop control for the shape and properties could be a feasible new solution. The overall aim in this project is to establish an intelligent closed-loop control system for the wall thickness as well as the α’-martensite content of AISI 304L-workpieces in a flow forming process. To reach this goal, a novel sensor concept for online measurements of the wall thickness reduction and the martensite content during forming process is proposed. It includes the setup of a modified flow forming machine and the integration of the sensor system in the machine control. Additionally, a simulation model for the flow forming process is presented which describes the forming process with regard to the plastic workpiece deformation, the induced α’-martensite fraction, and the sensor behavior. This model was used for designing a closed-loop process control of the wall thickness reduction that was subsequently realized at the real plant including online measured feedback from the sensor system.</jats:p>}},
  author       = {{Kersting, Lukas and Arian, Bahman and Vasquez, Julian Rozo and Trächtler, Ansgar and Homberg, Werner and Walther, Frank}},
  issn         = {{1662-9795}},
  journal      = {{Key Engineering Materials}},
  keywords     = {{Mechanical Engineering, Mechanics of Materials, General Materials Science}},
  pages        = {{862--874}},
  publisher    = {{Trans Tech Publications, Ltd.}},
  title        = {{{Innovative Online Measurement and Modelling Approach for Property-Controlled Flow Forming Processes}}},
  doi          = {{10.4028/p-yp2hj3}},
  volume       = {{926}},
  year         = {{2022}},
}