@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 = {{Abstract 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.}}, 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 = {{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.}}, 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}}, } @book{47417, author = {{Föllinger, Otto and Konigorski, Ulrich and Lohmann, Boris and Roppenecker, Günter and Trächtler, Ansgar}}, publisher = {{VDE-Verlag}}, title = {{{Regelungstechnik. Einführung in die Methoden und ihre Anwendung}}}, year = {{2022}}, } @inproceedings{29803, abstract = {{Ultrasonic wire bonding is a solid-state joining process used to form electrical interconnections in micro and power electronics and batteries. A high frequency oscillation causes a metallurgical bond deformation in the contact area. Due to the numerous physical influencing factors, it is very difficult to accurately capture this process in a model. Therefore, our goal is to determine a suitable feed-forward control strategy for the bonding process even without detailed model knowledge. We propose the use of batch constrained Bayesian optimization for the control design. Hence, Bayesian optimization is precisely adapted to the application of bonding: the constraint is used to check one quality feature of the process and the use of batches leads to more efficient experiments. Our approach is suitable to determine a feed-forward control for the bonding process that provides very high quality bonds without using a physical model. We also show that the quality of the Bayesian optimization based control outperforms random search as well as manual search by a user. Using a simple prior knowledge model derived from data further improves the quality of the connection. The Bayesian optimization approach offers the possibility to perform a sensitivity analysis of the control parameters, which allows to evaluate the influence of each control parameter on the bond quality. In summary, Bayesian optimization applied to the bonding process provides an excellent opportunity to develop a feedforward control without full modeling of the underlying physical processes.}}, author = {{Hesse, Michael and Hunstig, Matthias and Timmermann, Julia and Trächtler, Ansgar}}, booktitle = {{Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods (ICPRAM)}}, isbn = {{978-989-758-549-4}}, keywords = {{Bayesian optimization, Wire bonding, Feed-forward control, model-free design}}, location = {{Online}}, pages = {{383--394}}, title = {{{Batch Constrained Bayesian Optimization for UltrasonicWire Bonding Feed-forward Control Design}}}, year = {{2022}}, } @inbook{33849, abstract = {{Modern traffic control systems are key to cope with current and future traffic challenges. In this paper information obtained from a microscopic traffic estimation using various data sources is used to feed a new developed traffic control approach. The presented method can control a traffic area with multiple traffic light systems (TLS) reacting to individual road users and pedestrians. In contrast to widespread green time extension techniques, this control selects the best phase sequence by analyzing the current traffic state reconstructed in SUMO and its predicted progress. To achieve this, the key aspect of the control strategy is to use Model Predictive Control (MPC). In order to maintain realism for real world applications, among other things, the traffic phase transitions are modelled in detail and integrated within the prediction. For the efficiency, the approach incorporates a fuzzy logic preselection of all phases reducing the computational effort. The evaluation itself is able to be easily adjusted to focus on various objectives like low occupancies, reducing waiting times and emissions, few number of phase transitions etc. determining the best switching times for the selected phases. Exemplary traffic simulations demonstrate the functionality of the MPC-based control and, in addition, some aspects under development like the real-world communication network are also discussed.}}, author = {{Malena, Kevin and Link, Christopher and Bußemas, Leon and Gausemeier, Sandra and Trächtler, Ansgar}}, booktitle = {{Communications in Computer and Information Science}}, editor = {{Klein, Cornel and Jarke, Mathias and Helfert, Markus and Berns, Karsten and Gusikhin, Oleg}}, isbn = {{9783031170973}}, issn = {{1865-0929}}, keywords = {{Traffic control, Traffic estimation, Real-time, MPC, Fuzzy, Isolated intersection, Networked intersection, Sensor fusion}}, pages = {{232–254}}, publisher = {{Springer International Publishing}}, title = {{{Traffic Estimation and MPC-Based Traffic Light System Control in Realistic Real-Time Traffic Environments}}}, doi = {{10.1007/978-3-031-17098-0_12}}, volume = {{1612}}, year = {{2022}}, } @inproceedings{26389, abstract = {{Within this work, we investigate how data-driven numerical approximation methods of the Koopman operator can be used in practical control engineering applications. We refer to the method Extended Dynamic Mode Decomposition (EDMD), which approximates a nonlinear dynamical system as a linear model. This makes the method ideal for control engineering applications, because a linear system description is often assumed for this purpose. Using academic examples, we simulatively analyze the prediction performance of the learned EDMD models and show how relevant system properties like stability, controllability, and observability are reflected by the EDMD model, which is a critical requirement for a successful control design process. Subsequently, we present our experimental results on a mechatronic test bench and evaluate the applicability to the control engineering design process. As a result, the investigated methods are suitable as a low-effort alternative for the design steps of model building and adaptation in the classical model-based controller design method.}}, author = {{Junker, Annika and Timmermann, Julia and Trächtler, Ansgar}}, booktitle = {{2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC 2022)}}, isbn = {{978-1-6654-5946-4}}, keywords = {{Koopman Operator, Nonlinear Control, Extended Dynamic Mode Decomposition, Hybrid Modelling}}, location = {{Cairo, Egypt}}, pages = {{1--9}}, title = {{{Data-Driven Models for Control Engineering Applications Using the Koopman Operator}}}, doi = {{10.1109/AIRC56195.2022.9836980}}, year = {{2022}}, } @inproceedings{34011, author = {{Junker, Annika and Fittkau, Niklas and Timmermann, Julia and Trächtler, Ansgar}}, booktitle = {{Proceedings - 32. Workshop Computational Intelligence: Berlin, 1. - 2. Dezember 2022}}, location = {{Berlin, Germany}}, pages = {{119--124}}, title = {{{Autonomes Putten mittels datengetriebener und physikbasierter Methoden}}}, doi = {{10.5445/KSP/1000151141}}, year = {{2022}}, } @article{50071, author = {{Junker, Annika and Timmermann, Julia and Trächtler, Ansgar}}, issn = {{2405-8963}}, journal = {{IFAC-PapersOnLine}}, keywords = {{Control and Systems Engineering}}, number = {{12}}, pages = {{389--394}}, publisher = {{Elsevier BV}}, title = {{{Learning Data-Driven PCHD Models for Control Engineering Applications*}}}, doi = {{10.1016/j.ifacol.2022.07.343}}, volume = {{55}}, year = {{2022}}, } @inproceedings{26180, author = {{Michael, Jan and Grote, Eva-Maria and Pfeifer, Stefan and Rasor, Rik and Henke, Christian and Trächtler, Ansgar and Kaiser, Lydia}}, title = {{{Towards the Concept of a Digital Green Twin for a Sustainable Product Lifecycle}}}, year = {{2021}}, } @inproceedings{26997, author = {{Ehlert, Meik and Michael, Jan and Henke, Christian and Trächtler, Ansgar and Kalla, Matthias and Bagaber, Bakr and Ponick, Bernd and Mertens, Axel}}, booktitle = {{Proceedings of the International Conference on SMACD and 16th Conference on PRIME}}, pages = {{164--167}}, title = {{{Connecting Energy Storages from Tool Independent, Signal-flow Oriented FMUs}}}, year = {{2021}}, } @article{27123, author = {{Poddubnyi, Vladimir I. and Trächtler, Ansgar and Warkentin, Andreas and Henke, Christian}}, journal = {{Russian Engineering Research}}, number = {{3}}, pages = {{198--201}}, publisher = {{Springer}}, title = {{{Model of a Triangular Caterpillar Drive and Analysis of Vertical Vehicle Dynamics}}}, volume = {{41}}, year = {{2021}}, } @inproceedings{23576, author = {{Biemelt, Patrick and Böhm, Sabrina and Gausemeier, Sandra and Trächtler, Ansgar}}, booktitle = {{Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC)}}, location = {{Melbourne, Australia}}, pages = {{1619 -- 1626}}, title = {{{Subjective Evaluation of Filter- and Optimization-Based Motion Cueing Algorithms for a Hybrid Kinematics Driving Simulator}}}, year = {{2021}}, } @inproceedings{22961, author = {{Schütz, Stefan and Elsner, Nikolaus and Henke, Christian and Trächtler, Ansgar}}, booktitle = {{Fachtagung VDI MECHATRONIK 2021}}, title = {{{Kraftsensitive Kalibriermethode für Industrieroboter}}}, year = {{2021}}, } @article{22962, author = {{Schütz, Stefan and Rüting, Arne Thorsten and Henke, Christian and Trächtler, Ansgar}}, journal = {{at-Automatisierungstechnik}}, number = {{3}}, pages = {{231--241}}, title = {{{Echtzeitfähige Planung optimierter Trajektorien für sensorgeführte, kinematisch redundante Robotersysteme auf einer Industriesteuerung}}}, volume = {{69}}, year = {{2021}}, } @article{23469, abstract = {{The implementation of control systems in metal forming processes improves product quality and productivity. By controlling workpiece properties during the process, beneficial effects caused by forming can be exploited and integrated in the product design. The overall goal of this investigation is to produce tailored tubular parts with a defined locally graded microstructure by means of reverse flow forming. For this purpose, the proposed system aims to control both the desired geometry of the workpiece and additionally the formation of strain-induced α′-martensite content in the metastable austenitic stainless steel AISI 304 L. The paper introduces an overall control scheme, a geometry model for describing the process and changes in the dimensions of the workpiece, as well as a material model for the process-induced formation of martensite, providing equations based on empirical data. Moreover, measurement systems providing a closed feedback loop are presented, including a novel softsensor for in-situ measurements of the martensite content.}}, author = {{Riepold, Markus and Arian, Bahman and Vasquez, Julian Rozo and Homberg, Werner and Walther, Frank and Trächtler, Ansgar}}, issn = {{2666-9129}}, journal = {{Advances in Industrial and Manufacturing Engineering}}, title = {{{Model approaches for closed-loop property control for flow forming}}}, doi = {{10.1016/j.aime.2021.100057}}, year = {{2021}}, } @inproceedings{30297, author = {{Rozo Vasquez, Julian and Arian, Bahman and Riepold, Markus and Walther, Frank and Homberg, Werner and Trächtler, Ansgar}}, booktitle = {{Proceedings of the 11th International Work­shop NDT in Progress}}, location = {{Prague}}, title = {{{Magnetic Barkhausen noise analysis for microstructural effects separation during flow forming of metastable austenite 304L.}}}, year = {{2021}}, } @inproceedings{23465, abstract = {{One of the main objectives of production engineering is to reproducibly manufacture (complex) defect-free parts. To achieve this, it is necessary to employ an appropriate process or tool design. While this will generally prove successful, it cannot, however, offset stochastic defects with local variations in material properties. Closed-loop process control represents a promising approach for a solution in this context. The state of the art involves using this approach to control geometric parameters such as a length. So far, no research or applications have been conducted with closed-loop control for microstructure and product properties. In the project on which this paper is based, the local martensite content of parts is to be adjusted in a highly precise and reproducible manner. The forming process employed is a special, property-controlled flow-forming process. A model-based controller is thus to generate corresponding correction values for the tool-path geometry and tool-path velocity on the basis of online martensite content measurements. For the controller model, it is planned to use a special process or microstructure (correlation) model. The planned paper not only describes the experimental setup but also presents results of initial experimental investigations for subsequent use in the closed-loop control of α’-martensite content during flow-forming.}}, author = {{Arian, Bahman and Homberg, Werner and Riepold, Markus and Trächtler, Ansgar and Rozo Vasquez, Julian and Walther, Frank}}, isbn = {{978-2-87019-302-0}}, keywords = {{Flow-forming, Spinning, Process Strategy, Martensite Content, Property Control, Micromagnetic Measurement, Metastable Austenitic Stainless Steel}}, location = {{Liège, Belgium}}, publisher = {{ULiège Library}}, title = {{{Forming of metastable austenitic stainless steel tubes with axially graded martensite content by flow-forming}}}, year = {{2021}}, }