@phdthesis{57157, author = {{Ngoti, Irene Fredolin}}, title = {{{Sustainable rural electrification through community-managed mini-grids: Evidence from Tanzania }}}, year = {{2024}}, } @inbook{57165, author = {{Bauer, Anna Brigitte}}, booktitle = {{Wissenschaftsdidaktik als kritische Kommunikationsanalyse - Ein Sammelband zur Weiterführung eines Gedankens von Ludwig Huber}}, editor = {{Scharlau, Ingrid and Jenert, Tobias}}, isbn = {{978-3-8474-3070-4}}, pages = {{59--74}}, publisher = {{Barbara Budrich}}, title = {{{Methodische Einführung des Konzeptes Messunsicheheiten in der Physik - Sprachliche Analyse von Standardwerken}}}, year = {{2024}}, } @inproceedings{56180, author = {{Arslan, Kader and Trier, Matthias}}, booktitle = {{Proceedings of the 19th International Conference on Wirtschaftsinformatik (WI 2024)}}, location = {{Würzburg, Germany}}, title = {{{Do Organizations Utilize Social Media Affordances? A Qualitative Investigation of Social Media Management Activities}}}, year = {{2024}}, } @techreport{57161, author = {{Werning, Alexander and Haeb-Umbach, Reinhold}}, title = {{{UPB-NT submission to DCASE24: Dataset pruning for targeted knowledge distillation}}}, 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}}, } @inproceedings{57185, author = {{Reiling, Fabian and Henke, Christian and Hunstig, Matthias and Gröger, Stefan and Trächtler, Ansgar}}, booktitle = {{2024 IEEE International Conference on Advanced Intelligent Mechatronics (AIM)}}, publisher = {{IEEE}}, title = {{{Batch constrained multi-objective Bayesian optimization using the example of ultrasonic wire bonding}}}, doi = {{10.1109/aim55361.2024.10637123}}, year = {{2024}}, } @inproceedings{57188, author = {{Stieren, Stephan and Werner, Achim and Henke, Christian and Trächtler, Ansgar}}, booktitle = {{2024 IEEE Conference on Technologies for Sustainability (SusTech)}}, publisher = {{IEEE}}, title = {{{A comprehensive test infrastructure for the evaluation of energy management systems of the household and grid level}}}, doi = {{10.1109/sustech60925.2024.10553455}}, year = {{2024}}, } @inproceedings{57187, author = {{Stieren, Stephan and Lenger, Luca and Kliem, Moritz and Henke, Christian and Trächtler, Ansgar}}, booktitle = {{2024 IEEE International Systems Conference (SysCon)}}, publisher = {{IEEE}}, title = {{{Development of digital business models for holistic energy management on device, home and grid level}}}, doi = {{10.1109/syscon61195.2024.10553440}}, year = {{2024}}, } @inproceedings{57184, author = {{Poy, Yi Han and Zarnack, Martin and Henkenjohann, Mark and Nolte, Udo}}, booktitle = {{AIAA SCITECH 2024 Forum}}, publisher = {{American Institute of Aeronautics and Astronautics}}, title = {{{Aerodynamic Derivatives Identification of a Fixed-Wing UAV using Flight Data}}}, doi = {{10.2514/6.2024-0248}}, year = {{2024}}, } @inproceedings{57186, author = {{Schmidt, Robin and Schütz, Stefan and Prinz, Sebastian and Henke, Christian and Trächtler, Ansgar}}, booktitle = {{Proceedings of the 4th IFSA Winter Conference on Automation, Robotics and Communications for Industry 4.0/5.0 (ARCI 2024)}}, title = {{{Optimizing Welding Efficiency: A First Approach for an Automated Mobile Welding Robot}}}, year = {{2024}}, } @article{57176, abstract = {{Incremental nonlinear dynamic inversion (INDI) is a widely used approach to controlling UAVs with highly nonlinear dynamics. One key element of INDI-based controllers is the control allocation realizing pseudo controls using available actuators. However, the tracking of commanded pseudo controls is not the only objective considered during control allocation. Since the approach only works locally due to linearization and the solution is often ambiguous, additional aspects like control efforts or penalizing the deviation of certain states must be considered. Conducting the control allocation by solving a quadratic program this results in a considerable number of weighting parameters, which must be tuned during control design. Currently, this is conducted manually and is therefore time consuming. An automated approach for tuning these parameters is therefore highly beneficial. Thus, this paper presents and evaluates a model-based approach automatically tuning the control allocation parameters of a tiltrotor VTOL using an optimization algorithm. This optimization algorithm searches for optimal parameters minimizing a cost functional that reflects the design target. This cost functional is calculated based on a test mission for the VTOL which is conducted within a simulation environment. The test mission represents the common operating range of the VTOL. The simulation environment consists of an aircraft model as well as a model of the INDI-based controller which is dependent on the control allocation parameters. On this basis, model-based optimization is conducted and the optimal parameters are identified. Finally, successful real-world tests on a 4-degrees-of-freedom testbench using the identified parameters are presented. Since the control allocation parameters can significantly influence the aircraft’s stability, the 4-DOF testbench for the aircraft is required for rapid validation of the parameters at a minimum amount of risk.}}, author = {{Henkenjohann, Mark and Nolte, Udo and Sion, Fabian and Henke, Christian and Trächtler, Ansgar}}, issn = {{2076-0825}}, journal = {{Actuators}}, number = {{5}}, publisher = {{MDPI AG}}, title = {{{Parameter Tuning Approach for Incremental Nonlinear Dynamic Inversion-Based Flight Controllers}}}, doi = {{10.3390/act13050187}}, volume = {{13}}, year = {{2024}}, } @inproceedings{57173, abstract = {{Manufacturing processes benefit from property control enabling reproducibility, application oriented outcomes, and efficient part production. In reverse flow forming, state of the art practices focus primarily on geometry control, neglecting property control. Given the intricacies of the process involving the interaction of tool and machine behavior, process parameters, properties of semi finished products and temperatures, incorporating process control becomes an imperative for producing components with predefined properties. The property controlled within this reverse flow forming process is the local α’ martensite content. Therefore, process strategies to actively influence the α’ martensite content must be implemented. In this study seamless AISI 304L steel tubes are used, where α’ martensite formation is strain and/or temperature induced through phase transformation within the process. This paper presents innovative process strategies, methods, and specially developed mechanical and thermal actuator systems to locally increase or suppress the α’ martensite content. The use and implementation of these approaches and tools allows the creation of unique optically invisible microstructure profiles containing 3D gradings, implying a radial grading of α’ martensite. The locally implemented α’ martensite, forming these 3D gradings, offers potential applications for functional or sensory purposes. This paper extends beyond theoretical concepts, providing tangible component outcomes.}}, author = {{Arian, Bahman and Homberg, Werner and Kersting, Lukas and Trächtler, Ansgar and Rozo Vasquez, Julian and Walther, Frank}}, booktitle = {{Materials Research Proceedings}}, issn = {{2474-395X}}, publisher = {{Materials Research Forum LLC}}, title = {{{α’-martensite grading techniques in reverse flow forming of AISI 304L}}}, doi = {{10.21741/9781644903254-76}}, volume = {{44}}, year = {{2024}}, } @inproceedings{57174, abstract = {{Increasing the sustainability and resource efficiency of forming processes is one of today's major goals. High-strength wire materials are usually available as strip material and are subjected to a downstream forming process such as punch-bending to produce parts for the electronics industry, for example. During the manufacturing process of the semi-finished product, residual stresses and plastic deformations are introduced into the wire by rolling and drawing processes. Straightening machines are used in the production lines to compensate for these. To increase the sustainability of these production lines, the straightening process is an essential step. Before the continuous manufacturing process starts, the straightening process must be set up and the optimal roller positions must be found. Once the process is set up, the roller position settings are usually not changed. Due to missing measurement systems for the straightening quality, it is not possible to dynamically adjust the positions of the straightening rollers to variations in the material properties. This leads to deviations in the dimensional accuracy of the components to be produced and thus to an increase in the rejection rate in the manufacturing processes. To reduce the rejection rate, a novel control system for a continuous feedback control of a straightening process is presented in this paper. This leads to a reduction of the rejection rate and unnecessary preforming operations in wire straightening process. The result is an increasing sustainability and efficiency of these production process.}}, author = {{Bathelt, Lukas and Djakow, Eugen and Henke, Christian and Trächtler, Ansgar}}, booktitle = {{Materials Research Proceedings}}, issn = {{2474-395X}}, publisher = {{Materials Research Forum LLC}}, title = {{{Innovative control system for straightening machines using sensor information from downstream processes}}}, doi = {{10.21741/9781644903131-308}}, volume = {{41}}, year = {{2024}}, } @inproceedings{57178, abstract = {{The incremental flow forming process is currently enhanced in research context by special closed-loop property control concepts to increase the productivity and to control the product properties making invisible property structures like a magnetic barcode possible. However, it is preferred to establish property control concepts on single roller machines instead of conventional machines with three roller actuation due to the better machine accessibility. For those single roller machines, rather poor surface qualities of flow formed workpieces were observed in the past especially for hydraulic actuators. Thus, a new actuator closed-loop position control concept is developed in this paper using model-based control design methods and taking the flow forming forces as a load into account. The novel closed-loop control is validated during workpiece production at the actual single roller flow forming machine. An analysis of the manufactured workpieces show that the surface quality is significantly enhanced by the new control to a roughness level almost similar to conventional three roller flow forming. Thus, a sincere added value to the flow forming process is offered by the novel actuator closed-loop position control.}}, author = {{Kersting, Lukas and Sander, Sebastian and Arian, Bahman and Rozo Vasquez, Julian and Trächtler, Ansgar and Homberg, Werner and Walther, Frank}}, booktitle = {{Materials Research Proceedings}}, issn = {{2474-395X}}, publisher = {{Materials Research Forum LLC}}, title = {{{Improving the flow forming process by a novel closed-loop control}}}, doi = {{10.21741/9781644903131-158}}, volume = {{41}}, year = {{2024}}, } @inproceedings{57183, abstract = {{In multi-stage bending and straightening operations cross-stage and quantity-dependent effects crucially affect the quality of the end product. Using punch-bending units in combination with a mechatronic straightening device can improve the accuracy and repeatability of product features remarkably well. In this work a concept for an innovative hybrid model of a roll straightener in a multi-stage straightening and multi-stage bending process is proposed. This model combines data-driven elements with expert knowledge and aims to minimise residual errors of the roll straightener to reliably decrease the risk of disadvantageous cross-stage and quantity-dependent effects on a subsequent punch-bending process.}}, author = {{Peters, Henning and Djakow, Eugen and Rostek, Tim and Mazur, Andreas and Trächtler, Ansgar and Homberg, Werner and Hammer, Barbara}}, booktitle = {{Materials Research Proceedings}}, issn = {{2474-395X}}, publisher = {{Materials Research Forum LLC}}, title = {{{Novel approach for data-driven modelling of multi-stage straightening and bending processes}}}, doi = {{10.21741/9781644903131-252}}, volume = {{41}}, year = {{2024}}, } @inproceedings{57171, abstract = {{In manufacturing, property control ensures efficient part production. However, in reverse flow forming, current practices focus on geometry control rather than property control. To address the complexity of the process and tool machine interaction, process control is crucial for defined component properties. This study focuses on controlling local α’ martensite content in reverse flow forming of seamless AISI 304L steel tubes. Strategies and systems are presented to influence α’ martensite content, creating unique microstructure profiles for 1D and 2D Gradings, with tangible component outcomes.}}, author = {{Arian, Bahman and Homberg, Werner and Kersting, Lukas and Trächtler, Ansgar and Rozo Vasquez, Julian and Walther, Frank}}, booktitle = {{Materials Research Proceedings}}, issn = {{2474-395X}}, publisher = {{Materials Research Forum LLC}}, title = {{{Thermomechanical reverse flow forming of AISI 304L}}}, doi = {{10.21741/9781644903131-151}}, volume = {{41}}, year = {{2024}}, } @inproceedings{57177, author = {{Jahneke, Julien and Nolte, Udo and Henkenjohann, Mark and Seidenberg, Tobias and Henke, Christian and Trächtler, Ansgar}}, booktitle = {{2024 IEEE Aerospace Conference}}, publisher = {{IEEE}}, title = {{{Development and Implementation of a Modular Interface for a DroneCAN Communication Bus}}}, doi = {{10.1109/aero58975.2024.10521247}}, year = {{2024}}, } @inproceedings{57180, author = {{Lenz, Cederic and Bause, Maximilian and Henke, Christian and Trächtler, Ansgar}}, booktitle = {{2024 International Conference on Advanced Robotics and Mechatronics (ICARM)}}, publisher = {{IEEE}}, title = {{{Boosting Low Data PINN Robustness with Transfer Learning*}}}, doi = {{10.1109/icarm62033.2024.10715896}}, year = {{2024}}, }