@article{57467,
abstract = {{Additive manufacturing of metallic components often results in the formation of columnar grain structures aligned along the build direction. These elongated grains can introduce anisotropy, negatively impacting the mechanical properties of the components. This study aimed to achieve controlled solidification with a fine-grained microstructure to enhance the mechanical performance of printed parts. Stainless steel 316L was used as the test material. High-intensity ultrasound was applied during the direct energy deposition (DED) process to inhibit the formation of columnar grains. The investigation emphasized the importance of amplitude changes of the ultrasound wave as the system’s geometry continuously evolves with the addition of multiple layers and assessed how these changes influence the grain size and distribution. Initial tests revealed significant amplitude fluctuations during layer deposition, highlighting the impact of layer deposition on process uniformity. The mechanical results demonstrated that the application of ultrasound effectively refined the grain structure, leading to a 15% increase in tensile strength compared to conventionally additively manufactured samples.}},
author = {{Lehnert, Dennis and Bödger, Christian and Pabel, Philipp and Scheidemann, Claus and Hemsel, Tobias and Gnaase, Stefan and Kostka, David and Tröster, Thomas}},
issn = {{2073-4352}},
journal = {{Crystals}},
number = {{11}},
publisher = {{MDPI AG}},
title = {{{The Influence of Ultrasonic Irradiation of a 316L Weld Pool Produced by DED on the Mechanical Properties of the Produced Component}}},
doi = {{10.3390/cryst14111001}},
volume = {{14}},
year = {{2024}},
}
@inproceedings{52211,
author = {{Beule, Felix and Teutenberg, Dominik and Meschut, Gerson}},
booktitle = {{DECHEMA-Workshop für Klebstoffanwender: Simulation von Klebverbindungen}},
location = {{Köln}},
title = {{{Klebstoffmodell - Parameteridentifikation, Verifikation und Validierung für den Lastfall Crash}}},
year = {{2024}},
}
@inproceedings{52214,
author = {{Beule, Felix and Teutenberg, Dominik and Meschut, Gerson and Schmelzle, Lars and Possart, Gunnar and Mergheim, Julia and Steinmann, Paul}},
location = {{Köln}},
title = {{{Methodenentwicklung zur Simulation von hyperelastischen Klebverbindungen unter Crashbelastung}}},
year = {{2024}},
}
@book{58800,
author = {{Yang, Keke and Seitz, Georg and Schreiber, Vincent and Meschut, Gerson and Biegler, Max and Jüttner, Sven and Rethmeier, Michael }},
isbn = {{978-3-96780-190-3}},
title = {{{Validierung von Methoden zur Vermeidung von Liquid Metal Embrittlement an realitätsnahen Prinzipbauteilen}}},
year = {{2024}},
}
@inproceedings{62052,
author = {{Reckmann, Eileen and Blomberg, Tobias and Temmen, Katrin}},
location = {{München}},
title = {{{„Das ist genau das richtige Setting“ ‒ Zusammen lehren und lernen im MINT-Cluster MINT4.OWL}}},
year = {{2024}},
}
@inproceedings{62078,
abstract = {{Fiber reinforced plastics (FRP) exhibit strongly non-linear deformation behavior. To capture this in simulations, intricate models with a variety of parameters are typically used. The identification of values for such parameters is highly challenging and requires in depth understanding of the model itself. Machine learning (ML) is a promising approach for alleviating this challenge by directly predicting parameters based on experimental results. So far, this works mostly for purely artificial data. In this work, two approaches to generalize to experimental data are investigated: a sequential approach, leveraging understanding of the constitutive model and a direct, purely data driven approach. This is exemplary carried out for a highly non-linear strain rate dependent constitutive model for the shear behavior of FRP.The sequential model is found to work better on both artificial and experimental data. It is capable of extracting well suited parameters from the artificial data under realistic conditions. For the experimental data, the model performance depends on the composition of the experimental curves, varying between excellently suiting and reasonable predictions. Taking the expert knowledge into account for ML-model training led to far better results than the purely data driven approach. Robustifying the model predictions on experimental data promises further improvement. }},
author = {{Gerritzen, Johannes and Hornig, Andreas and Winkler, Peter and Gude, Maik}},
booktitle = {{ECCM21 - Proceedings of the 21st European Conference on Composite Materials}},
isbn = {{978-2-912985-01-9}},
keywords = {{Direct parameter identification, Machine learning, Convolutional neural networks, Strain rate dependency, Fiber reinforced plastics, woven composites, segmentation, synthetic training data, x-ray computed tomography}},
pages = {{1252–1259}},
publisher = {{European Society for Composite Materials (ESCM)}},
title = {{{Direct parameter identification for highly nonlinear strain rate dependent constitutive models using machine learning}}},
doi = {{10.60691/yj56-np80}},
volume = {{3}},
year = {{2024}},
}
@article{51116,
abstract = {{Self-piercing riveting is an established joining technique for lightweight materials. To increase the sustainability of the rivet manufacturing process, the authors of the present paper have developed an approach for shortening the process chain by omitting the heat treatment and rivet coating. To do this, use is made of high nitrogen steel as the rivet material. Successful joining with these rivets has already been proven, and it has also been shown that a competitive joint strength can be achieved with these rivets. Up until now, no studies have been conducted of the corrosion behaviour of uncoated rivets in high nitrogen steel compared to conventional rivets made of heat-treatable steel with a coating of Almac® or zinc-nickel with topcoat, and the corrosion behaviour of joints manufactured with these rivets has also not been investigated. Furthermore, the suitability of rivets in high nitrogen steel for structures undergoing cathodic dip painting has not been evaluated to date. These are therefore the aims of the research work presented in this paper. Corrosion behaviour is tested by exposing rivets and joints to a salt spray atmosphere. Cross-cut tests are conducted in order to classify the adhesion of cathodic dip paint to the different rivet surfaces and materials. The results of the experimental test show that the cathodic dip paint has sufficient adhesion to the uncoated rivets in high nitrogen steel and that these rivets can therefore be used in the manufacture of car bodies. Due to the stainless properties of the high nitrogen steel, better corrosion resistance is seen by comparison to the commonly used coatings of Almac® and zinc-nickel with topcoat. A study of the corrosion behaviour of the joints shows that the rivet head diameter and rivet head position, in particular, are decisive for preventing crevice corrosion under the rivet head and contact corrosion within the joint. }},
author = {{Uhe, Benedikt and Kuball, Clara-Maria and Merklein, Marion and Meschut, Gerson}},
journal = {{Production Engineering}},
title = {{{Corrosion behaviour of self-piercing riveted joints with uncoated rivets in high nitrogen steel}}},
doi = {{10.1007/s11740-024-01262-6}},
year = {{2024}},
}
@inproceedings{55638,
abstract = {{Abstract. Traditionally, joints are cylindrical and rotationally symmetric. In the present study, non-rotationally symmetric joints are used for joining steel and Glass mat-reinforced thermoplastic sheets (GMT). In addition, the study also analyzes the impact of non-rotational symmetric joint rotation on the load-bearing capacity. Single lap joint specimens were fabricated using the In-Mold assembly technique for joining steel sheets with GMT. Tensile shear tests were performed on different orientations of the joint geometry, and it was observed that changing the joint orientation influences the load-bearing capacity. The joints are constitutively modeled using beam elements and the influence of joint rotation on load distribution is examined through a static simulation study. }},
author = {{Devulapally, Deekshith Reddy and Martin, Sven and Tröster, Thomas}},
booktitle = {{Materials Research Proceedings}},
issn = {{2474-395X}},
publisher = {{Materials Research Forum LLC}},
title = {{{Non-rotationally symmetric joints – Mechanisms and load bearing capacity}}},
doi = {{10.21741/9781644903131-183}},
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}},
}