@article{62053, abstract = {{ABSTRACTThis 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 magnetic Barkhausen noise (MBN) and eddy currents (EC), which are suitable for detection of microstructure evolution due to formation of ferromagnetic phases. The focus of this study was put on the qualification of different micromagnetic techniques and 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}}, issn = {{2577-8196}}, journal = {{Engineering Reports}}, number = {{1}}, publisher = {{Wiley}}, title = {{{Barkhausen Noise‐ and Eddy Current‐Based Measurements for Online Detection of Deformation‐Induced Martensite During Flow Forming of Metastable Austenitic Steel AISI 304L}}}, doi = {{10.1002/eng2.13070}}, volume = {{7}}, year = {{2024}}, } @article{57311, author = {{Yang, Keke and Sowada, Matthias and Olfert, Viktoria and Seitz, Georg and Schreiber, Vincent and Heitmann, Marcel and Hein, David and Biegler, Max and Jüttner, Sven and Rethmeier, Michael and Meschut, Gerson}}, issn = {{2238-7854}}, journal = {{Journal of Materials Research and Technology}}, publisher = {{Elsevier BV}}, title = {{{Influence of liquid metal embrittlement on the failure behavior of dissimilar spot welds with advanced high-strength steel: A component study}}}, doi = {{10.1016/j.jmrt.2024.11.166}}, year = {{2024}}, } @inproceedings{56670, abstract = {{Systems Engineering is becoming increasingly important in the engineering of complex technical systems. Its introduction is forcing companies to undertake major transformation initiatives. As established change management approaches show, the corporate culture is an important key criterion for success of transformation. Therefore, when introducing Systems Engineering into an organization, transformation initiatives must be tailored to an existing corporate culture or the corporate culture itself must be changed in order to enable Systems Engineering. In literature and in industrial practice, different approaches for assessment of corporate culture exist. Within this research, a systematic literature review on methods and models for corporate culture assessment is conducted. Core elements are collected and combined with the fundamentals and success factors of Systems Engineering to develop a model for corporate culture assessment. The developed model is applied to the industrial practice of an ongoing Systems Engineering transformation of a large car manufacturer. The results of the assessment are compared with the emerging project challenges. Based on this model and its supporting tool and templates, organizations and transformation leaders are enabled to rapidly obtain an orientation of hindering or supporting currently established cultural aspects with regard to Systems Engineering transformation and to provide a decision basis for further measures.}}, author = {{Graessler, Iris and Grewe, Benedikt}}, booktitle = {{AHFE International}}, issn = {{2771-0718}}, publisher = {{AHFE International}}, title = {{{Importance of cultural change in Systems Engineering Transformation: A model for cultural assessment}}}, doi = {{10.54941/ahfe1005551}}, volume = {{158}}, year = {{2024}}, } @inproceedings{56346, author = {{Gräßler, Iris and Özcan, Deniz}}, booktitle = {{AHFE International}}, location = {{Split}}, publisher = {{AHFE International}}, title = {{{Quality Key Figures for Developing Future Scenarios}}}, doi = {{10.54941/ahfe1005553}}, volume = {{158}}, year = {{2024}}, } @article{62767, abstract = {{Abstract In this study, we develop a novel multi-fidelity deep learning approach that transforms low-fidelity solution maps into high-fidelity ones by incorporating parametric space information into an autoencoder architecture. This method’s integration of parametric space information significantly reduces the amount of training data needed to effectively predict high-fidelity solutions from low-fidelity ones. In this study, we examine a two-dimensional steady-state heat transfer analysis within a heterogeneous materials microstructure. The heat conductivity coefficients for two different materials are condensed from a 101  $$\times $$ ×  101 grid to smaller grids. We then solve the boundary value problem on the coarsest grid using a pre-trained physics-informed neural operator network known as Finite Operator Learning (FOL). The resulting low-fidelity solution is subsequently upscaled back to a 101  $$\times $$ ×  101 grid using a newly designed enhanced autoencoder. The novelty of the developed enhanced autoencoder lies in the concatenation of heat conductivity maps of different resolutions to the decoder segment in distinct steps. Hence the developed algorithm is named microstructure-embedded autoencoder (MEA). We compare the MEA outcomes with those from finite element methods, the standard U-Net, and an interpolation approach as an upscaling technique. Our analysis shows that MEA outperforms these methods in terms of computational efficiency and error on representative test cases. As a result, the MEA serves as a potential supplement to neural operator networks, effectively upscaling low-fidelity solutions to high-fidelity while preserving critical details often lost in traditional upscaling methods, such as sharp interfaces features lost in the context of interpolation approaches.}}, author = {{Najafi Koopas, Rasoul and Rezaei, Shahed and Rauter, Natalie and Ostwald, Richard and Lammering, Rolf}}, issn = {{0178-7675}}, journal = {{Computational Mechanics}}, number = {{4}}, pages = {{1377--1406}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{Introducing a microstructure-embedded autoencoder approach for reconstructing high-resolution solution field data from a reduced parametric space}}}, doi = {{10.1007/s00466-024-02568-z}}, volume = {{75}}, year = {{2024}}, } @article{62770, abstract = {{AbstractThe open-source parameter identification tool ADAPT (A diversely applicable parameter identification Tool) is integrated with a machine learning-based approach for start value prediction in order to calibrate a Gurson–Tvergaard–Needleman (GTN) and a Lemaitre damage model. As representative example case-hardened steel 16MnCrS5 is elaborated. An artificial neural network (ANN) is initially trained by using load–displacement curves derived from simulations of a boundary value problem—instead of using data generated for homogeneous states of deformation at material point or one-element level—with varying material parameter combinations. The ANN is then employed so as to predict sets of material parameters that already provide close solutions to the experiment. These predicted parameter sets serve as starting values for a subsequent multi-objective parameter identification by using ADAPT. ADAPT allows for the consideration of input data from multiple scales, including integral data such as load–displacement curves, full-field data such as displacement and strain fields, and high-resolution experimental void data at the micro-scale. The influence of each data set on prediction quality is analyzed. Using various types of input data introduces additional information, enhancing prediction accuracy. The validation is carried out with respect to experimental void measurements of forward rod extruded parts. The results demonstrate, by incorporating void measurements in the optimization process, that it is possible to improve the quantitative prediction of ductile damage in the sense of void area fractions by factor 28 in forward rod extrusion.}}, author = {{Gerlach, Jan and Schulte, Robin and Schowtjak, Alexander and Clausmeyer, Till and Ostwald, Richard and Tekkaya, A. Erman and Menzel, Andreas}}, issn = {{0939-1533}}, journal = {{Archive of Applied Mechanics}}, number = {{8}}, pages = {{2217--2242}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{Enhancing damage prediction in bulk metal forming through machine learning-assisted parameter identification}}}, doi = {{10.1007/s00419-024-02634-1}}, volume = {{94}}, year = {{2024}}, } @article{62768, author = {{Najafi Koopas, Rasoul and Rezaei, Shahed and Rauter, Natalie and Ostwald, Richard and Lammering, Rolf}}, issn = {{0013-7944}}, journal = {{Engineering Fracture Mechanics}}, publisher = {{Elsevier BV}}, title = {{{A spatiotemporal deep learning framework for prediction of crack dynamics in heterogeneous solids: Efficient mapping of concrete microstructures to its fracture properties}}}, doi = {{10.1016/j.engfracmech.2024.110675}}, volume = {{314}}, year = {{2024}}, } @article{63346, abstract = {{ Lightweight design by using low-density and load-adapted materials can reduce the weight of vehicles and the emissions generated during operation. However, the usage of different materials requires innovative joining technologies with increased versatility. In this investigation, the focus is on describing and characterising the failure behaviour of connections manufactured by an innovative thermomechanical joining process with adaptable auxiliary joining elements in single-lap tensile-shear tests. In order to analyse the failure development in detail, the specimens are investigated using in-situ computed tomography (in-situ CT). Here, the tensile-shear test is interrupted at points of interest and CT scans are conducted under load. In addition, the interrupted in-situ testing procedure is validated by comparing the loading behaviour with conventional continuous tensile-shear tests. The results of the in-situ investigations of joints with varying material combinations clearly describe the cause of failure, allowing conclusions towards an improved joint design. }}, author = {{Borgert, Thomas and Köhler, D and Wiens, Eugen and Kupfer, R and Troschitz, J and Homberg, Werner and Gude, M}}, issn = {{1464-4207}}, journal = {{Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications}}, number = {{12}}, pages = {{2299--2306}}, publisher = {{SAGE Publications}}, title = {{{In-situ computed tomography analysis of the failure mechanisms of thermomechanically manufactured joints with auxiliary joining element}}}, doi = {{10.1177/14644207241232233}}, volume = {{238}}, year = {{2024}}, } @unpublished{56289, author = {{Seeger, Karl and Genovese, Matteo and Schlüter, Alexander and Kockel, Christina and Corigliano, Orlando and Díaz Canales, Edith Benjamina and Fragiacomo, Petronilla and Praktiknjo, Aaron}}, booktitle = {{United States Association for Energy Economics (USAEE) & International Association for Energy Economics (IAEE) Research Paper Series}}, publisher = {{Elsevier BV}}, title = {{{Evaluating Supply Scenarios for Hydrogen and Green Fuels from Canada, Chile, and Algeria to Germany via a Techno-Economic Assessment}}}, year = {{2024}}, } @inproceedings{56357, author = {{Díaz Canales, Edith Benjamina and Avila , Alfredo and Schlüter, Sabine and Lacayo, Erick and Schlüter, Alexander}}, booktitle = {{19th Conference on Sustainable Development of Energy, Water and Environment Systems}}, location = {{Rome}}, publisher = {{ Faculty of Mechanical Engineering and Naval Architecture, Zagreb}}, title = {{{Implementing Strategic Environmental Assessment (SEA) in the Global South, a challenge: Nicaragua as a case study.}}}, 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}}, }