@article{58491, abstract = {{Similar to bulk metal forming, clinch joining is characterised by large plastic deformations and a variety of different 3D stress states, including severe compression. However, inherent to plastic forming is the nucleation and growth of defects, whose detrimental effects on the material behaviour can be described by continuum damage models and eventually lead to material failure. As the damage evolution strongly depends on the stress state, a stress-state-dependent model is utilised to correctly track the accumulation. To formulate and parameterise this model, besides classical experiments, so-called modified punch tests are also integrated herein to enhance the calibration of the failure model by capturing a larger range of stress states and metal-forming-specific loading conditions. Moreover, when highly ductile materials are considered, such as the dual-phase steel HCT590X and the aluminium alloy EN AW-6014 T4 investigated here, strong necking and localisation might occur prior to fracture. This can alter the stress state and affect the actual strain at failure. This influence is captured by coupling plasticity and damage to incorporate the damage-induced softening effect. Its relative importance is shown by conducting inverse parameter identifications to determine damage and failure parameters for both mentioned ductile metals based on up to 12 different experiments.}}, author = {{Friedlein, Johannes and Böhnke, Max and Schlichter, Malte and Bobbert, Mathias and Meschut, Gerson and Mergheim, Julia and Steinmann, Paul}}, issn = {{2504-4494}}, journal = {{Journal of Manufacturing and Materials Processing}}, keywords = {{ductile damage, stress-state dependency, failure, parameter identification, punch test, clinching}}, number = {{4}}, publisher = {{MDPI AG}}, title = {{{Material Parameter Identification for a Stress-State-Dependent Ductile Damage and Failure Model Applied to Clinch Joining}}}, doi = {{10.3390/jmmp8040157}}, volume = {{8}}, year = {{2024}}, } @inproceedings{59237, abstract = {{Batch and process fluctuations during the fabrication of sheet metal components result in discrepancies in the resulting component properties, affecting subsequent process steps and potentially leading to production rejects. Consequently, the identification of deviations and knowledge of the effects of fluctuations are crucial for achieving consistently high product quality, reducing waste and thus increasing resource efficiency of production processes through countermeasures derived from this. The approach presented to address this is the use of data-driven metamodeling to map entire process chains and predict process parameters in order to compensate for process and batch fluctuation. The investigated process chain consists of the sub-processes deep drawing, clamping and clinching. For each process step, relevant input and output variables are identified, numerical simulation models are created, and subsequently validated. Variant simulations of the sub-processes are conducted and evaluated to generate a database for the metamodeling of the individual process steps. Machine learning techniques are utilized for the automated selection and optimization of learning methods to create models that depict the relationships between input and output variables. Finally, the models for the sub-processes are linked together to form a superordinate metamodel for the entire process chain, with the aim to make inline-process adaptations possible.Abstract Purpose Cardiopulmonary exercise testing (CPET) is considered the gold standard for assessing cardiorespiratory fitness. To ensure consistent performance of each test, it is necessary to adapt the power increase of the test protocol to the physical characteristics of each individual. This study aimed to use machine learning models to determine individualized ramp protocols based on non-exercise features. We hypothesized that machine learning models will predict peak oxygen uptake ($$\dot{V}$$ V ˙ O2peak) and peak power output (PPO) more accurately than conventional multiple linear regression (MLR). Methods The cross-sectional study was conducted with 274 (♀168, ♂106) participants who performed CPET on a cycle ergometer. Machine learning models and multiple linear regression were used to predict $$\dot{V}$$ V ˙ O2peak and PPO using non-exercise features. The accuracy of the models was compared using criteria such as root mean square error (RMSE). Shapley additive explanation (SHAP) was applied to determine the feature importance. Results The most accurate machine learning model was the random forest (RMSE: 6.52 ml/kg/min [95% CI 5.21–8.17]) for $$\dot{V}$$ V ˙ O2peak prediction and the gradient boosting regression (RMSE: 43watts [95% CI 35–52]) for PPO prediction. Compared to the MLR, the machine learning models reduced the RMSE by up to 28% and 22% for prediction of $$\dot{V}$$ V ˙ O2peak and PPO, respectively. Furthermore, SHAP ranked body composition data such as skeletal muscle mass and extracellular water as the most impactful features. Conclusion Machine learning models predict $$\dot{V}$$ V ˙ O2peak and PPO more accurately than MLR and can be used to individualize CPET protocols. Features that provide information about the participant's body composition contribute most to the improvement of these predictions. Trial registration number DRKS00031401 (6 March 2023, retrospectively registered). }}, author = {{Wenzel, Charlotte and Liebig, Thomas and Swoboda, Adrian and Smolareck, Rika and Schlagheck, Marit Lea and Walzik, David and Groll, Andreas and Goulding, Richie P. and Zimmer, Philipp}}, issn = {{1439-6319}}, journal = {{European Journal of Applied Physiology}}, number = {{11}}, pages = {{3421--3431}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{Machine learning predicts peak oxygen uptake and peak power output for customizing cardiopulmonary exercise testing using non-exercise features}}}, doi = {{10.1007/s00421-024-05543-x}}, volume = {{124}}, year = {{2024}}, } @article{60176, abstract = {{AbstractAimTo investigate the associations of the Dietary Approaches to Stop Hypertension (DASH) score with subcutaneous (SAT) and visceral (VAT) adipose tissue volume and hepatic lipid content (HLC) in people with diabetes and to examine whether changes in the DASH diet were associated with changes in these outcomes.MethodsIn total, 335 participants with recent‐onset type 1 diabetes (T1D) and type 2 diabetes (T2D) from the German Diabetes Study were included in the cross‐sectional analysis, and 111 participants in the analysis of changes during the 5‐year follow‐up. Associations between the DASH score and VAT, SAT and HLC and their changes were investigated using multivariable linear regression models by diabetes type. The proportion mediated by changes in potential mediators was determined using mediation analysis.ResultsA higher baseline DASH score was associated with lower HLC, especially in people with T2D (per 5 points: −1.5% [−2.7%; −0.3%]). Over 5 years, a 5‐point increase in the DASH score was associated with decreased VAT in people with T2D (−514 [−800; −228] cm3). Similar, but imprecise, associations were observed for VAT changes in people with T1D (−403 [−861; 55] cm3) and for HLC in people with T2D (−1.3% [−2.8%; 0.3%]). Body mass index and waist circumference changes explained 8%‐48% of the associations between DASH and VAT changes in both groups. In people with T2D, adipose tissue insulin resistance index (Adipo‐IR) changes explained 47% of the association between DASH and HLC changes.ConclusionsA shift to a DASH‐like diet was associated with favourable VAT and HLC changes, which were partly explained by changes in anthropometric measures and Adipo‐IR.}}, author = {{Schaefer, Edyta and Lang, Alexander and Kupriyanova, Yuliya and Bódis, Kálmán B. and Weber, Katharina S. and Buyken, Anette and Barbaresko, Janett and Kössler, Theresa and Kahl, Sabine and Zaharia, Oana‐Patricia and Szendroedi, Julia and Herder, Christian and Schrauwen‐Hinderling, Vera B. and Wagner, Robert and Kuss, Oliver and Roden, Michael and Schlesinger, Sabrina}}, issn = {{1462-8902}}, journal = {{Diabetes, Obesity and Metabolism}}, number = {{10}}, pages = {{4281--4292}}, publisher = {{Wiley}}, title = {{{Adherence to the Dietary Approaches to Stop Hypertension (DASH) diet is associated with lower visceral and hepatic lipid content in recent‐onset type 1 diabetes and type 2 diabetes}}}, doi = {{10.1111/dom.15772}}, volume = {{26}}, year = {{2024}}, } @inproceedings{64104, author = {{Scheideler, Christian and Hinnenthal , Kristian and Liedtke, David Jan}}, title = {{{Efficient Shape Formation by 3D Hybrid Programmable Matter: An Algorithm for Low Diameter Intermediate Structures. SAND 2024: 15:1-15:20}}}, year = {{2024}}, } @inproceedings{64106, author = {{Scheideler, Christian and Kostitsyna, Irina and Liedtke, David Jan}}, title = {{{Universal Coating by 3D Hybrid Programmable Matter.}}}, year = {{2024}}, } @article{64002, abstract = {{The production of formaldehyde on industrial scale requires huge amounts of energy due to the involvement of reforming processes in combination with the demand in the megaton scale. Hence, a direct route for the transformation of (bio)methane to formaldehyde would decrease costs and puts less pressure on the environment. Herein, we report on the use of zinc modified silicas as possible support materials for vanadium catalysts and the resulting consequences for the performance in the selective oxidation of methane to formaldehyde. After optimization of the Zn content and reaction conditions, a remarkably high space-time yield of 12.4 kgCH2O·kgcat−1·h−1 was achieved. As a result of the extensive characterization by means of UV–vis, Raman, XANES and NMR spectroscopy it was found that vanadium is in the vicinity of highly dispersed zinc atoms which promote the formation of active vanadium species as supposed by theoretical calculations. This work presents a further step of catalyst development towards direct industrial methane conversion which may help to overcome current limitations in the future.}}, author = {{Kunkel, Benny and Seeburg, Dominik and Kabelitz, Anke and Witte, Steffen and Gutmann, Torsten and Breitzke, Hergen and Buntkowsky, Gerd and Buzanich, Ana Guilherme and Wohlrab, Sebastian}}, journal = {{Catalysis Today}}, keywords = {{Formaldehyde, Local coordination, SBA-15, Vanadium oxo species, XANES, Zinc doped silica}}, pages = {{114643}}, title = {{{Highly productive V/Zn-SiO2 catalysts for the selective oxidation of methane}}}, doi = {{10.1016/j.cattod.2024.114643}}, volume = {{432}}, year = {{2024}}, } @inproceedings{58223, abstract = {{The Shapley value (SV) is a prevalent approach of allocating credit to machine learning (ML) entities to understand black box ML models. Enriching such interpretations with higher-order interactions is inevitable for complex systems, where the Shapley Interaction Index (SII) is a direct axiomatic extension of the SV. While it is well-known that the SV yields an optimal approximation of any game via a weighted least square (WLS) objective, an extension of this result to SII has been a long-standing open problem, which even led to the proposal of an alternative index. In this work, we characterize higher-order SII as a solution to a WLS problem, which constructs an optimal approximation via SII and k-Shapley values (k-SII). We prove this representation for the SV and pairwise SII and give empirically validated conjectures for higher orders. As a result, we propose KernelSHAP-IQ, a direct extension of KernelSHAP for SII, and demonstrate state-of-the-art performance for feature interactions.}}, author = {{Fumagalli, Fabian and Muschalik, Maximilian and Kolpaczki, Patrick and Hüllermeier, Eyke and Hammer, Barbara}}, booktitle = {{Proceedings of the 41st International Conference on Machine Learning (ICML)}}, pages = {{14308–14342}}, publisher = {{PMLR}}, title = {{{KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions}}}, volume = {{235}}, year = {{2024}}, } @article{61834, abstract = {{Abstract3D printing or additive manufacturing (AM) possesses enormous potential to benefit the manufacturing industry. Presently, rotary draw bending (RDB) is one of the most commonly used cold-forming industrial process for bending metal tubes. Pressure die is a fundamental forming tool in RDB processes, and it is conventionally made by various grades of comparatively expensive alloy steels. This research presents a novel design of a pressure die which can be 3D printed by using inexpensive polymeric filaments. In this research paper, the 3D-printed pressure die is named as “FFF-pressure die.” The material used to fabricate the FFF-pressure die is a thermoplastic polymer known as “ecoPLA.” The mechanical properties of ecoPLA are studied in relation to the process conditions of a RDB process. Firstly, an initial feasibility of using the FFF-pressure die in a RDB process is obtained by conducting a quick static stress analysis with actual process conditions. After initial feasibility, a complete RDB process is developed and simulated with actual process conditions and material properties. The FFF-pressure die is then practically fabricated by FFF 3D printer and experimentally tested on an industrial RDB machine. The results of practical experiments are compared with the simulation results. In order to make a comparison of the FFF-pressure die with the conventional metal pressure die, the simulation and practical process is also conducted with the conventional metal pressure die. A performance and cost comparison is made between the polymeric FFF-pressure die and the conventional metal pressure die. Von Mises stresses, contact forces, failure risk, and elastic deformations are analyzed. The advantages and limitations of using the FFF-pressure die in a RDB process are discussed in the end. This research intends to widen the avenue of using cost-effective and lightweight forming tools in metal forming industries.}}, author = {{Kaleem, Muhammad Ali and Steinheimer, Rainer and Frohn-Sörensen, Peter and Gabsa, Steffen and Engel, Bernd}}, issn = {{0268-3768}}, journal = {{The International Journal of Advanced Manufacturing Technology}}, number = {{3-4}}, pages = {{1789--1804}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{Additive manufacturing of polymeric pressure die for rotary draw bending process}}}, doi = {{10.1007/s00170-024-14221-3}}, volume = {{134}}, 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{62942, abstract = {{AbstractNanostructured bilayer thin films with superhydrophobic and superhydrophilic surfaces were prepared using Ti6Al4V alloy substrates which allowed for the comparative analysis of polyvinyl acetate (PVAc) particle adsorption as a function of the interface structure. The PVAc particles were obtained from emulsion polymerization of vinyl acetate. A superhydrophilic TiO2 nanofiber-based 3D network was created on the Ti6Al4V alloy substrate by application of a hydrothermal method. Subsequent UV-grafting of ultra-thin polydimethylsiloxane (PDMS) layers resulted in a superhydrophobic surface. The modification steps were followed via Diffuse Reflectance Infrared Fourier Transform Spectroscopy, X-ray Photoelectron Spectroscopy, Field Emission-Scanning Electron Microscopy, contact angle and Electrochemical Impedance Spectroscopy. A mechanism for the adsorption of PVAc at the two electrolyte/substrate interfaces could be revealed.}}, author = {{Neßlinger, Vanessa and Atlanov, Jan and Grundmeier, Guido}}, issn = {{3004-9261}}, journal = {{Discover Applied Sciences}}, number = {{6}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{Interactions of polyvinyl acetate dispersions with nanostructured superhydrophilic and superhydrophobic Ti6Al4V alloy surfaces}}}, doi = {{10.1007/s42452-024-05916-z}}, volume = {{6}}, year = {{2024}}, } @article{53146, author = {{Berger, Thomas and Dennstädt, Dario and Lanza, L. and Worthmann, K. }}, journal = {{SIAM Journal on Control and Optimization}}, title = {{{Robust Funnel Model Predictive Control for Output Tracking with Prescribed Performance}}}, year = {{2024}}, } @inproceedings{63497, author = {{Förster, Nikolas and Wallscheid, Oliver and Schafmeister, Frank}}, booktitle = {{2024 IEEE Design Methodologies Conference (DMC)}}, keywords = {{MOSFET, Thermal resistance, Surface resistance, Bridge circuits, Zero voltage switching, Pareto optimization, Capacitance, Numerical simulation, Optimization, Resistance heating, Pareto Optimization, Dual-Active Bridge, ZVS, Inductor Optimization, Transformer Optimization, Heat Sink Optimization}}, pages = {{1--8}}, title = {{{Dual-Active Bridge Sequential Pareto Optimization for Fast Pre-Design and Final Component Selection}}}, doi = {{10.1109/DMC62632.2024.10812131}}, 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{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}}, } @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{57111, author = {{Mihaylov, Martin Miroslavov and Kress, Christian and Scheytt, J. Christoph}}, location = {{Paderborn}}, title = {{{Simulation and Optimization of Low-Loss Photonic Coupling Structures for TFLN Integrated Circuits for Quantum Applications}}}, year = {{2024}}, } @article{57429, author = {{Krueger, Bettina and Stutz, Bianca and Jakobsmeyer, Rasmus and Reinsberger, Claus and Buyken, Anette E.}}, issn = {{0742-0528}}, journal = {{Chronobiology International}}, pages = {{1--10}}, publisher = {{Informa UK Limited}}, title = {{{Relevance of high glycaemic index breakfast for heart rate variability among collegiate students with early and late chronotypes}}}, doi = {{10.1080/07420528.2024.2428203}}, year = {{2024}}, } @inproceedings{54356, abstract = {{Although there are numerous design and control methodologies for the LLC resonant converter, they often do not consider decentralized control strategies to operate them as isolated DC-DC converters within a cascaded H-bridge. The total output power of all LLC converters must be constant to supply a load such as a wa- ter electrolyzer. However, each individual LLC converter can vary its output power as long as the total output power remains constant. This opens new possibilities in increasing the system efficiency and robustness. Usually, the DC-link voltage of each module capacitor shows a 2nd harmonic voltage ripple. However, the total stored energy in all DC-link capacitors is constant within a grid period for a balanced three-phase system. By controlling each LLC converter’s output power locally to be proportional to the energy stored in its DC-link capacitor, modules with a lower instantaneous DC-link voltage transfer less power to the load than modules with a higher DC-link voltage. As a result, a higher efficiency, voltage gain and lower peak resonant capacitor voltage can be achieved with the same components. The 22.2kW experimental prototype of the LLC converter reaches an efficiency of over 97% at resonance which is similar to the precalculated value.}}, author = {{Unruh, Roland and Böcker, Joachim and Schafmeister, Frank}}, booktitle = {{ECCE Europe 2024; IEEE Energy Conversion Congress & Exposition Europe}}, isbn = {{979-8-3503-6444-6}}, keywords = {{Cascaded H-Bridge, Converter Losses, Decentralized Control, Full-Bridge Converter, LLC Resonant Converter}}, location = {{Darmstadt, Germany}}, publisher = {{IEEE}}, title = {{{Experimentally Verified 22 kW, 40 kHz LLC Resonant Converter Design with new Control for a 1 MW Cascaded H-Bridge Converter}}}, doi = {{https://doi.org/10.1109/ECCEEurope62508.2024.10751954}}, year = {{2024}}, }