--- _id: '64678' abstract: - lang: eng text: "One of the major topics in the modern automotive industry is reducing emissions and increasing the mileage\r\nrange. To tackle this challenge, on the one hand, modifying the powertrain system is a possibility, and on the\r\nother hand, lightweight design offers various possibilities. Multi-Material Design (MMD) involves designing car\r\nbodies that combine different materials that require joining. Given the variety of materials, mechanical joining\r\nprocesses are preferred. Especially the current development of the Giga/Mega-casting process concerning\r\naluminium casting and the subsequent mechanical joining illustrates the challenges of this material group. In car\r\nproduction, aluminium castings are mainly made from aluminium-silicon (AlSi) alloys. Ultimately, the alloy\r\nsystem's insufficient ductility leads to crack initiation during mechanical joining. Cast parts are therefore often\r\nused in areas of the car body that are exposed to high-pressure loads. For example, self-piercing riveting (SPR) is\r\nused due to its high load-bearing capacity. In this study, improved joinability is demonstrated by influencing the\r\nmicrostructure through tailored solidification rates and a developed heat-treatment chain strategy adapted for\r\nhypoeutectic AlSi systems. Data on microstructure, mechanical, and joining properties are used to develop a\r\nsolidification-joining correlation for the SPR process across a range of Si contents and solidification rates. The\r\npurpose is to develop the ability to produce suitable aluminium castings with sufficient joinability, thereby\r\nimproving versatility." article_type: original author: - first_name: Moritz full_name: Neuser, Moritz id: '32340' last_name: Neuser - first_name: Pia Katharina full_name: Kaimann, Pia Katharina id: '44935' last_name: Kaimann - first_name: Ina full_name: Stratmann, Ina last_name: Stratmann - first_name: Mathias full_name: Bobbert, Mathias id: '7850' last_name: Bobbert - first_name: Johann Moritz Benedikt full_name: Klöckner, Johann Moritz Benedikt last_name: Klöckner - first_name: Moritz full_name: Mann, Moritz last_name: Mann - first_name: Kay-Peter full_name: Hoyer, Kay-Peter id: '48411' last_name: Hoyer - first_name: Gerson full_name: Meschut, Gerson id: '32056' last_name: Meschut orcid: 0000-0002-2763-1246 - first_name: Mirko full_name: Schaper, Mirko id: '43720' last_name: Schaper citation: ama: Neuser M, Kaimann PK, Stratmann I, et al. Solidification-joinability correlation of hypoeutectic aluminium casting alloys for self-piercing riveting (SPR). Journal of Manufacturing Processes. 2026;164. doi:https://doi.org/10.1016/j.jmapro.2026.02.040 apa: Neuser, M., Kaimann, P. K., Stratmann, I., Bobbert, M., Klöckner, J. M. B., Mann, M., Hoyer, K.-P., Meschut, G., & Schaper, M. (2026). Solidification-joinability correlation of hypoeutectic aluminium casting alloys for self-piercing riveting (SPR). Journal of Manufacturing Processes, 164. https://doi.org/10.1016/j.jmapro.2026.02.040 bibtex: '@article{Neuser_Kaimann_Stratmann_Bobbert_Klöckner_Mann_Hoyer_Meschut_Schaper_2026, title={Solidification-joinability correlation of hypoeutectic aluminium casting alloys for self-piercing riveting (SPR)}, volume={164}, DOI={https://doi.org/10.1016/j.jmapro.2026.02.040}, journal={Journal of Manufacturing Processes}, publisher={Elsevier}, author={Neuser, Moritz and Kaimann, Pia Katharina and Stratmann, Ina and Bobbert, Mathias and Klöckner, Johann Moritz Benedikt and Mann, Moritz and Hoyer, Kay-Peter and Meschut, Gerson and Schaper, Mirko}, year={2026} }' chicago: Neuser, Moritz, Pia Katharina Kaimann, Ina Stratmann, Mathias Bobbert, Johann Moritz Benedikt Klöckner, Moritz Mann, Kay-Peter Hoyer, Gerson Meschut, and Mirko Schaper. “Solidification-Joinability Correlation of Hypoeutectic Aluminium Casting Alloys for Self-Piercing Riveting (SPR).” Journal of Manufacturing Processes 164 (2026). https://doi.org/10.1016/j.jmapro.2026.02.040. ieee: 'M. Neuser et al., “Solidification-joinability correlation of hypoeutectic aluminium casting alloys for self-piercing riveting (SPR),” Journal of Manufacturing Processes, vol. 164, 2026, doi: https://doi.org/10.1016/j.jmapro.2026.02.040.' mla: Neuser, Moritz, et al. “Solidification-Joinability Correlation of Hypoeutectic Aluminium Casting Alloys for Self-Piercing Riveting (SPR).” Journal of Manufacturing Processes, vol. 164, Elsevier, 2026, doi:https://doi.org/10.1016/j.jmapro.2026.02.040. short: M. Neuser, P.K. Kaimann, I. Stratmann, M. Bobbert, J.M.B. Klöckner, M. Mann, K.-P. Hoyer, G. Meschut, M. Schaper, Journal of Manufacturing Processes 164 (2026). date_created: 2026-02-26T11:21:24Z date_updated: 2026-02-26T11:22:03Z department: - _id: '43' - _id: '158' - _id: '157' - _id: '321' doi: https://doi.org/10.1016/j.jmapro.2026.02.040 funded_apc: '1' intvolume: ' 164' keyword: - Mechanical joining - Aluminium - Self-piercing riveting - Casting - Microstructure - Joinability AlSi-alloys language: - iso: eng project: - _id: '131' name: TRR 285 - Project Area A - _id: '133' name: TRR 285 - Project Area C - _id: '136' name: TRR 285 - Subproject A02 - _id: '146' name: TRR 285 - Subproject C02 - _id: '130' name: 'TRR 285: Methodenentwicklung zur mechanischen Fügbarkeit in wandlungsfähigen Prozessketten' publication: Journal of Manufacturing Processes publication_status: published publisher: Elsevier quality_controlled: '1' status: public title: Solidification-joinability correlation of hypoeutectic aluminium casting alloys for self-piercing riveting (SPR) type: journal_article user_id: '32340' volume: 164 year: '2026' ... --- _id: '64985' abstract: - lang: eng text: Modern industrial development has necessitated a wide range of joining technologies. Self-pierce riveting has become a prevalent technique for sheet metal assembly, especially in automotive applications. Achieving proper joint geometry and adequate load-bearing capacity depends on appropriate tool selection and precise process control. Material properties and condition also play a significant role in process performance. To accommodate the inevitable variations in component characteristics during production, a robust and stable joining process is essential. The study focuses on investigating the influence of preformed joining partners on the joining process and the joint's load capacity. An EN AW-6014 in T4 condition, as well as an HCT590X, are used as materials for this study. For this purpose, an exemplary process chain consisting of the steps of performing, joining, and shear load testing is studied. Each process step is implemented using an FE model to predict the outcome of subsequent steps. For analysis of the influence of pre-strain, an optimisation software is used to plan and execute variations of the process. These variations are used to create a meta-model that can describe the relationships between pre-forming and characteristic parameters of subsequent process steps. The resulting model is validated by comparing simulation and experimental data. Finally, in a novel approach, the robustness of the presented process chain is analyzed in terms of a tolerable performance level for the joining partners. article_number: '100391' author: - first_name: Jean-Patrick full_name: Ludwig, Jean-Patrick id: '76631' last_name: Ludwig - first_name: Emil full_name: Tolke, Emil last_name: Tolke - first_name: Malte Christian full_name: Schlichter, Malte Christian id: '61977' last_name: Schlichter - first_name: Mathias full_name: Bobbert, Mathias id: '7850' last_name: Bobbert - first_name: Gerson full_name: Meschut, Gerson id: '32056' last_name: Meschut orcid: 0000-0002-2763-1246 citation: ama: Ludwig J-P, Tolke E, Schlichter MC, Bobbert M, Meschut G. Numerical analysis of the robustness of self-pierce riveting with pre-formed joining partners. Journal of Advanced Joining Processes. 2026;13. doi:10.1016/j.jajp.2026.100391 apa: Ludwig, J.-P., Tolke, E., Schlichter, M. C., Bobbert, M., & Meschut, G. (2026). Numerical analysis of the robustness of self-pierce riveting with pre-formed joining partners. Journal of Advanced Joining Processes, 13, Article 100391. https://doi.org/10.1016/j.jajp.2026.100391 bibtex: '@article{Ludwig_Tolke_Schlichter_Bobbert_Meschut_2026, title={Numerical analysis of the robustness of self-pierce riveting with pre-formed joining partners}, volume={13}, DOI={10.1016/j.jajp.2026.100391}, number={100391}, journal={Journal of Advanced Joining Processes}, publisher={Elsevier BV}, author={Ludwig, Jean-Patrick and Tolke, Emil and Schlichter, Malte Christian and Bobbert, Mathias and Meschut, Gerson}, year={2026} }' chicago: Ludwig, Jean-Patrick, Emil Tolke, Malte Christian Schlichter, Mathias Bobbert, and Gerson Meschut. “Numerical Analysis of the Robustness of Self-Pierce Riveting with Pre-Formed Joining Partners.” Journal of Advanced Joining Processes 13 (2026). https://doi.org/10.1016/j.jajp.2026.100391. ieee: 'J.-P. Ludwig, E. Tolke, M. C. Schlichter, M. Bobbert, and G. Meschut, “Numerical analysis of the robustness of self-pierce riveting with pre-formed joining partners,” Journal of Advanced Joining Processes, vol. 13, Art. no. 100391, 2026, doi: 10.1016/j.jajp.2026.100391.' mla: Ludwig, Jean-Patrick, et al. “Numerical Analysis of the Robustness of Self-Pierce Riveting with Pre-Formed Joining Partners.” Journal of Advanced Joining Processes, vol. 13, 100391, Elsevier BV, 2026, doi:10.1016/j.jajp.2026.100391. short: J.-P. Ludwig, E. Tolke, M.C. Schlichter, M. Bobbert, G. Meschut, Journal of Advanced Joining Processes 13 (2026). date_created: 2026-03-16T12:30:39Z date_updated: 2026-03-16T12:38:13Z department: - _id: '9' doi: 10.1016/j.jajp.2026.100391 intvolume: ' 13' keyword: - Self-pierce riveting - FE modelling - Plastic pre-deformation - Meta modelling language: - iso: eng project: - _id: '131' name: TRR 285 - Project Area A - _id: '135' name: TRR 285 - Subproject A01 - _id: '130' name: 'TRR 285: Methodenentwicklung zur mechanischen Fügbarkeit in wandlungsfähigen Prozessketten' publication: Journal of Advanced Joining Processes publication_identifier: issn: - 2666-3309 publication_status: published publisher: Elsevier BV quality_controlled: '1' status: public title: Numerical analysis of the robustness of self-pierce riveting with pre-formed joining partners type: journal_article user_id: '76631' volume: 13 year: '2026' ... --- _id: '59483' abstract: - lang: eng text: Abstract. The assessment of mechanically joined connections, such as clinched connections, is usually conducted destructively. Applicable non-destructive testing methods like computed tomography are time-consuming and costly, or, like electrical resistance measurement, provide only a limited amount of information. A fast, non-destructive evaluation of the joints condition shall be made possible by using transient dynamic analysis (TDA). It is based on the introduction of sound waves and the evaluation of the response behavior after passing through the structure. This study focuses the application of TDA to clinched shear connections to evaluate the performance of the tactile measuring setup. Twenty-one series were investigated, covering variations in joining task, manufacturing and defect. The evaluation was carried out using machine learning to determine for which series characteristic signals may be detected. It was shown that a classification of the investigated specimens is possible, whereby the classification accuracy depends on the examined variation. Furthermore, the accuracy was evaluated as a function of frequency and results were concluded to identify the limits of the used measuring setup. author: - first_name: Gregor full_name: Reschke, Gregor last_name: Reschke - first_name: Alexander full_name: Brosius, Alexander last_name: Brosius citation: ama: 'Reschke G, Brosius A. Transient dynamic analysis: Performance evaluation of tactile measurement. In: Materials Research Proceedings. Vol 52. Materials Research Forum LLC; 2025:293-300. doi:10.21741/9781644903551-36' apa: 'Reschke, G., & Brosius, A. (2025). Transient dynamic analysis: Performance evaluation of tactile measurement. Materials Research Proceedings, 52, 293–300. https://doi.org/10.21741/9781644903551-36' bibtex: '@inproceedings{Reschke_Brosius_2025, title={Transient dynamic analysis: Performance evaluation of tactile measurement}, volume={52}, DOI={10.21741/9781644903551-36}, booktitle={Materials Research Proceedings}, publisher={Materials Research Forum LLC}, author={Reschke, Gregor and Brosius, Alexander}, year={2025}, pages={293–300} }' chicago: 'Reschke, Gregor, and Alexander Brosius. “Transient Dynamic Analysis: Performance Evaluation of Tactile Measurement.” In Materials Research Proceedings, 52:293–300. Materials Research Forum LLC, 2025. https://doi.org/10.21741/9781644903551-36.' ieee: 'G. Reschke and A. Brosius, “Transient dynamic analysis: Performance evaluation of tactile measurement,” in Materials Research Proceedings, Paderborn, 2025, vol. 52, pp. 293–300, doi: 10.21741/9781644903551-36.' mla: 'Reschke, Gregor, and Alexander Brosius. “Transient Dynamic Analysis: Performance Evaluation of Tactile Measurement.” Materials Research Proceedings, vol. 52, Materials Research Forum LLC, 2025, pp. 293–300, doi:10.21741/9781644903551-36.' short: 'G. Reschke, A. Brosius, in: Materials Research Proceedings, Materials Research Forum LLC, 2025, pp. 293–300.' conference: end_date: 2025-04-03 location: Paderborn name: 21st SheMet Conference start_date: 2025-04-01 date_created: 2025-04-10T11:27:20Z date_updated: 2025-04-10T11:33:28Z department: - _id: '43' - _id: '157' doi: 10.21741/9781644903551-36 intvolume: ' 52' keyword: - Joining - Machine Learning - Transient Dynamic Analysis language: - iso: eng page: 293-300 project: - _id: '130' grant_number: '418701707' name: 'TRR 285: TRR 285: Methodenentwicklung zur mechanischen Fügbarkeit in wandlungsfähigen Prozessketten' - _id: '133' name: 'TRR 285 - C: TRR 285 - Project Area C' - _id: '148' name: 'TRR 285 – C04: TRR 285 - Subproject C04' publication: Materials Research Proceedings publication_identifier: issn: - 2474-395X publication_status: published publisher: Materials Research Forum LLC quality_controlled: '1' status: public title: 'Transient dynamic analysis: Performance evaluation of tactile measurement' type: conference user_id: '98812' volume: 52 year: '2025' ... --- _id: '59708' abstract: - lang: ger text: Die Arbeitszufriedenheit von Lehrkräften gilt als zentrale Komponente für die Qualität des Bil­dungssystems. In inklusiven Schulen müssen Regelschullehrkräfte und sonderpädagogische Lehrkräfte kooperieren, um allen Schüler:innen eine bestmögliche Förderung zu gewährleisten. Dazu benötigen sie jedoch Zeitfenster, die von vielen Lehrkräften als nicht ausreichend benannt werden. Ziel des vorliegenden Beitrags ist es, empirisch zu untersuchen, welche Bedeutung festen Zeitfenstern für die Lehrkräftekooperation im Klassenteam, im Jahrgangsteam und im Fachteam für die Arbeitszufriedenheit zukommt. Weiterhin soll überprüft werden, ob Teile der Zusammenhänge über die Zufriedenheit mit der Kooperationshäufigkeit und die kollektive Selbstwirksamkeitsüberzeugung der Lehrkräfte erklärt werden können. Dazu werden Daten aus dem BMBF-geförderten Projekt BiFoKi mit N=194 Lehrkräften und N=28 Schulleitungen analy­siert. Die Ergebnisse zeigen, dass feste Zeitfenster für die Kooperation in den unterschiedlichen Teams mit einer erhöhten Arbeitszufriedenheit im Zusammenhang stehen und in Teilen über die kollektive Selbstwirksamkeitsüberzeugung mediiert werden. - lang: eng text: The job satisfaction of teachers is considered a central component for the quality of the educa­tion system. In inclusive schools, regular school teachers and special needs teachers must co­operate in order to ensure that all pupils receive the best possible support. To do this, however, they need time slots that many teachers say are not sufficient. The aim of this article is to em­pirically investigate the importance of fixed time slots for teacher cooperation in the class team, the year team and the expert team for job satisfaction. Furthermore, it will be examined whether parts of the correlations can be explained by satisfaction with the frequency of cooperation and the teachers' collective self-efficacy expectations. To this end, data from the BMBF-funded BiFoKi project with N=194 teachers and N=28 head teachers will be analyzed. The results show that fixed time slots for cooperation in the different teams are associated with increased job satisfaction and are mediated in part by collective self-efficacy expectations. alternative_title: - Time for job satisfaction? A quantitative-empirical study on the significance of fixed cooperation times for the job satisfaction of teachers in inclusive schools article_type: original author: - first_name: Verena full_name: Wohnhas, Verena last_name: Wohnhas - first_name: Phillip full_name: Neumann, Phillip id: '95559' last_name: Neumann - first_name: Birgit full_name: Lütje-Klose, Birgit last_name: Lütje-Klose citation: ama: Wohnhas V, Neumann P, Lütje-Klose B. Zeit für Arbeitszufriedenheit? Eine quantitativ-empirische Studie zur Bedeutung fester Kooperationszeiten für die Arbeitszufriedenheit von Lehrkräften in inklusiven Schulen. QfI - Qualifizierung für Inklusion Online-Zeitschrift zur Forschung über Aus-, Fort- und Weiterbildung pädagogischer Fachkräfte. 2025;6(2). doi:10.21248/qfi.167 apa: Wohnhas, V., Neumann, P., & Lütje-Klose, B. (2025). Zeit für Arbeitszufriedenheit? Eine quantitativ-empirische Studie zur Bedeutung fester Kooperationszeiten für die Arbeitszufriedenheit von Lehrkräften in inklusiven Schulen. QfI - Qualifizierung für Inklusion. Online-Zeitschrift zur Forschung über Aus-, Fort- und Weiterbildung pädagogischer Fachkräfte, 6(2). https://doi.org/10.21248/qfi.167 bibtex: '@article{Wohnhas_Neumann_Lütje-Klose_2025, title={Zeit für Arbeitszufriedenheit? Eine quantitativ-empirische Studie zur Bedeutung fester Kooperationszeiten für die Arbeitszufriedenheit von Lehrkräften in inklusiven Schulen}, volume={6}, DOI={10.21248/qfi.167}, number={2}, journal={QfI - Qualifizierung für Inklusion. Online-Zeitschrift zur Forschung über Aus-, Fort- und Weiterbildung pädagogischer Fachkräfte}, publisher={University Library J. C. Senckenberg}, author={Wohnhas, Verena and Neumann, Phillip and Lütje-Klose, Birgit}, year={2025} }' chicago: Wohnhas, Verena, Phillip Neumann, and Birgit Lütje-Klose. “Zeit für Arbeitszufriedenheit? Eine quantitativ-empirische Studie zur Bedeutung fester Kooperationszeiten für die Arbeitszufriedenheit von Lehrkräften in inklusiven Schulen.” QfI - Qualifizierung für Inklusion. Online-Zeitschrift zur Forschung über Aus-, Fort- und Weiterbildung pädagogischer Fachkräfte 6, no. 2 (2025). https://doi.org/10.21248/qfi.167. ieee: 'V. Wohnhas, P. Neumann, and B. Lütje-Klose, “Zeit für Arbeitszufriedenheit? Eine quantitativ-empirische Studie zur Bedeutung fester Kooperationszeiten für die Arbeitszufriedenheit von Lehrkräften in inklusiven Schulen,” QfI - Qualifizierung für Inklusion. Online-Zeitschrift zur Forschung über Aus-, Fort- und Weiterbildung pädagogischer Fachkräfte, vol. 6, no. 2, 2025, doi: 10.21248/qfi.167.' mla: Wohnhas, Verena, et al. “Zeit für Arbeitszufriedenheit? Eine quantitativ-empirische Studie zur Bedeutung fester Kooperationszeiten für die Arbeitszufriedenheit von Lehrkräften in inklusiven Schulen.” QfI - Qualifizierung für Inklusion. Online-Zeitschrift zur Forschung über Aus-, Fort- und Weiterbildung pädagogischer Fachkräfte, vol. 6, no. 2, University Library J. C. Senckenberg, 2025, doi:10.21248/qfi.167. short: V. Wohnhas, P. Neumann, B. Lütje-Klose, QfI - Qualifizierung für Inklusion. Online-Zeitschrift zur Forschung über Aus-, Fort- und Weiterbildung pädagogischer Fachkräfte 6 (2025). date_created: 2025-04-29T08:14:03Z date_updated: 2025-04-29T08:31:28Z department: - _id: '854' doi: 10.21248/qfi.167 intvolume: ' 6' issue: '2' keyword: - Arbeitszufriedenheit - Inklusion - Sonderpädagogik - Kooperation - Selbstwirksamkeit - Schulentwicklung - job satisfaction - Inclusion - Special Education - Self-efficacy - school development language: - iso: ger main_file_link: - open_access: '1' url: https://www.qfi-oz.de/index.php/inklusion/article/view/167 oa: '1' publication: QfI - Qualifizierung für Inklusion. Online-Zeitschrift zur Forschung über Aus-, Fort- und Weiterbildung pädagogischer Fachkräfte publication_identifier: issn: - 2699-2477 publication_status: published publisher: University Library J. C. Senckenberg status: public title: Zeit für Arbeitszufriedenheit? Eine quantitativ-empirische Studie zur Bedeutung fester Kooperationszeiten für die Arbeitszufriedenheit von Lehrkräften in inklusiven Schulen type: journal_article user_id: '95559' volume: 6 year: '2025' ... --- _id: '59872' abstract: - lang: eng text: Lightweight design is a driving concept in modern automotive engineering to minimize resource consumption over a vehicle's lifecycle through multi-material design, which relies on the use of joining techniques in car body fabrication. Multi-material design and the increasing trend towards producing large structural components using the megacasting process pose considerable challenges, particularly in the mechanical joining of aluminium-silicon (AlSi) castings. These castings typically exhibit low ductility and are prone to cracking when mechanically joined. Based on the excellent castability of hypoeutectic AlSi alloys, these are applied in sand casting and die casting as well as in megacasting. With a silicon content between 7 wt% and 12 wt%, these AlSi-alloys have a plate-like silicon phase that initiates cracks during mechanical joining. To enhance the joinability of castings, the research hypothesis is that improved solidification conditions enable a significant modification in the microstructure and therefore, increase the mechanical properties. During the manufacture of the castings using the sand casting process, the solidification conditions within the structural elements are varied to modify the microstructure to obtain castings with graded microstructure. The castings are evaluated using mechanical, microstructural and joining testing methods and finally, a microstructure-joinability correlation is established. article_number: '01081' article_type: original author: - first_name: Moritz full_name: Neuser, Moritz id: '32340' last_name: Neuser - first_name: Malte Christian full_name: Schlichter, Malte Christian id: '61977' last_name: Schlichter - first_name: Kay-Peter full_name: Hoyer, Kay-Peter id: '48411' last_name: Hoyer - first_name: Mathias full_name: Bobbert, Mathias id: '7850' last_name: Bobbert - first_name: Gerson full_name: Meschut, Gerson id: '32056' last_name: Meschut orcid: 0000-0002-2763-1246 - first_name: Mirko full_name: Schaper, Mirko id: '43720' last_name: Schaper citation: ama: Neuser M, Schlichter MC, Hoyer K-P, Bobbert M, Meschut G, Schaper M. Mechanical joinability of microstructurally graded structural components manufactured from hypoeutectic aluminium casting alloys. 44th Conference of the International Deep Drawing Research Group (IDDRG 2025). 2025;408. doi:10.1051/matecconf/202540801081 apa: Neuser, M., Schlichter, M. C., Hoyer, K.-P., Bobbert, M., Meschut, G., & Schaper, M. (2025). Mechanical joinability of microstructurally graded structural components manufactured from hypoeutectic aluminium casting alloys. 44th Conference of the International Deep Drawing Research Group (IDDRG 2025), 408, Article 01081. https://doi.org/10.1051/matecconf/202540801081 bibtex: '@article{Neuser_Schlichter_Hoyer_Bobbert_Meschut_Schaper_2025, title={Mechanical joinability of microstructurally graded structural components manufactured from hypoeutectic aluminium casting alloys}, volume={408}, DOI={10.1051/matecconf/202540801081}, number={01081}, journal={44th Conference of the International Deep Drawing Research Group (IDDRG 2025)}, author={Neuser, Moritz and Schlichter, Malte Christian and Hoyer, Kay-Peter and Bobbert, Mathias and Meschut, Gerson and Schaper, Mirko}, year={2025} }' chicago: Neuser, Moritz, Malte Christian Schlichter, Kay-Peter Hoyer, Mathias Bobbert, Gerson Meschut, and Mirko Schaper. “Mechanical Joinability of Microstructurally Graded Structural Components Manufactured from Hypoeutectic Aluminium Casting Alloys.” 44th Conference of the International Deep Drawing Research Group (IDDRG 2025) 408 (2025). https://doi.org/10.1051/matecconf/202540801081. ieee: 'M. Neuser, M. C. Schlichter, K.-P. Hoyer, M. Bobbert, G. Meschut, and M. Schaper, “Mechanical joinability of microstructurally graded structural components manufactured from hypoeutectic aluminium casting alloys,” 44th Conference of the International Deep Drawing Research Group (IDDRG 2025), vol. 408, Art. no. 01081, 2025, doi: 10.1051/matecconf/202540801081.' mla: Neuser, Moritz, et al. “Mechanical Joinability of Microstructurally Graded Structural Components Manufactured from Hypoeutectic Aluminium Casting Alloys.” 44th Conference of the International Deep Drawing Research Group (IDDRG 2025), vol. 408, 01081, 2025, doi:10.1051/matecconf/202540801081. short: M. Neuser, M.C. Schlichter, K.-P. Hoyer, M. Bobbert, G. Meschut, M. Schaper, 44th Conference of the International Deep Drawing Research Group (IDDRG 2025) 408 (2025). conference: end_date: 2025-06-05 location: Lissabon (Portugal) name: 44th Conference of the International Deep Drawing Research Group (IDDRG 2025) start_date: 2025-06-02 date_created: 2025-05-12T15:21:06Z date_updated: 2026-02-24T13:41:58Z department: - _id: '43' - _id: '158' - _id: '157' - _id: '9' - _id: '321' doi: 10.1051/matecconf/202540801081 intvolume: ' 408' keyword: - Joining - Casting - Self-pierce riveting - Aluminium casting alloy language: - iso: eng main_file_link: - open_access: '1' url: "\thttps://doi.org/10.1051/matecconf/202540801081" oa: '1' project: - _id: '131' name: 'TRR 285 - A: TRR 285 - Project Area A' - _id: '136' name: 'TRR 285 – A02: TRR 285 - Subproject A02' - _id: '135' name: 'TRR 285 – A01: TRR 285 - Subproject A01' - _id: '130' name: 'TRR 285: Methodenentwicklung zur mechanischen Fügbarkeit in wandlungsfähigen Prozessketten' publication: 44th Conference of the International Deep Drawing Research Group (IDDRG 2025) publication_status: published quality_controlled: '1' status: public title: Mechanical joinability of microstructurally graded structural components manufactured from hypoeutectic aluminium casting alloys type: journal_article user_id: '7850' volume: 408 year: '2025' ... --- _id: '62080' abstract: - lang: eng text: The failure behavior of fiber reinforced polymers (FRP) is strongly influenced by their microstructure, i.e. fiber arrangement or local fiber volume content. However, this information cannot be directly used for structural analyses, since it requires a discretization on micrometer level. Therefore, current failure theories do not directly account for such effects, but describe the behavior averaged over an entire specimen. This foundation in experimentally accessible loading conditions leads to purely theory based extension to more complex stress states without direct validation possibilities. This work aims at leveraging micro-scale simulations to obtain failure information under arbitrary loading conditions. The results are propagated to the meso-scale, enabling efficient structural analyses, by means of machine learning (ML). It is shown that the ML model is capable of correctly assessing previously unseen stress states and therefore poses an efficient tool of exploiting information from the micro-scale in larger simulations. author: - first_name: Johannes full_name: Gerritzen, Johannes id: '105344' last_name: Gerritzen orcid: 0000-0002-0169-8602 - first_name: Andreas full_name: Hornig, Andreas last_name: Hornig - first_name: Maik full_name: Gude, Maik last_name: Gude citation: ama: 'Gerritzen J, Hornig A, Gude M. Efficient failure information propagation under complex stress states in fiber reinforced polymers: From micro- to meso-scale using machine learning. In: Meschut G, Bobbert M, Duflou J, et al., eds. Sheet Metal 2025. Materials Research Proceedings. Materials Research Forum LLC, Materials Research Foundations; 2025:260–267. doi:10.21741/9781644903551-32' apa: 'Gerritzen, J., Hornig, A., & Gude, M. (2025). Efficient failure information propagation under complex stress states in fiber reinforced polymers: From micro- to meso-scale using machine learning. In G. Meschut, M. Bobbert, J. Duflou, L. Fratini, H. Hagenah, P. Martins, M. Merklein, & F. Micari (Eds.), Sheet Metal 2025 (pp. 260–267). Materials Research Forum LLC, Materials Research Foundations. https://doi.org/10.21741/9781644903551-32' bibtex: '@inproceedings{Gerritzen_Hornig_Gude_2025, series={Materials Research Proceedings}, title={Efficient failure information propagation under complex stress states in fiber reinforced polymers: From micro- to meso-scale using machine learning}, DOI={10.21741/9781644903551-32}, booktitle={Sheet Metal 2025}, publisher={Materials Research Forum LLC, Materials Research Foundations}, author={Gerritzen, Johannes and Hornig, Andreas and Gude, Maik}, editor={Meschut, G. and Bobbert, M. and Duflou, J. and Fratini, L. and Hagenah, H. and Martins, P. and Merklein, M. and Micari, F.}, year={2025}, pages={260–267}, collection={Materials Research Proceedings} }' chicago: 'Gerritzen, Johannes, Andreas Hornig, and Maik Gude. “Efficient Failure Information Propagation under Complex Stress States in Fiber Reinforced Polymers: From Micro- to Meso-Scale Using Machine Learning.” In Sheet Metal 2025, edited by G. Meschut, M. Bobbert, J. Duflou, L. Fratini, H. Hagenah, P. Martins, M. Merklein, and F. Micari, 260–267. Materials Research Proceedings. Materials Research Forum LLC, Materials Research Foundations, 2025. https://doi.org/10.21741/9781644903551-32.' ieee: 'J. Gerritzen, A. Hornig, and M. Gude, “Efficient failure information propagation under complex stress states in fiber reinforced polymers: From micro- to meso-scale using machine learning,” in Sheet Metal 2025, 2025, pp. 260–267, doi: 10.21741/9781644903551-32.' mla: 'Gerritzen, Johannes, et al. “Efficient Failure Information Propagation under Complex Stress States in Fiber Reinforced Polymers: From Micro- to Meso-Scale Using Machine Learning.” Sheet Metal 2025, edited by G. Meschut et al., Materials Research Forum LLC, Materials Research Foundations, 2025, pp. 260–267, doi:10.21741/9781644903551-32.' short: 'J. Gerritzen, A. Hornig, M. Gude, in: G. Meschut, M. Bobbert, J. Duflou, L. Fratini, H. Hagenah, P. Martins, M. Merklein, F. Micari (Eds.), Sheet Metal 2025, Materials Research Forum LLC, Materials Research Foundations, 2025, pp. 260–267.' date_created: 2025-11-04T12:48:37Z date_updated: 2026-02-27T06:43:37Z doi: 10.21741/9781644903551-32 editor: - first_name: G. full_name: Meschut, G. last_name: Meschut - first_name: M. full_name: Bobbert, M. last_name: Bobbert - first_name: J. full_name: Duflou, J. last_name: Duflou - first_name: L. full_name: Fratini, L. last_name: Fratini - first_name: H. full_name: Hagenah, H. last_name: Hagenah - first_name: P. full_name: Martins, P. last_name: Martins - first_name: M. full_name: Merklein, M. last_name: Merklein - first_name: F. full_name: Micari, F. last_name: Micari keyword: - Failure - Fiber Reinforced Plastic - Machine Learning language: - iso: eng page: 260–267 project: - _id: '130' name: 'TRR 285: Methodenentwicklung zur mechanischen Fügbarkeit in wandlungsfähigen Prozessketten' - _id: '137' name: TRR 285 - Subproject A03 - _id: '131' name: TRR 285 - Project Area A publication: Sheet Metal 2025 publication_identifier: isbn: - 978-1-64490-354-4 publisher: Materials Research Forum LLC, Materials Research Foundations series_title: Materials Research Proceedings status: public title: 'Efficient failure information propagation under complex stress states in fiber reinforced polymers: From micro- to meso-scale using machine learning' type: conference user_id: '105344' year: '2025' ... --- _id: '61149' abstract: - lang: eng text: The use of continuous fiber-reinforced thermoplastics (FRTP) in automotive industry increases due to their excellent material properties and possibility of rapid processing. The scale spanning heterogeneity of their material structure and its influence on the material behavior, however, presents significant challenges for most joining technologies, such as self-piercing riveting (SPR). During mechanical joining, the material structure is significantly altered within and around the joining zone, heavily influencing the material behavior. A comprehensive understanding of the underlying phenomena of material alteration during the SPR process is essential as basis for validating numerical simulations. This study examines the material structure at ten stages of a step-setting test of SPR with two FRTP sheets with glass-fiber reinforcement. Utilizing X-ray computed tomography (CT), the damage phenomena within different areas of the setting test are analyzed three-dimensionally and key parameters are quantified. Dominating phenomena during the penetration of the rivet into the laminate are fiber failure (FF), interfiber failure (IFF) and fiber bending, while delamination, fiber kinking and roving splitting are also observed. At the final stages, the bottom layers of the second sheet collapse and form a bulge into the cavity of the die. author: - first_name: Alrik full_name: Dargel, Alrik id: '114764' last_name: Dargel - first_name: Benjamin full_name: Gröger, Benjamin last_name: Gröger - first_name: Malte Christian full_name: Schlichter, Malte Christian id: '61977' last_name: Schlichter - first_name: Johannes full_name: Gerritzen, Johannes id: '105344' last_name: Gerritzen orcid: 0000-0002-0169-8602 - first_name: Daniel full_name: Köhler, Daniel id: '83408' last_name: Köhler - first_name: Gerson full_name: Meschut, Gerson id: '32056' last_name: Meschut orcid: 0000-0002-2763-1246 - first_name: Maik full_name: Gude, Maik last_name: Gude - first_name: Robert full_name: Kupfer, Robert last_name: Kupfer citation: ama: 'Dargel A, Gröger B, Schlichter MC, et al. LOCAL DEFORMATION AND FAILURE OF COMPOSITES DURING SELF-PIERCING RIVETING: A CT BASED MICROSTRUCTURE INVESTIGATION. In: Gomes JFS, Meguid SA, eds. Proceedings of the 8th International Conference on Integrity-Reliability-Failure (IRF2025). FEUP; 2025. doi:10.24840/978-972-752-323-8' apa: 'Dargel, A., Gröger, B., Schlichter, M. C., Gerritzen, J., Köhler, D., Meschut, G., Gude, M., & Kupfer, R. (2025). LOCAL DEFORMATION AND FAILURE OF COMPOSITES DURING SELF-PIERCING RIVETING: A CT BASED MICROSTRUCTURE INVESTIGATION. In J. F. S. Gomes & S. A. Meguid (Eds.), Proceedings of the 8th International Conference on Integrity-Reliability-Failure (IRF2025). FEUP. https://doi.org/10.24840/978-972-752-323-8' bibtex: '@inproceedings{Dargel_Gröger_Schlichter_Gerritzen_Köhler_Meschut_Gude_Kupfer_2025, place={Porto}, title={LOCAL DEFORMATION AND FAILURE OF COMPOSITES DURING SELF-PIERCING RIVETING: A CT BASED MICROSTRUCTURE INVESTIGATION}, DOI={10.24840/978-972-752-323-8}, booktitle={Proceedings of the 8th International Conference on Integrity-Reliability-Failure (IRF2025)}, publisher={FEUP}, author={Dargel, Alrik and Gröger, Benjamin and Schlichter, Malte Christian and Gerritzen, Johannes and Köhler, Daniel and Meschut, Gerson and Gude, Maik and Kupfer, Robert}, editor={Gomes, J.F. Silva and Meguid, Shaker A.}, year={2025} }' chicago: 'Dargel, Alrik, Benjamin Gröger, Malte Christian Schlichter, Johannes Gerritzen, Daniel Köhler, Gerson Meschut, Maik Gude, and Robert Kupfer. “LOCAL DEFORMATION AND FAILURE OF COMPOSITES DURING SELF-PIERCING RIVETING: A CT BASED MICROSTRUCTURE INVESTIGATION.” In Proceedings of the 8th International Conference on Integrity-Reliability-Failure (IRF2025), edited by J.F. Silva Gomes and Shaker A. Meguid. Porto: FEUP, 2025. https://doi.org/10.24840/978-972-752-323-8.' ieee: 'A. Dargel et al., “LOCAL DEFORMATION AND FAILURE OF COMPOSITES DURING SELF-PIERCING RIVETING: A CT BASED MICROSTRUCTURE INVESTIGATION,” in Proceedings of the 8th International Conference on Integrity-Reliability-Failure (IRF2025), Porto, 2025, doi: 10.24840/978-972-752-323-8.' mla: 'Dargel, Alrik, et al. “LOCAL DEFORMATION AND FAILURE OF COMPOSITES DURING SELF-PIERCING RIVETING: A CT BASED MICROSTRUCTURE INVESTIGATION.” Proceedings of the 8th International Conference on Integrity-Reliability-Failure (IRF2025), edited by J.F. Silva Gomes and Shaker A. Meguid, FEUP, 2025, doi:10.24840/978-972-752-323-8.' short: 'A. Dargel, B. Gröger, M.C. Schlichter, J. Gerritzen, D. Köhler, G. Meschut, M. Gude, R. Kupfer, in: J.F.S. Gomes, S.A. Meguid (Eds.), Proceedings of the 8th International Conference on Integrity-Reliability-Failure (IRF2025), FEUP, Porto, 2025.' conference: end_date: 2025-07-18 location: Porto name: 8th International Conference on Integrity-Reliability-Failure (IRF2025) start_date: 2025-07-15 date_created: 2025-09-08T11:52:45Z date_updated: 2026-02-27T06:45:17Z doi: 10.24840/978-972-752-323-8 editor: - first_name: J.F. Silva full_name: Gomes, J.F. Silva last_name: Gomes - first_name: Shaker A. full_name: Meguid, Shaker A. last_name: Meguid keyword: - self-piercing riveting - computed tomography - thermoplastic composites - process-structure-interaction language: - iso: eng main_file_link: - open_access: '1' url: https://www.researchgate.net/publication/395593556_LOCAL_DEFORMATION_AND_FAILURE_OF_COMPOSITES_DURING_SELF-PIERCING_RIVETING_A_CT_BASED_MICROSTRUCTURE_INVESTIGATION oa: '1' place: Porto project: - _id: '133' name: TRR 285 - Project Area C - _id: '148' name: TRR 285 - Subproject C04 - _id: '130' name: 'TRR 285: Methodenentwicklung zur mechanischen Fügbarkeit in wandlungsfähigen Prozessketten' - _id: '131' name: TRR 285 - Project Area A - _id: '137' name: TRR 285 - Subproject A03 - _id: '135' name: TRR 285 - Subproject A01 publication: Proceedings of the 8th International Conference on Integrity-Reliability-Failure (IRF2025) publication_identifier: isbn: - '9789727523238' publication_status: published publisher: FEUP status: public title: 'LOCAL DEFORMATION AND FAILURE OF COMPOSITES DURING SELF-PIERCING RIVETING: A CT BASED MICROSTRUCTURE INVESTIGATION' type: conference user_id: '105344' year: '2025' ... --- _id: '58807' abstract: - lang: eng text: "One of the most important strategies for reducing CO2 emissions in the mobility sector is lightweight construction. In particular, the car body offers several opportunities for weight reduction. Multi-material designs are increasingly being applied to select the most suitable material for the respective load and ultimately achieve synergy effects. For example, aluminium castings are used at the nodes of a spaceframe body. Subsequently, these are joined with profiles to form the bodyshell. To join different materials mechanical joining techniques, such as semi-tubular self-piercing riveting, are deployed. According to the current state of the art, cracks occur in the aluminium castings during the mechanical joining process as a result of the high degree of deformation. Although the aluminium casting alloys of the AlSi-system exhibit low ductility, these alloys reveal excellent castability. In particular, the ability to cast thin structural parts is enabled by the low liquidus point of the near eutectic aluminium casting alloys.\r\nThis study addresses the mechanical joining properties of the near eutectic aluminium casting alloy AlSi12, depending on different microstructures. These are achieved by annealing processes and modifying agents. Through an adapted heat treatment, the previously lamellar morphology can be transformed into a globular morphology, which leads to increased ductility and prevents the formation of cracks during the self-piercing riveting (SPR). The joinability is investigated using different die geometries, whereas the joint formation is analysed regarding crack initiation. To evaluate the increased ductility, microstructural and mechanical tests are performed and finally, a microstructure-joinability correlation is established." article_type: original author: - first_name: Moritz full_name: Neuser, Moritz id: '32340' last_name: Neuser - first_name: Pia Katharina full_name: Holtkamp, Pia Katharina id: '44935' last_name: Holtkamp - first_name: Kay-Peter full_name: Hoyer, Kay-Peter id: '48411' last_name: Hoyer - first_name: Fabian full_name: Kappe, Fabian id: '66459' last_name: Kappe - first_name: Safak full_name: Yildiz, Safak last_name: Yildiz - first_name: Mathias full_name: Bobbert, Mathias id: '7850' last_name: Bobbert - first_name: Gerson full_name: Meschut, Gerson id: '32056' last_name: Meschut orcid: 0000-0002-2763-1246 - first_name: Mirko full_name: Schaper, Mirko id: '43720' last_name: Schaper citation: ama: 'Neuser M, Holtkamp PK, Hoyer K-P, et al. Mechanical properties and joinability of the near-eutectic aluminium casting alloy AlSi12. The Journal of Materials: Design and Applications, Part L. Published online 2025. doi:10.1177/14644207251319922' apa: 'Neuser, M., Holtkamp, P. K., Hoyer, K.-P., Kappe, F., Yildiz, S., Bobbert, M., Meschut, G., & Schaper, M. (2025). Mechanical properties and joinability of the near-eutectic aluminium casting alloy AlSi12. The Journal of Materials: Design and Applications, Part L. 5th International Conference on Materials Design and Applications 2024, Porto, Portugal. https://doi.org/10.1177/14644207251319922' bibtex: '@article{Neuser_Holtkamp_Hoyer_Kappe_Yildiz_Bobbert_Meschut_Schaper_2025, title={Mechanical properties and joinability of the near-eutectic aluminium casting alloy AlSi12}, DOI={10.1177/14644207251319922}, journal={The Journal of Materials: Design and Applications, Part L}, publisher={Sage Publications}, author={Neuser, Moritz and Holtkamp, Pia Katharina and Hoyer, Kay-Peter and Kappe, Fabian and Yildiz, Safak and Bobbert, Mathias and Meschut, Gerson and Schaper, Mirko}, year={2025} }' chicago: 'Neuser, Moritz, Pia Katharina Holtkamp, Kay-Peter Hoyer, Fabian Kappe, Safak Yildiz, Mathias Bobbert, Gerson Meschut, and Mirko Schaper. “Mechanical Properties and Joinability of the Near-Eutectic Aluminium Casting Alloy AlSi12.” The Journal of Materials: Design and Applications, Part L, 2025. https://doi.org/10.1177/14644207251319922.' ieee: 'M. Neuser et al., “Mechanical properties and joinability of the near-eutectic aluminium casting alloy AlSi12,” The Journal of Materials: Design and Applications, Part L, 2025, doi: 10.1177/14644207251319922.' mla: 'Neuser, Moritz, et al. “Mechanical Properties and Joinability of the Near-Eutectic Aluminium Casting Alloy AlSi12.” The Journal of Materials: Design and Applications, Part L, Sage Publications, 2025, doi:10.1177/14644207251319922.' short: 'M. Neuser, P.K. Holtkamp, K.-P. Hoyer, F. Kappe, S. Yildiz, M. Bobbert, G. Meschut, M. Schaper, The Journal of Materials: Design and Applications, Part L (2025).' conference: end_date: 2024-07-05 location: Porto, Portugal name: 5th International Conference on Materials Design and Applications 2024 start_date: 2024-07-04 date_created: 2025-02-24T10:25:31Z date_updated: 2025-02-24T12:25:04Z department: - _id: '43' - _id: '158' - _id: '157' - _id: '9' - _id: '321' doi: 10.1177/14644207251319922 has_accepted_license: '1' keyword: - aluminium - casting - microstructure - joinability - self-piercing riveting language: - iso: eng project: - _id: '131' name: 'TRR 285 - A: TRR 285 - Project Area A' - _id: '136' name: 'TRR 285 – A02: TRR 285 - Subproject A02' - _id: '133' name: 'TRR 285 - C: TRR 285 - Project Area C' - _id: '146' name: 'TRR 285 – C02: TRR 285 - Subproject C02' publication: 'The Journal of Materials: Design and Applications, Part L' publication_status: published publisher: Sage Publications quality_controlled: '1' status: public title: Mechanical properties and joinability of the near-eutectic aluminium casting alloy AlSi12 type: journal_article user_id: '32340' year: '2025' ... --- _id: '58885' abstract: - lang: eng text: 'There have been several attempts to conceptualize and operationalize pedagogical content knowledge (PCK) in the context of teachers'' professional competencies. A recent and popular model is the Refined Consensus Model (RCM), which proposes a framework of dispositional competencies (personal PCK—pPCK) that influence more action-related competencies (enacted PCK—ePCK) and vice versa. However, descriptions of the internal structure of pPCK and possible knowledge domains that might develop independently are still limited, being either primarily theoretically motivated or strictly hierarchical and therefore of limited use, for example, for formative feedback and further development of the RCM. Meanwhile, a non-hierarchical differentiation for the ePCK regarding the plan-teach-reflect cycle has emerged. In this study, we present an exploratory computational approach to investigate pre-service teachers'' pPCK for a similar non-hierarchical structure using a large dataset of responses to a pPCK questionnaire (N=846). We drew on theoretical foundations and previous empirical findings to achieve interpretability by integrating this external knowledge into our analyses using the Computational Grounded Theory (CGT) framework. The results of a cluster analysis of the pPCK scores indicate the emergence of prototypical groups, which we refer to as competency profiles: (1) a group with low performance, (2) a group with relatively advanced competency in using pPCK to create instructional elements, (3) a group with relatively advanced competency in using pPCK to assess and analyze described instructional elements, and (4) a group with high performance. These groups show tendencies for certain language usage, which we analyze using a structural topic model in a CGT-inspired pattern refinement step. We verify these patterns by demonstrating the ability of a machine learning model to predict the competency profile assignments. Finally, we discuss some implications of the results for the further development of the RCM and their potential usability for an automated formative assessment.' article_type: original author: - first_name: Jannis full_name: Zeller, Jannis id: '99022' last_name: Zeller orcid: 0000-0002-1834-5520 - first_name: Josef full_name: Riese, Josef id: '429' last_name: Riese orcid: 0000-0003-2927-2619 citation: ama: 'Zeller J, Riese J. Competency Profiles of PCK Using Unsupervised Learning: What Implications for the Structures of pPCK Emerge From Non-Hierarchical Analyses? Journal of Research in Science Teaching. Published online 2025. doi:10.1002/tea.70001' apa: 'Zeller, J., & Riese, J. (2025). Competency Profiles of PCK Using Unsupervised Learning: What Implications for the Structures of pPCK Emerge From Non-Hierarchical Analyses? Journal of Research in Science Teaching. https://doi.org/10.1002/tea.70001' bibtex: '@article{Zeller_Riese_2025, title={Competency Profiles of PCK Using Unsupervised Learning: What Implications for the Structures of pPCK Emerge From Non-Hierarchical Analyses?}, DOI={10.1002/tea.70001}, journal={Journal of Research in Science Teaching}, author={Zeller, Jannis and Riese, Josef}, year={2025} }' chicago: 'Zeller, Jannis, and Josef Riese. “Competency Profiles of PCK Using Unsupervised Learning: What Implications for the Structures of PPCK Emerge From Non-Hierarchical Analyses?” Journal of Research in Science Teaching, 2025. https://doi.org/10.1002/tea.70001.' ieee: 'J. Zeller and J. Riese, “Competency Profiles of PCK Using Unsupervised Learning: What Implications for the Structures of pPCK Emerge From Non-Hierarchical Analyses?,” Journal of Research in Science Teaching, 2025, doi: 10.1002/tea.70001.' mla: 'Zeller, Jannis, and Josef Riese. “Competency Profiles of PCK Using Unsupervised Learning: What Implications for the Structures of PPCK Emerge From Non-Hierarchical Analyses?” Journal of Research in Science Teaching, 2025, doi:10.1002/tea.70001.' short: J. Zeller, J. Riese, Journal of Research in Science Teaching (2025). date_created: 2025-03-04T08:08:37Z date_updated: 2025-03-04T08:08:42Z department: - _id: '299' doi: 10.1002/tea.70001 keyword: - computational grounded theory - language analysis - machine learning - pedagogical content knowledge - unsupervised learning language: - iso: eng main_file_link: - open_access: '1' url: https://onlinelibrary.wiley.com/doi/epdf/10.1002/tea.70001 oa: '1' publication: Journal of Research in Science Teaching publication_identifier: eissn: - 1098-2736 issn: - 0022-4308 publication_status: published status: public title: 'Competency Profiles of PCK Using Unsupervised Learning: What Implications for the Structures of pPCK Emerge From Non-Hierarchical Analyses?' type: journal_article user_id: '99022' year: '2025' ... --- _id: '60290' abstract: - lang: eng text: The constantly increasing demand for climate protection and resource conservation requires innovative and versatile joining processes that improve adaptability to the joining task and robustness to enable flexible manufacturing on a production line. Therefore, the versatile SPR (V-SPR) and tumbling SPR (T-SPR) were developed. Using the example of a mixed material combination HCT590X+Z (t0 = 1.0 mm) / EN AW-6014 T4 (t0 = 2.0 mm), these processes were examined and compared with regard to the binding mechanisms form closure and force closure using micrographs, non-destructive resistance measurements and destructive torsion tests. For this purpose, a new sample geometry was defined, and the methods were adapted to the SPR process variants. author: - first_name: Stephan full_name: Lüder, Stephan last_name: Lüder - first_name: Pia Katharina full_name: Holtkamp, Pia Katharina id: '44935' last_name: Holtkamp - first_name: Simon full_name: Wituschek, Simon last_name: Wituschek - first_name: Mathias full_name: Bobbert, Mathias id: '7850' last_name: Bobbert - first_name: Gerson full_name: Meschut, Gerson id: '32056' last_name: Meschut orcid: 0000-0002-2763-1246 - first_name: Michael full_name: Lechner, Michael last_name: Lechner - first_name: Hans Christian full_name: Schmale, Hans Christian last_name: Schmale citation: ama: 'Lüder S, Holtkamp PK, Wituschek S, et al. Analysis of the binding mechanisms depending on versatile process variants of self-piercing riveting. In: Meschut G, Bobbert M, Duflou J, et al., eds. Materials Research Proceedings. Vol 52. Sheet Metal 2025. Materials Research Forum LLC; 2025:101-108. doi:10.21741/9781644903551-13' apa: Lüder, S., Holtkamp, P. K., Wituschek, S., Bobbert, M., Meschut, G., Lechner, M., & Schmale, H. C. (2025). Analysis of the binding mechanisms depending on versatile process variants of self-piercing riveting. In G. Meschut, M. Bobbert, J. Duflou, L. Fratini, H. Hagenah, P. A. F. Martins, M. Merklein, & F. Micari (Eds.), Materials Research Proceedings (Vol. 52, pp. 101–108). Materials Research Forum LLC. https://doi.org/10.21741/9781644903551-13 bibtex: '@inproceedings{Lüder_Holtkamp_Wituschek_Bobbert_Meschut_Lechner_Schmale_2025, place={Millersville}, series={Sheet Metal 2025}, title={Analysis of the binding mechanisms depending on versatile process variants of self-piercing riveting}, volume={52}, DOI={10.21741/9781644903551-13}, booktitle={Materials Research Proceedings}, publisher={Materials Research Forum LLC}, author={Lüder, Stephan and Holtkamp, Pia Katharina and Wituschek, Simon and Bobbert, Mathias and Meschut, Gerson and Lechner, Michael and Schmale, Hans Christian}, editor={Meschut, Gerson and Bobbert, Mathias and Duflou, Joost and Fratini, Livan and Hagenah, Hinnerk and Martins, Paulo A. F. and Merklein, Marion and Micari, Fabrizio}, year={2025}, pages={101–108}, collection={Sheet Metal 2025} }' chicago: 'Lüder, Stephan, Pia Katharina Holtkamp, Simon Wituschek, Mathias Bobbert, Gerson Meschut, Michael Lechner, and Hans Christian Schmale. “Analysis of the Binding Mechanisms Depending on Versatile Process Variants of Self-Piercing Riveting.” In Materials Research Proceedings, edited by Gerson Meschut, Mathias Bobbert, Joost Duflou, Livan Fratini, Hinnerk Hagenah, Paulo A. F. Martins, Marion Merklein, and Fabrizio Micari, 52:101–8. Sheet Metal 2025. Millersville: Materials Research Forum LLC, 2025. https://doi.org/10.21741/9781644903551-13.' ieee: 'S. Lüder et al., “Analysis of the binding mechanisms depending on versatile process variants of self-piercing riveting,” in Materials Research Proceedings, Paderborn, 2025, vol. 52, pp. 101–108, doi: 10.21741/9781644903551-13.' mla: Lüder, Stephan, et al. “Analysis of the Binding Mechanisms Depending on Versatile Process Variants of Self-Piercing Riveting.” Materials Research Proceedings, edited by Gerson Meschut et al., vol. 52, Materials Research Forum LLC, 2025, pp. 101–08, doi:10.21741/9781644903551-13. short: 'S. Lüder, P.K. Holtkamp, S. Wituschek, M. Bobbert, G. Meschut, M. Lechner, H.C. Schmale, in: G. Meschut, M. Bobbert, J. Duflou, L. Fratini, H. Hagenah, P.A.F. Martins, M. Merklein, F. Micari (Eds.), Materials Research Proceedings, Materials Research Forum LLC, Millersville, 2025, pp. 101–108.' conference: end_date: 2025-04-03 location: Paderborn name: 21st International Conference on Sheet Metal start_date: 2025-04-01 date_created: 2025-06-20T10:13:22Z date_updated: 2025-06-27T08:19:26Z department: - _id: '630' - _id: '43' - _id: '157' doi: 10.21741/9781644903551-13 editor: - first_name: Gerson full_name: Meschut, Gerson last_name: Meschut - first_name: Mathias full_name: Bobbert, Mathias last_name: Bobbert - first_name: Joost full_name: Duflou, Joost last_name: Duflou - first_name: Livan full_name: Fratini, Livan last_name: Fratini - first_name: Hinnerk full_name: Hagenah, Hinnerk last_name: Hagenah - first_name: Paulo A. F. full_name: Martins, Paulo A. F. last_name: Martins - first_name: Marion full_name: Merklein, Marion last_name: Merklein - first_name: Fabrizio full_name: Micari, Fabrizio last_name: Micari extern: '1' intvolume: ' 52' keyword: - Joining - Self-Piercing Riveting - Sheet Metal language: - iso: eng page: 101 - 108 place: Millersville project: - _id: '131' name: 'TRR 285 - A: TRR 285 - Project Area A' - _id: '138' name: 'TRR 285 – A04: TRR 285 - Subproject A04' - _id: '133' name: 'TRR 285 - C: TRR 285 - Project Area C' - _id: '146' name: 'TRR 285 – C02: TRR 285 - Subproject C02' publication: Materials Research Proceedings publication_identifier: issn: - 2474-395X publication_status: published publisher: Materials Research Forum LLC quality_controlled: '1' series_title: Sheet Metal 2025 status: public title: Analysis of the binding mechanisms depending on versatile process variants of self-piercing riveting type: conference user_id: '44935' volume: 52 year: '2025' ... --- _id: '60680' abstract: - lang: eng text: "Classical machine learning techniques often struggle with overfitting and unreliable predictions when exposed to novel conditions. Introducing causality into the modelling process offers a promising way to mitigate these challenges by enhancing predictive robustness. However, constructing an initial causal graph manually using domain knowledge is time-consuming, particularly in complex time series with numerous variables. To address this, causal discovery algorithms can provide a preliminary causal structure that domain experts can refine. This study investigates causal feature selection with domain knowledge using a data center system as an example. We use simulated time-series data to compare \r\ndifferent causal feature selection with traditional machine-learning feature selection methods. Our results show that predictions based on causal features are more robust compared to those derived from traditional methods. These findings underscore the potential of combining causal discovery algorithms with human expertise to improve machine learning applications." author: - first_name: David Ricardo full_name: Zapata Gonzalez, David Ricardo id: '105506' last_name: Zapata Gonzalez - first_name: Marcel full_name: Meyer, Marcel id: '105120' last_name: Meyer - first_name: Oliver full_name: Müller, Oliver id: '72849' last_name: Müller citation: ama: 'Zapata Gonzalez DR, Meyer M, Müller O. Bridging the gap between data-driven and theory-driven modelling – leveraging causal machine learning for integrative modelling of dynamical systems. In: ; 2025.' apa: Zapata Gonzalez, D. R., Meyer, M., & Müller, O. (2025). Bridging the gap between data-driven and theory-driven modelling – leveraging causal machine learning for integrative modelling of dynamical systems. European Conference on Information Systems, Amman, Jordan. bibtex: '@inproceedings{Zapata Gonzalez_Meyer_Müller_2025, title={Bridging the gap between data-driven and theory-driven modelling – leveraging causal machine learning for integrative modelling of dynamical systems}, author={Zapata Gonzalez, David Ricardo and Meyer, Marcel and Müller, Oliver}, year={2025} }' chicago: Zapata Gonzalez, David Ricardo, Marcel Meyer, and Oliver Müller. “Bridging the Gap between Data-Driven and Theory-Driven Modelling – Leveraging Causal Machine Learning for Integrative Modelling of Dynamical Systems,” 2025. ieee: D. R. Zapata Gonzalez, M. Meyer, and O. Müller, “Bridging the gap between data-driven and theory-driven modelling – leveraging causal machine learning for integrative modelling of dynamical systems,” presented at the European Conference on Information Systems, Amman, Jordan, 2025. mla: Zapata Gonzalez, David Ricardo, et al. Bridging the Gap between Data-Driven and Theory-Driven Modelling – Leveraging Causal Machine Learning for Integrative Modelling of Dynamical Systems. 2025. short: 'D.R. Zapata Gonzalez, M. Meyer, O. Müller, in: 2025.' conference: end_date: 18.06.2025 location: Amman, Jordan name: European Conference on Information Systems start_date: 16.06.2025 date_created: 2025-07-21T07:52:03Z date_updated: 2025-07-22T06:30:37Z department: - _id: '196' keyword: - Causal Machine Learning - Causality in Time Series - Causal Discovery - Human-Machine Collaboration language: - iso: eng main_file_link: - url: https://aisel.aisnet.org/ecis2025/bus_analytics/bus_analytics/2/ status: public title: Bridging the gap between data-driven and theory-driven modelling – leveraging causal machine learning for integrative modelling of dynamical systems type: conference user_id: '72849' year: '2025' ... --- _id: '55400' abstract: - lang: eng text: "This study contributes to the evolving field of robot learning in interaction\r\nwith humans, examining the impact of diverse input modalities on learning\r\noutcomes. It introduces the concept of \"meta-modalities\" which encapsulate\r\nadditional forms of feedback beyond the traditional preference and scalar\r\nfeedback mechanisms. Unlike prior research that focused on individual\r\nmeta-modalities, this work evaluates their combined effect on learning\r\noutcomes. Through a study with human participants, we explore user preferences\r\nfor these modalities and their impact on robot learning performance. Our\r\nfindings reveal that while individual modalities are perceived differently,\r\ntheir combination significantly improves learning behavior and usability. This\r\nresearch not only provides valuable insights into the optimization of\r\nhuman-robot interactive task learning but also opens new avenues for enhancing\r\nthe interactive freedom and scaffolding capabilities provided to users in such\r\nsettings." article_type: original author: - first_name: Helen full_name: Beierling, Helen last_name: Beierling - first_name: 'Robin ' full_name: 'Beierling, Robin ' last_name: Beierling - first_name: Anna-Lisa full_name: Vollmer, Anna-Lisa last_name: Vollmer citation: ama: Beierling H, Beierling R, Vollmer A-L. The power of combined modalities in interactive robot learning. Frontiers in Robotics and AI. 2025;12. apa: Beierling, H., Beierling, R., & Vollmer, A.-L. (2025). The power of combined modalities in interactive robot learning. Frontiers in Robotics and AI, 12. bibtex: '@article{Beierling_Beierling_Vollmer_2025, title={The power of combined modalities in interactive robot learning}, volume={12}, journal={Frontiers in Robotics and AI}, publisher={Frontiers }, author={Beierling, Helen and Beierling, Robin and Vollmer, Anna-Lisa}, year={2025} }' chicago: Beierling, Helen, Robin Beierling, and Anna-Lisa Vollmer. “The Power of Combined Modalities in Interactive Robot Learning.” Frontiers in Robotics and AI 12 (2025). ieee: H. Beierling, R. Beierling, and A.-L. Vollmer, “The power of combined modalities in interactive robot learning,” Frontiers in Robotics and AI, vol. 12, 2025. mla: Beierling, Helen, et al. “The Power of Combined Modalities in Interactive Robot Learning.” Frontiers in Robotics and AI, vol. 12, Frontiers , 2025. short: H. Beierling, R. Beierling, A.-L. Vollmer, Frontiers in Robotics and AI 12 (2025). date_created: 2024-07-26T08:35:24Z date_updated: 2025-09-17T13:38:18Z ddc: - '004' extern: '1' file: - access_level: closed content_type: application/pdf creator: helebeen date_created: 2025-09-17T13:36:09Z date_updated: 2025-09-17T13:36:09Z file_id: '61331' file_name: frobt-12-1598968.pdf file_size: 36978223 relation: main_file success: 1 file_date_updated: 2025-09-17T13:36:09Z funded_apc: '1' has_accepted_license: '1' intvolume: ' 12' keyword: - human-robot interaction - human-in-the-loop learning - reinforcement learning - interactive robot learning - multi-modal feedback - learning from demonstration - preference-based learning - scaffolding in robot learning language: - iso: eng main_file_link: - open_access: '1' url: https://pmc.ncbi.nlm.nih.gov/articles/PMC12312635/ oa: '1' project: - _id: '123' name: 'TRR 318 - B5: TRR 318 - Subproject B5' publication: Frontiers in Robotics and AI publication_status: published publisher: 'Frontiers ' status: public title: The power of combined modalities in interactive robot learning type: journal_article user_id: '50995' volume: 12 year: '2025' ... --- _id: '61327' abstract: - lang: eng text: "Robot learning from humans has been proposed and researched for several decades as a means to enable robots to learn new skills or\r\nadapt existing ones to new situations. Recent advances in artificial intelligence, including learning approaches like reinforcement\r\nlearning and architectures like transformers and foundation models, combined with access to massive datasets, has created attractive\r\nopportunities to apply those data-hungry techniques to this problem. We argue that the focus on massive amounts of pre-collected\r\ndata, and the resulting learning paradigm, where humans demonstrate and robots learn in isolation, is overshadowing a specialized\r\narea of work we term Human-Interactive-Robot-Learning (HIRL). This paradigm, wherein robots and humans interact during the\r\nlearning process, is at the intersection of multiple fields (artificial intelligence, robotics, human-computer interaction, design and others)\r\nand holds unique promise. Using HIRL, robots can achieve greater sample efficiency (as humans can provide task knowledge through\r\ninteraction), align with human preferences (as humans can guide the robot behavior towards their expectations), and explore more\r\nmeaningfully and safely (as humans can utilize domain knowledge to guide learning and prevent catastrophic failures). This can result\r\nin robotic systems that can more quickly and easily adapt to new tasks in human environments. The objective of this paper is to\r\nprovide a broad and consistent overview of HIRL research and to guide researchers toward understanding the scope of HIRL, and\r\ncurrent open or underexplored challenges related to four themes — namely, human, robot learning, interaction, and broader context.\r\nThe paper includes concrete use cases to illustrate the interaction between these challenges and inspire further research according to\r\nbroad recommendations and a call for action for the growing HIRL community" article_type: original author: - first_name: 'Kim ' full_name: 'Baraka, Kim ' last_name: Baraka - first_name: Ifrah full_name: Idrees, Ifrah last_name: Idrees - first_name: Taylor Kessler full_name: Faulkner, Taylor Kessler last_name: Faulkner - first_name: Erdem full_name: Biyik, Erdem last_name: Biyik - first_name: Serena full_name: Booth, Serena last_name: Booth - first_name: Mohamed full_name: Chetouani, Mohamed last_name: Chetouani - first_name: Daniel H. full_name: Grollman, Daniel H. last_name: Grollman - first_name: Akanksha full_name: Saran, Akanksha last_name: Saran - first_name: Emmanuel full_name: Senft, Emmanuel last_name: Senft - first_name: Silvia full_name: Tulli, Silvia last_name: Tulli - first_name: Anna-Lisa full_name: Vollmer, Anna-Lisa last_name: Vollmer - first_name: Antonio full_name: Andriella, Antonio last_name: Andriella - first_name: Helen full_name: Beierling, Helen last_name: Beierling - first_name: Tiffany full_name: Horter, Tiffany last_name: Horter - first_name: Jens full_name: Kober, Jens last_name: Kober - first_name: Isaac full_name: Sheidlower, Isaac last_name: Sheidlower - first_name: Matthew E. full_name: Taylor, Matthew E. last_name: Taylor - first_name: Sanne full_name: van Waveren, Sanne last_name: van Waveren - first_name: Xuesu full_name: Xiao, Xuesu last_name: Xiao citation: ama: 'Baraka K, Idrees I, Faulkner TK, et al. Human-Interactive Robot Learning: Definition, Challenges, and Recommendations. Transactions on Human-Robot Interaction.' apa: 'Baraka, K., Idrees, I., Faulkner, T. K., Biyik, E., Booth, S., Chetouani, M., Grollman, D. H., Saran, A., Senft, E., Tulli, S., Vollmer, A.-L., Andriella, A., Beierling, H., Horter, T., Kober, J., Sheidlower, I., Taylor, M. E., van Waveren, S., & Xiao, X. (n.d.). Human-Interactive Robot Learning: Definition, Challenges, and Recommendations. Transactions on Human-Robot Interaction.' bibtex: '@article{Baraka_Idrees_Faulkner_Biyik_Booth_Chetouani_Grollman_Saran_Senft_Tulli_et al., title={Human-Interactive Robot Learning: Definition, Challenges, and Recommendations}, journal={Transactions on Human-Robot Interaction}, author={Baraka, Kim and Idrees, Ifrah and Faulkner, Taylor Kessler and Biyik, Erdem and Booth, Serena and Chetouani, Mohamed and Grollman, Daniel H. and Saran, Akanksha and Senft, Emmanuel and Tulli, Silvia and et al.} }' chicago: 'Baraka, Kim , Ifrah Idrees, Taylor Kessler Faulkner, Erdem Biyik, Serena Booth, Mohamed Chetouani, Daniel H. Grollman, et al. “Human-Interactive Robot Learning: Definition, Challenges, and Recommendations.” Transactions on Human-Robot Interaction, n.d.' ieee: 'K. Baraka et al., “Human-Interactive Robot Learning: Definition, Challenges, and Recommendations,” Transactions on Human-Robot Interaction.' mla: 'Baraka, Kim, et al. “Human-Interactive Robot Learning: Definition, Challenges, and Recommendations.” Transactions on Human-Robot Interaction.' short: K. Baraka, I. Idrees, T.K. Faulkner, E. Biyik, S. Booth, M. Chetouani, D.H. Grollman, A. Saran, E. Senft, S. Tulli, A.-L. Vollmer, A. Andriella, H. Beierling, T. Horter, J. Kober, I. Sheidlower, M.E. Taylor, S. van Waveren, X. Xiao, Transactions on Human-Robot Interaction (n.d.). date_created: 2025-09-17T12:42:45Z date_updated: 2025-09-17T13:40:16Z keyword: - Robot learning - Interactive learning systems - Human-robot interaction - Human-in-the-loop machine learning - Teaching and learning language: - iso: eng project: - _id: '123' name: TRR 318 - Subproject B5 publication: Transactions on Human-Robot Interaction publication_status: submitted status: public title: 'Human-Interactive Robot Learning: Definition, Challenges, and Recommendations' type: journal_article user_id: '50995' year: '2025' ... --- _id: '62701' abstract: - lang: eng text: 'Learning continuous vector representations for knowledge graphs has significantly improved state-of-the-art performances in many challenging tasks. Yet, deep-learning-based models are only post-hoc and locally explainable. In contrast, learning Web Ontology Language (OWL) class expressions in Description Logics (DLs) is ante-hoc and globally explainable. However, state-of-the-art learners have two well-known lim-itations: scaling to large knowledge graphs and handling missing infor-mation. Here, we present a decision-tree-based learner (tDL) to learn Web Ontology Languages (OWLs) class expressions over large knowl-edge graphs, while imputing missing triples. Given positive and negative example individuals, tDL firstly constructs unique OWL expressions in .SHOIN from concise bounded descriptions of individuals. Each OWL class expression is used as a feature in a binary classification problem to represent input individuals. Thereafter, tDL fits a CART decision tree to learn Boolean decision rules distinguishing positive examples from nega-tive examples. A final OWL expression in.SHOIN is built by traversing the built CART decision tree from the root node to leaf nodes for each positive example. By this, tDL can learn OWL class expressions without exploration, i.e., the number of queries to a knowledge graph is bounded by the number of input individuals. Our empirical results show that tDL outperforms the current state-of-the-art models across datasets. Impor-tantly, our experiments over a large knowledge graph (DBpedia with 1.1 billion triples) show that tDL can effectively learn accurate OWL class expressions, while the state-of-the-art models fail to return any results. Finally, expressions learned by tDL can be seamlessly translated into natural language explanations using a pre-trained large language model and a DL verbalizer.' author: - first_name: Caglar full_name: Demir, Caglar last_name: Demir - first_name: Moshood full_name: Yekini, Moshood last_name: Yekini - first_name: Michael full_name: Röder, Michael last_name: Röder - first_name: Yasir full_name: Mahmood, Yasir last_name: Mahmood - first_name: Axel-Cyrille full_name: Ngonga Ngomo, Axel-Cyrille last_name: Ngonga Ngomo citation: ama: 'Demir C, Yekini M, Röder M, Mahmood Y, Ngonga Ngomo A-C. Tree-Based OWL Class Expression Learner over Large Graphs. In: Lecture Notes in Computer Science. Springer Nature Switzerland; 2025. doi:10.1007/978-3-032-06066-2_29' apa: Demir, C., Yekini, M., Röder, M., Mahmood, Y., & Ngonga Ngomo, A.-C. (2025). Tree-Based OWL Class Expression Learner over Large Graphs. In Lecture Notes in Computer Science. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - ECML PKDD, Porto, Portugal. Springer Nature Switzerland. https://doi.org/10.1007/978-3-032-06066-2_29 bibtex: '@inbook{Demir_Yekini_Röder_Mahmood_Ngonga Ngomo_2025, place={Cham}, title={Tree-Based OWL Class Expression Learner over Large Graphs}, DOI={10.1007/978-3-032-06066-2_29}, booktitle={Lecture Notes in Computer Science}, publisher={Springer Nature Switzerland}, author={Demir, Caglar and Yekini, Moshood and Röder, Michael and Mahmood, Yasir and Ngonga Ngomo, Axel-Cyrille}, year={2025} }' chicago: 'Demir, Caglar, Moshood Yekini, Michael Röder, Yasir Mahmood, and Axel-Cyrille Ngonga Ngomo. “Tree-Based OWL Class Expression Learner over Large Graphs.” In Lecture Notes in Computer Science. Cham: Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-032-06066-2_29.' ieee: 'C. Demir, M. Yekini, M. Röder, Y. Mahmood, and A.-C. Ngonga Ngomo, “Tree-Based OWL Class Expression Learner over Large Graphs,” in Lecture Notes in Computer Science, Cham: Springer Nature Switzerland, 2025.' mla: Demir, Caglar, et al. “Tree-Based OWL Class Expression Learner over Large Graphs.” Lecture Notes in Computer Science, Springer Nature Switzerland, 2025, doi:10.1007/978-3-032-06066-2_29. short: 'C. Demir, M. Yekini, M. Röder, Y. Mahmood, A.-C. Ngonga Ngomo, in: Lecture Notes in Computer Science, Springer Nature Switzerland, Cham, 2025.' conference: end_date: 2025-09-19 location: Porto, Portugal name: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - ECML PKDD start_date: 2025-09-15 date_created: 2025-11-28T14:09:17Z date_updated: 2025-11-28T14:57:39Z department: - _id: '34' - _id: '574' doi: 10.1007/978-3-032-06066-2_29 keyword: - Decision Tree - OWL Class Expression Learning - Description Logic - Knowledge Graph - Large Language Model - Verbalizer language: - iso: eng place: Cham project: - _id: '285' name: SAIL - Nachhaltiger Lebenszyklus von intelligenten soziotechnischen Systemen publication: Lecture Notes in Computer Science publication_identifier: isbn: - '9783032060655' - '9783032060662' issn: - 0302-9743 - 1611-3349 publication_status: published publisher: Springer Nature Switzerland status: public title: Tree-Based OWL Class Expression Learner over Large Graphs type: book_chapter user_id: '114533' year: '2025' ... --- _id: '62007' abstract: - lang: eng text: "Ensemble methods are widely employed to improve generalization in machine learning. This has also prompted the adoption of ensemble learning for the knowledge graph embedding (KGE) models in performing link prediction. Typical approaches to this end train multiple models as part of the ensemble, and the diverse predictions are then averaged. However, this approach has some significant drawbacks. For instance, the computational overhead of training multiple models increases latency and memory overhead. In contrast, model merging approaches offer a promising alternative that does not require training multiple models. In this work, we introduce model merging, specifically weighted averaging, in\r\nKGE models. Herein, a running average of model parameters from a training epoch onward is maintained and used for predictions. To address this, we additionally propose an approach that selectively updates the running average of the ensemble model parameters only when the generalization performance improves on a validation dataset. We evaluate these two different weighted averaging approaches on link prediction tasks, comparing the state-of-the-art benchmark ensemble approach. Additionally, we evaluate the weighted averaging approach considering literal-augmented KGE models and multi-hop query answering tasks as well. The results demonstrate that the proposed weighted averaging approach consistently improves performance across diverse evaluation settings." author: - first_name: Rupesh full_name: Sapkota, Rupesh id: '89326' last_name: Sapkota - first_name: Caglar full_name: Demir, Caglar last_name: Demir - first_name: Arnab full_name: Sharma, Arnab last_name: Sharma - first_name: Axel-Cyrille full_name: Ngonga Ngomo, Axel-Cyrille last_name: Ngonga Ngomo citation: ama: 'Sapkota R, Demir C, Sharma A, Ngonga Ngomo A-C. Parameter Averaging in Link Prediction. In: Proceedings of the Thirteenth International Conference on Knowledge Capture(K-CAP 2025). ACM; 2025. doi:https://doi.org/10.1145/3731443.3771365' apa: Sapkota, R., Demir, C., Sharma, A., & Ngonga Ngomo, A.-C. (2025). Parameter Averaging in Link Prediction. Proceedings of the Thirteenth International Conference on Knowledge Capture(K-CAP 2025). Knowledge Capture Conference 2025, Dayton, OH, USA. https://doi.org/10.1145/3731443.3771365 bibtex: '@inproceedings{Sapkota_Demir_Sharma_Ngonga Ngomo_2025, place={Dayton, OH, USA}, title={Parameter Averaging in Link Prediction}, DOI={https://doi.org/10.1145/3731443.3771365}, booktitle={Proceedings of the Thirteenth International Conference on Knowledge Capture(K-CAP 2025)}, publisher={ACM}, author={Sapkota, Rupesh and Demir, Caglar and Sharma, Arnab and Ngonga Ngomo, Axel-Cyrille}, year={2025} }' chicago: 'Sapkota, Rupesh, Caglar Demir, Arnab Sharma, and Axel-Cyrille Ngonga Ngomo. “Parameter Averaging in Link Prediction.” In Proceedings of the Thirteenth International Conference on Knowledge Capture(K-CAP 2025). Dayton, OH, USA: ACM, 2025. https://doi.org/10.1145/3731443.3771365.' ieee: 'R. Sapkota, C. Demir, A. Sharma, and A.-C. Ngonga Ngomo, “Parameter Averaging in Link Prediction,” presented at the Knowledge Capture Conference 2025, Dayton, OH, USA, 2025, doi: https://doi.org/10.1145/3731443.3771365.' mla: Sapkota, Rupesh, et al. “Parameter Averaging in Link Prediction.” Proceedings of the Thirteenth International Conference on Knowledge Capture(K-CAP 2025), ACM, 2025, doi:https://doi.org/10.1145/3731443.3771365. short: 'R. Sapkota, C. Demir, A. Sharma, A.-C. Ngonga Ngomo, in: Proceedings of the Thirteenth International Conference on Knowledge Capture(K-CAP 2025), ACM, Dayton, OH, USA, 2025.' conference: end_date: 2025-12-10 location: Dayton, OH, USA name: Knowledge Capture Conference 2025 start_date: 2025-12-10 date_created: 2025-10-28T10:02:40Z date_updated: 2025-12-04T09:15:07Z ddc: - '000' department: - _id: '574' doi: https://doi.org/10.1145/3731443.3771365 file: - access_level: open_access content_type: application/pdf creator: rupezzz date_created: 2025-10-28T10:02:13Z date_updated: 2025-10-28T10:02:13Z file_id: '62008' file_name: public.pdf file_size: 837462 relation: main_file file_date_updated: 2025-10-28T10:02:13Z has_accepted_license: '1' keyword: - Knowledge Graphs - Embeddings - Ensemble Learning language: - iso: eng main_file_link: - url: https://papers.dice-research.org/2025/KCAP_ASWA/public.pdf oa: '1' place: Dayton, OH, USA project: - _id: '285' name: SAIL - Nachhaltiger Lebenszyklus von intelligenten soziotechnischen Systemen publication: Proceedings of the Thirteenth International Conference on Knowledge Capture(K-CAP 2025) publisher: ACM status: public title: Parameter Averaging in Link Prediction type: conference user_id: '89326' year: '2025' ... --- _id: '58874' author: - first_name: Manuel full_name: Fahrbach, Manuel last_name: Fahrbach - first_name: Tobias full_name: Jenert, Tobias id: '71994' last_name: Jenert orcid: ' https://orcid.org/0000-0001-9262-5646' - first_name: Alexander full_name: Fust, Alexander last_name: Fust - first_name: Noah full_name: Bellwald, Noah last_name: Bellwald - first_name: Christoph full_name: Winkler, Christoph last_name: Winkler citation: ama: 'Fahrbach M, Jenert T, Fust A, Bellwald N, Winkler C. Fostering self-regulated entrepreneurial learning in entrepreneurship education. In: Annals of Entrepreneurship Education and Pedagogy - 2025. Edward Elgar Publishing; 2025:249–265. doi:10.4337/9781035325795.00021' apa: Fahrbach, M., Jenert, T., Fust, A., Bellwald, N., & Winkler, C. (2025). Fostering self-regulated entrepreneurial learning in entrepreneurship education. In Annals of Entrepreneurship Education and Pedagogy - 2025 (pp. 249–265). Edward Elgar Publishing. https://doi.org/10.4337/9781035325795.00021 bibtex: '@inbook{Fahrbach_Jenert_Fust_Bellwald_Winkler_2025, title={Fostering self-regulated entrepreneurial learning in entrepreneurship education}, DOI={10.4337/9781035325795.00021}, booktitle={Annals of Entrepreneurship Education and Pedagogy - 2025}, publisher={Edward Elgar Publishing}, author={Fahrbach, Manuel and Jenert, Tobias and Fust, Alexander and Bellwald, Noah and Winkler, Christoph}, year={2025}, pages={249–265} }' chicago: Fahrbach, Manuel, Tobias Jenert, Alexander Fust, Noah Bellwald, and Christoph Winkler. “Fostering Self-Regulated Entrepreneurial Learning in Entrepreneurship Education.” In Annals of Entrepreneurship Education and Pedagogy - 2025, 249–265. Edward Elgar Publishing, 2025. https://doi.org/10.4337/9781035325795.00021. ieee: M. Fahrbach, T. Jenert, A. Fust, N. Bellwald, and C. Winkler, “Fostering self-regulated entrepreneurial learning in entrepreneurship education,” in Annals of Entrepreneurship Education and Pedagogy - 2025, Edward Elgar Publishing, 2025, pp. 249–265. mla: Fahrbach, Manuel, et al. “Fostering Self-Regulated Entrepreneurial Learning in Entrepreneurship Education.” Annals of Entrepreneurship Education and Pedagogy - 2025, Edward Elgar Publishing, 2025, pp. 249–265, doi:10.4337/9781035325795.00021. short: 'M. Fahrbach, T. Jenert, A. Fust, N. Bellwald, C. Winkler, in: Annals of Entrepreneurship Education and Pedagogy - 2025, Edward Elgar Publishing, 2025, pp. 249–265.' date_created: 2025-02-28T14:42:29Z date_updated: 2025-12-08T10:57:18Z department: - _id: '208' - _id: '640' doi: 10.4337/9781035325795.00021 keyword: - Self-Regulated Learning - Entrepreneurship Education - Entrepreneurship Research language: - iso: eng page: 249–265 project: - _id: '618' name: Self-Regulated Learning for Entrepreneurs – Förderung der Selbstregulationsfähigkeit angehender Unternehmer*innen publication: Annals of Entrepreneurship Education and Pedagogy - 2025 publication_identifier: isbn: - '9781035325795' - '9781035325788' - '9781035325795' publication_status: published publisher: Edward Elgar Publishing quality_controlled: '1' status: public title: Fostering self-regulated entrepreneurial learning in entrepreneurship education type: book_chapter user_id: '71994' year: '2025' ... --- _id: '63498' author: - first_name: Wilhelm full_name: Kirchgässner, Wilhelm last_name: Kirchgässner - first_name: Nikolas full_name: Förster, Nikolas last_name: Förster - first_name: Till full_name: Piepenbrock, Till last_name: Piepenbrock - first_name: Oliver full_name: Schweins, Oliver last_name: Schweins - first_name: Oliver full_name: Wallscheid, Oliver last_name: Wallscheid citation: ama: 'Kirchgässner W, Förster N, Piepenbrock T, Schweins O, Wallscheid O. HARDCORE: H-Field and Power Loss Estimation for Arbitrary Waveforms With Residual, Dilated Convolutional Neural Networks in Ferrite Cores. IEEE Transactions on Power Electronics. 2025;40(2):3326-3335. doi:10.1109/TPEL.2024.3488174' apa: 'Kirchgässner, W., Förster, N., Piepenbrock, T., Schweins, O., & Wallscheid, O. (2025). HARDCORE: H-Field and Power Loss Estimation for Arbitrary Waveforms With Residual, Dilated Convolutional Neural Networks in Ferrite Cores. IEEE Transactions on Power Electronics, 40(2), 3326–3335. https://doi.org/10.1109/TPEL.2024.3488174' bibtex: '@article{Kirchgässner_Förster_Piepenbrock_Schweins_Wallscheid_2025, title={HARDCORE: H-Field and Power Loss Estimation for Arbitrary Waveforms With Residual, Dilated Convolutional Neural Networks in Ferrite Cores}, volume={40}, DOI={10.1109/TPEL.2024.3488174}, number={2}, journal={IEEE Transactions on Power Electronics}, author={Kirchgässner, Wilhelm and Förster, Nikolas and Piepenbrock, Till and Schweins, Oliver and Wallscheid, Oliver}, year={2025}, pages={3326–3335} }' chicago: 'Kirchgässner, Wilhelm, Nikolas Förster, Till Piepenbrock, Oliver Schweins, and Oliver Wallscheid. “HARDCORE: H-Field and Power Loss Estimation for Arbitrary Waveforms With Residual, Dilated Convolutional Neural Networks in Ferrite Cores.” IEEE Transactions on Power Electronics 40, no. 2 (2025): 3326–35. https://doi.org/10.1109/TPEL.2024.3488174.' ieee: 'W. Kirchgässner, N. Förster, T. Piepenbrock, O. Schweins, and O. Wallscheid, “HARDCORE: H-Field and Power Loss Estimation for Arbitrary Waveforms With Residual, Dilated Convolutional Neural Networks in Ferrite Cores,” IEEE Transactions on Power Electronics, vol. 40, no. 2, pp. 3326–3335, 2025, doi: 10.1109/TPEL.2024.3488174.' mla: 'Kirchgässner, Wilhelm, et al. “HARDCORE: H-Field and Power Loss Estimation for Arbitrary Waveforms With Residual, Dilated Convolutional Neural Networks in Ferrite Cores.” IEEE Transactions on Power Electronics, vol. 40, no. 2, 2025, pp. 3326–35, doi:10.1109/TPEL.2024.3488174.' short: W. Kirchgässner, N. Förster, T. Piepenbrock, O. Schweins, O. Wallscheid, IEEE Transactions on Power Electronics 40 (2025) 3326–3335. date_created: 2026-01-06T08:07:13Z date_updated: 2026-01-06T08:08:01Z department: - _id: '52' doi: 10.1109/TPEL.2024.3488174 intvolume: ' 40' issue: '2' keyword: - Mathematical models - Estimation - Data models - Convolutional neural networks - Accuracy - Magnetic hysteresis - Magnetic cores - Temperature measurement - Magnetic domains - Temperature distribution - Convolutional neural network (CNN) - machine learning (ML) - magnetics page: 3326-3335 publication: IEEE Transactions on Power Electronics status: public title: 'HARDCORE: H-Field and Power Loss Estimation for Arbitrary Waveforms With Residual, Dilated Convolutional Neural Networks in Ferrite Cores' type: journal_article user_id: '83383' volume: 40 year: '2025' ... --- _id: '51518' abstract: - lang: eng text: In applications of piezoelectric actuators and sensors, the dependability and particularly the reliability throughout their lifetime are vital to manufacturers and end-users and are enabled through condition-monitoring approaches. Existing approaches often utilize impedance measurements over a range of frequencies or velocity measurements and require additional equipment or sensors, such as a laser Doppler vibrometer. Furthermore, the non-negligible effects of varying operating conditions are often unconsidered. To minimize the need for additional sensors while maintaining the dependability of piezoelectric bending actuators irrespective of varying operating conditions, an online diagnostics approach is proposed. To this end, time- and frequency-domain features are extracted from monitored current signals to reflect hairline crack development in bending actuators. For validation of applicability, the presented analysis method was evaluated on piezoelectric bending actuators subjected to accelerated lifetime tests at varying voltage amplitudes and under external damping conditions. In the presence of a crack and due to a diminished stiffness, the resonance frequency decreases and the root-mean-square amplitude of the current signal simultaneously abruptly drops during the lifetime tests. Furthermore, the piezoelectric crack surfaces clapping is reflected in higher harmonics of the current signal. Thus, time-domain features and harmonics of the current signals are sufficient to diagnose hairline cracks in the actuators. article_number: '521' article_type: original author: - first_name: Osarenren Kennedy full_name: Aimiyekagbon, Osarenren Kennedy id: '9557' last_name: Aimiyekagbon - first_name: Amelie full_name: Bender, Amelie id: '54290' last_name: Bender - first_name: Tobias full_name: Hemsel, Tobias id: '210' last_name: Hemsel - first_name: Walter full_name: Sextro, Walter id: '21220' last_name: Sextro citation: ama: Aimiyekagbon OK, Bender A, Hemsel T, Sextro W. Diagnostics of Piezoelectric Bending Actuators Subjected to Varying Operating Conditions. Electronics. 2024;13(3). doi:10.3390/electronics13030521 apa: Aimiyekagbon, O. K., Bender, A., Hemsel, T., & Sextro, W. (2024). Diagnostics of Piezoelectric Bending Actuators Subjected to Varying Operating Conditions. Electronics, 13(3), Article 521. https://doi.org/10.3390/electronics13030521 bibtex: '@article{Aimiyekagbon_Bender_Hemsel_Sextro_2024, title={Diagnostics of Piezoelectric Bending Actuators Subjected to Varying Operating Conditions}, volume={13}, DOI={10.3390/electronics13030521}, number={3521}, journal={Electronics}, publisher={MDPI AG}, author={Aimiyekagbon, Osarenren Kennedy and Bender, Amelie and Hemsel, Tobias and Sextro, Walter}, year={2024} }' chicago: Aimiyekagbon, Osarenren Kennedy, Amelie Bender, Tobias Hemsel, and Walter Sextro. “Diagnostics of Piezoelectric Bending Actuators Subjected to Varying Operating Conditions.” Electronics 13, no. 3 (2024). https://doi.org/10.3390/electronics13030521. ieee: 'O. K. Aimiyekagbon, A. Bender, T. Hemsel, and W. Sextro, “Diagnostics of Piezoelectric Bending Actuators Subjected to Varying Operating Conditions,” Electronics, vol. 13, no. 3, Art. no. 521, 2024, doi: 10.3390/electronics13030521.' mla: Aimiyekagbon, Osarenren Kennedy, et al. “Diagnostics of Piezoelectric Bending Actuators Subjected to Varying Operating Conditions.” Electronics, vol. 13, no. 3, 521, MDPI AG, 2024, doi:10.3390/electronics13030521. short: O.K. Aimiyekagbon, A. Bender, T. Hemsel, W. Sextro, Electronics 13 (2024). date_created: 2024-02-20T06:46:43Z date_updated: 2024-03-15T16:15:56Z department: - _id: '151' doi: 10.3390/electronics13030521 funded_apc: '1' intvolume: ' 13' issue: '3' keyword: - piezoelectric transducer - self-sensing - fault detection - diagnostics - hairline crack - condition monitoring language: - iso: eng publication: Electronics publication_identifier: issn: - 2079-9292 publication_status: published publisher: MDPI AG quality_controlled: '1' status: public title: Diagnostics of Piezoelectric Bending Actuators Subjected to Varying Operating Conditions type: journal_article user_id: '9557' volume: 13 year: '2024' ... --- _id: '53793' abstract: - lang: eng text: We utilize extreme learning machines for the prediction of partial differential equations (PDEs). Our method splits the state space into multiple windows that are predicted individually using a single model. Despite requiring only few data points (in some cases, our method can learn from a single full-state snapshot), it still achieves high accuracy and can predict the flow of PDEs over long time horizons. Moreover, we show how additional symmetries can be exploited to increase sample efficiency and to enforce equivariance. author: - first_name: Hans full_name: Harder, Hans id: '98879' last_name: Harder - first_name: Sebastian full_name: Peitz, Sebastian id: '47427' last_name: Peitz orcid: 0000-0002-3389-793X citation: ama: Harder H, Peitz S. Predicting PDEs Fast and Efficiently with Equivariant Extreme Learning Machines. apa: Harder, H., & Peitz, S. (n.d.). Predicting PDEs Fast and Efficiently with Equivariant Extreme Learning Machines. bibtex: '@article{Harder_Peitz, title={Predicting PDEs Fast and Efficiently with Equivariant Extreme Learning Machines}, author={Harder, Hans and Peitz, Sebastian} }' chicago: Harder, Hans, and Sebastian Peitz. “Predicting PDEs Fast and Efficiently with Equivariant Extreme Learning Machines,” n.d. ieee: H. Harder and S. Peitz, “Predicting PDEs Fast and Efficiently with Equivariant Extreme Learning Machines.” . mla: Harder, Hans, and Sebastian Peitz. Predicting PDEs Fast and Efficiently with Equivariant Extreme Learning Machines. short: H. Harder, S. Peitz, (n.d.). date_created: 2024-04-30T08:43:14Z date_updated: 2024-04-30T08:45:24Z keyword: - extreme learning machines - partial differential equations - data-driven prediction - high-dimensional systems language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2404.18530 oa: '1' publication_status: unpublished status: public title: Predicting PDEs Fast and Efficiently with Equivariant Extreme Learning Machines type: preprint user_id: '98879' year: '2024' ... --- _id: '54960' abstract: - lang: ger text: Das Fachdidaktische Wissen (FDW) wird als zentrale Komponente des Professionswissens von Lehrkräften bereits lange intensiv untersucht. Bislang liegen Ergebnisse zu Zusammenhängen des FDW mit anderen Professionswissensbereichen, zur Performanz in prototypischen Handlungssituationen und erste datengestützte inhaltlich-hierarchische Analysen auf Basis von Item Response Modellen (IRT-Modellen) vor. Im Zusammenhang mit einem projektübergreifend durchgeführten Vergleich entsprechender IRT-Modelle haben sich jedoch Limitationen bei der Vereinbarkeit und der inhaltlichen Reichhaltigkeit entsprechender Ergebnisse gezeigt, wie im Beitrag vorgestellt wird . Daher werden Analysemethoden aus dem Bereich des Machine Learning (unsupervised) vorgeschlagen, welche im Gegensatz zu IRT-Modellen auch nicht-hierarchische inhaltliche Strukturen aufdecken können. Es werden Ergebnisse entsprechender Clusteranalysen sowie Analysepläne zur Unterstützung dieser auf Basis der authentischen Sprachproduktionen von Proband:innen mithilfe von Natural Language Processing vorgestellt. author: - first_name: Jannis full_name: Zeller, Jannis id: '99022' last_name: Zeller orcid: 0000-0002-1834-5520 - first_name: Josef full_name: Riese, Josef id: '429' last_name: Riese orcid: 0000-0003-2927-2619 citation: ama: 'Zeller J, Riese J. Fähigkeitsprofile im Physikdidaktischen Wissen mithilfe von Machine Learning. In: van Vorst H, ed. Frühe naturwissenschaftliche Bildung, Tagungsband der GDCP Jahrestagung 2023. Gesellschaft für Didaktik der Chemie und Physik; 2024:122-125.' apa: Zeller, J., & Riese, J. (2024). Fähigkeitsprofile im Physikdidaktischen Wissen mithilfe von Machine Learning. In H. van Vorst (Ed.), Frühe naturwissenschaftliche Bildung, Tagungsband der GDCP Jahrestagung 2023 (pp. 122–125). Gesellschaft für Didaktik der Chemie und Physik. bibtex: '@inproceedings{Zeller_Riese_2024, place={Hamburg}, title={Fähigkeitsprofile im Physikdidaktischen Wissen mithilfe von Machine Learning}, booktitle={Frühe naturwissenschaftliche Bildung, Tagungsband der GDCP Jahrestagung 2023}, publisher={Gesellschaft für Didaktik der Chemie und Physik}, author={Zeller, Jannis and Riese, Josef}, editor={van Vorst, Helena}, year={2024}, pages={122–125} }' chicago: 'Zeller, Jannis, and Josef Riese. “Fähigkeitsprofile im Physikdidaktischen Wissen mithilfe von Machine Learning.” In Frühe naturwissenschaftliche Bildung, Tagungsband der GDCP Jahrestagung 2023, edited by Helena van Vorst, 122–25. Hamburg: Gesellschaft für Didaktik der Chemie und Physik, 2024.' ieee: J. Zeller and J. Riese, “Fähigkeitsprofile im Physikdidaktischen Wissen mithilfe von Machine Learning,” in Frühe naturwissenschaftliche Bildung, Tagungsband der GDCP Jahrestagung 2023, Hamburg, 2024, pp. 122–125. mla: Zeller, Jannis, and Josef Riese. “Fähigkeitsprofile im Physikdidaktischen Wissen mithilfe von Machine Learning.” Frühe naturwissenschaftliche Bildung, Tagungsband der GDCP Jahrestagung 2023, edited by Helena van Vorst, Gesellschaft für Didaktik der Chemie und Physik, 2024, pp. 122–25. short: 'J. Zeller, J. Riese, in: H. van Vorst (Ed.), Frühe naturwissenschaftliche Bildung, Tagungsband der GDCP Jahrestagung 2023, Gesellschaft für Didaktik der Chemie und Physik, Hamburg, 2024, pp. 122–125.' conference: location: Hamburg name: GDCP Jahrestagung 2023 date_created: 2024-07-01T14:33:40Z date_updated: 2024-07-03T08:47:31Z ddc: - '370' department: - _id: '15' - _id: '299' editor: - first_name: Helena full_name: van Vorst, Helena last_name: van Vorst file: - access_level: closed content_type: application/pdf creator: jzeller date_created: 2024-07-01T14:27:20Z date_updated: 2024-07-01T14:27:20Z file_id: '54961' file_name: Zeller, Riese (2024) Fähigkeitsprofile im Physikdidaktischen Wissen mithilfe von ML.pdf file_size: 389778 relation: main_file success: 1 file_date_updated: 2024-07-01T14:27:20Z has_accepted_license: '1' keyword: - Physikdidaktisches Wissen - Fähigkeitsprofile - Machine Learning language: - iso: ger page: 122-125 place: Hamburg publication: Frühe naturwissenschaftliche Bildung, Tagungsband der GDCP Jahrestagung 2023 publication_status: published publisher: Gesellschaft für Didaktik der Chemie und Physik status: public title: Fähigkeitsprofile im Physikdidaktischen Wissen mithilfe von Machine Learning type: conference user_id: '99022' year: '2024' ...