@article{23803, author = {{Reitz, Alexander and Grydin, Olexandr and Schaper, Mirko}}, journal = {{Materials Data for Smart Forming Technologies}}, location = {{Freiberg}}, title = {{{Characterization of phase transformations during graded thermo- mechanical treatment of steel 22MnB5 by means of optical methods }}}, year = {{2021}}, } @article{21545, author = {{Masendorf, Lukas and Wächter, Michael and Esderts, Alfons and Otroshi, Mortaza and Meschut, Gerson}}, issn = {{8756-758X}}, journal = {{Fatigue & Fracture of Engineering Materials & Structures}}, pages = {{15}}, title = {{{Service life estimation of self‐piercing riveted joints by linear damage accumulation}}}, doi = {{10.1111/ffe.13446}}, year = {{2021}}, } @misc{45788, author = {{Bülling, Jonas}}, title = {{{Political Speaker Transfer: Learning to Generate Text in the Styles of Barack Obama and Donald Trump}}}, year = {{2021}}, } @misc{45787, author = {{Mishra, Avishek}}, title = {{{Computational Text Professionalization using Neural Sequence-to-Sequence Models}}}, year = {{2021}}, } @article{46563, author = {{Georgi, Christopher}}, journal = {{Sprachreport}}, number = {{2}}, pages = {{14–21}}, title = {{{Sprache in Politik und Gesellschaft. Perspektiven und Zugänge. Bericht von der 57. Jahrestagung des Leibniz-Instituts für Deutsche Sprache (als Online-Konferenz), 9. bis 11. März 2021}}}, year = {{2021}}, } @article{46562, author = {{Georgi, Christopher}}, journal = {{Deutsche Sprache}}, pages = {{279–283}}, title = {{{Sprache in Politik und Gesellschaft. Perspektiven und Zugänge. Bericht von der 57. Jahrestagung des Leibniz-Instituts für Deutsche Sprache, 9. bis 11. März 2021}}}, volume = {{3}}, year = {{2021}}, } @article{46629, author = {{Vöing, Nerea and Leschke, Jonas and Daumiller, Martin}}, journal = {{ZFHE}}, number = {{3}}, pages = {{199--216}}, title = {{{„Evidenz“, was meinen Sie damit? – Eine Interviewstudie zum Verständnis von Hochschullehrenden vom Evidenzbegriff}}}, doi = {{10.3217/zfhe-16-03/12}}, volume = {{16}}, year = {{2021}}, } @inproceedings{46106, author = {{Erofeeva, Maria and Klowait, Nils Oliver}}, booktitle = {{2021 7th International Conference of the Immersive Learning Research Network (iLRN)}}, publisher = {{IEEE}}, title = {{{The Impact of Virtual Reality, Augmented Reality, and Interactive Whiteboards on the Attention Management in Secondary School STEM Teaching}}}, doi = {{10.23919/ilrn52045.2021.9459318}}, year = {{2021}}, } @misc{43005, author = {{Gawlikowicz, Roland}}, title = {{{Untersuchung von Polymer-Dry-Blends und Bewertung ihrer Verarbeitungseigenschaften für den Lasersinterprozess (Studienarbeit)}}}, year = {{2021}}, } @phdthesis{21630, abstract = {{Eine zustandsbasierte Instandhaltungsstrategie reduziert das Risiko eines Ausfalls eines technischen Systems bei gleichzeitig hoher Ausnutzung und planbaren Instandhaltungsmaßnahmen. Das Ziel dieser Arbeit liegt in der Entwicklung einer Zustandsüberwachung für Gummi-Metall-Elemente. Die Herausforderungen dieser Zustandsüberwachung leiten sich aus dem viskoelastischen Verhalten sowie dem komplexen Degradationsverhalten der Elemente ab. Infolge der daraus resultierenden Unsicherheiten werden die Elemente heutzutage präventiv instandgehalten. In Lebensdauerversuchen der Gummi-Metall-Elemente werden drei Messgrößen detektiert. Dabei wird mit der Temperatur eine Messgröße identifiziert, die am geeignetsten zur Beschreibung des Zustands der Elemente ist. Generell wird die Genauigkeit einer Zustandsüberwachung durch verschiedene Unsicherheiten beeinflusst. Für die Prognose der nutzbaren Restlebensdauer der Gummi-Metall-Elemente wird das Partikelfilter, eine verbreitete modellbasierte Methode zur Zustandsüberwachung technischer Systeme, weiterentwickelt, um Unsicherheiten im Verhalten und der Degradation der Elemente zu berücksichtigen. Anhand der Ergebnisse wird belegt, dass aufbauend auf dieser Zustandsüberwachung die Ausnutzung der Gummi-Metall-Elemente in realen Anwendungen durch eine präventive Instandhaltung erhöht werden kann. Damit bildet diese Arbeit die Basis für zukünftige, prädiktive Instandhaltungskonzepte für diese Elemente. Weiterhin bestätigt die Arbeit, dass eine Berücksichtigung vorliegender Unsicherheiten zu einem frühen Zeitpunkt im Entwicklungsprozess des Zustandsüberwachungssystems empfehlenswert ist.}}, author = {{Bender, Amelie}}, keywords = {{Zustandsüberwachung, Prognose der Restlebensdauer, modellbasierte Prognose, Partikelfilter, Unsicherheiten, Gummi, Verlässlichkeit, Lebensdauerversuche, Predictive Maintenance}}, publisher = {{Shaker}}, title = {{{Zustandsüberwachung zur Prognose der Restlebensdauer von Gummi-Metall-Elementen unter Berücksichtigung systembasierter Unsicherheiten}}}, doi = {{10.17619/UNIPB/1-1084}}, year = {{2021}}, } @article{21436, abstract = {{Ultrasonic wire bonding is a solid-state joining process, used in the electronics industry to form electrical connections, e.g. to connect electrical terminals within semiconductor modules. Many process parameters affect the bond strength, such like the bond normal force, ultrasonic power, wire material and bonding frequency. Today, process design, development, and optimization is most likely based on the knowledge of process engineers and is mainly performed by experimental testing. In this contribution, a newly developed simulation tool is presented, to reduce time and costs and efficiently determine optimized process parameter. Based on a co-simulation of MATLAB and ANSYS, the different physical phenomena of the wire bonding process are considered using finite element simulation for the complex plastic deformation of the wire and reduced order models for the transient dynamics of the transducer, wire, substrate and bond formation. The model parameters such as the coefficients of friction between bond tool and wire and between wire and substrate were determined for aluminium and copper wire in experiments with a test rig specially developed for the requirements of heavy wire bonding. To reduce simulation time, for the finite element simulation a restart analysis and high performance computing is utilized. Detailed analysis of the bond formation showed, that the normal pressure distribution in the contact between wire and substrate has high impact on bond formation and distribution of welded areas in the contact area.}}, author = {{Schemmel, Reinhard and Krieger, Viktor and Hemsel, Tobias and Sextro, Walter}}, issn = {{0026-2714}}, journal = {{Microelectronics Reliability}}, keywords = {{Ultrasonic heavy wire bonding, Co-simulation, ANSYS, MATLAB, Process optimization, Friction coefficient, Copper-copper, Aluminium-copper}}, pages = {{114077}}, title = {{{Co-simulation of MATLAB and ANSYS for ultrasonic wire bonding process optimization}}}, doi = {{https://doi.org/10.1016/j.microrel.2021.114077}}, volume = {{119}}, year = {{2021}}, } @inproceedings{22724, abstract = {{ Predictive Maintenance as a desirable maintenance strategy in industrial applications relies on suitable condition monitoring solutions to reduce costs and risks of the monitored technical systems. In general, those solutions utilize model-based or data-driven methods to diagnose the current state or predict future states of monitored technical systems. However, both methods have their advantages and drawbacks. Combining both methods can improve uncertainty consideration and accuracy. Different combination approaches of those hybrid methods exist to exploit synergy effects. The choice of an appropriate approach depends on different requirements and the goal behind the selection of a hybrid approach. In this work, the hybrid approach for estimating remaining useful lifetime takes potential uncertainties into account. Therefore, a data-driven estimation of new measurements is integrated within a model-based method. To consider uncertainties within the system, a differentiation between different system behavior is realized throughout diverse states of degradation. The developed hybrid prediction approach bases on a particle filtering method combined with a machine learning method, to estimate the remaining useful lifetime of technical systems. Particle filtering as a Monte Carlo simulation technique is suitable to map and propagate uncertainties. Moreover, it is a state-of-the-art model-based method for predicting remaining useful lifetime of technical systems. To integrate uncertainties a multi-model particle filtering approach is employed. In general, resampling as a part of the particle filtering approach has the potential to lead to an accurate prediction. However, in the case where no future measurements are available, it may increase the uncertainty of the prediction. By estimating new measurements, those uncertainties are reduced within the data-driven part of the approach. Hence, both parts of the hybrid approach strive to account for and reduce uncertainties. Rubber-metal-elements are employed as a use-case to evaluate the developed approach. Rubber-metal-elements, which are used to isolate vibrations in various systems, such as railways, trucks and wind turbines, show various uncertainties in their behavior and their degradation. Those uncertainties are caused by diverse inner and outer factors, such as manufacturing influences and operating conditions. By expert knowledge the influences are described, analyzed and if possible reduced. However, the remaining uncertainties are considered within the hybrid prediction method. Relative temperature is the selected measurand to describe the element’s degradation. In lifetime tests, it is measured as the difference between the element’s temperature and the ambient temperature. Thereby, the influence of the ambient temperature on the element’s temperature is taken into account. Those elements show three typical states of degradation that are identified within the temperature measurements. Depending on the particular state of degradation a new measurement is estimated within the hybrid approach to reduce potential uncertainties. Finally, the performance of the developed hybrid method is compared to a model-based method for estimating the remaining useful lifetime of the same elements. Suitable performance indices are implemented to underline the differences between the results.}}, author = {{Bender, Amelie and Sextro, Walter}}, booktitle = {{Proceedings of the European Conference of the PHM Society 2021}}, editor = {{Do, Phuc and King, Steve and Fink, Olga}}, keywords = {{Hybrid prediction method, Multi-model particle filtering, Uncertainty quantification, RUL estimation}}, number = {{1}}, title = {{{Hybrid Prediction Method for Remaining Useful Lifetime Estimation Considering Uncertainties}}}, doi = {{https://doi.org/10.36001/phme.2021.v6i1.2843 }}, volume = {{6}}, year = {{2021}}, } @inproceedings{22507, abstract = {{Several methods, including order analysis, wavelet analysis and empirical mode decomposition have been proposed and successfully employed for the health state estimation of technical systems operating under varying conditions. However, where information such as the speed of rotating machinery, component specifications or other domain-specific information is unavailable, such methods are often infeasible. Thus, this paper investigates the application of classical time-domain features, features from the medical field and novel features from the highly comparative time-series analysis (HCTSA) package, for the health state estimation of rotating machinery operating under varying conditions. Furthermore, several feature selection methods are investigated to identify features as viable health indicators for the diagnostics and prognostics of technical systems. As a case study, the presented methods are evaluated on real-world and experimentally acquired vibration data of bearings operating under varying speed. The results show that the selected features can successfully be employed as health indicators for technical systems operating under varying conditions.}}, author = {{Aimiyekagbon, Osarenren Kennedy and Bender, Amelie and Sextro, Walter}}, booktitle = {{Proceedings of the Seventeenth International Conference on Condition Monitoring and Asset Management (CM 2021)}}, keywords = {{Wind turbine diagnostics, bearing diagnostics, non-stationary operating conditions, varying operating conditions, feature extraction, feature selection, fault detection, failure detection}}, title = {{{On the applicability of time series features as health indicators for technical systems operating under varying conditions}}}, year = {{2021}}, } @inproceedings{27111, abstract = {{In the industry 4.0 era, there is a growing need to transform unstructured data acquired by a multitude of sources into information and subsequently into knowledge to improve the quality of manufactured products, to boost production, for predictive maintenance, etc. Data-driven approaches, such as machine learning techniques, are typically employed to model the underlying relationship from data. However, an increase in model accuracy with state-of-the-art methods, such as deep convolutional neural networks, results in less interpretability and transparency. Due to the ease of implementation, interpretation and transparency to both domain experts and non-experts, a rule-based method is proposed in this paper, for prognostics and health management (PHM) and specifically for diagnostics. The proposed method utilizes the most relevant sensor signals acquired via feature extraction and selection techniques and expert knowledge. As a case study, the presented method is evaluated on data from a real-world quality control set-up provided by the European prognostics and health management society (PHME) at the conference’s 2021 data challenge. With the proposed method, our team took the third place, capable of successfully diagnosing different fault modes, irrespective of varying conditions.}}, author = {{Aimiyekagbon, Osarenren Kennedy and Muth, Lars and Wohlleben, Meike Claudia and Bender, Amelie and Sextro, Walter}}, booktitle = {{Proceedings of the European Conference of the PHM Society 2021}}, editor = {{Do, Phuc and King, Steve and Fink, Olga}}, keywords = {{PHME 2021, Feature Selection Classification, Feature Selection Clustering, Interpretable Model, Transparent Model, Industry 4.0, Real-World Diagnostics, Quality Control, Predictive Maintenance}}, number = {{1}}, pages = {{527--536}}, title = {{{Rule-based Diagnostics of a Production Line}}}, doi = {{10.36001/phme.2021.v6i1.3042}}, volume = {{6}}, year = {{2021}}, } @inproceedings{33975, author = {{Lenz, Cederic and Henke, Christian and Trächtler, Ansgar}}, booktitle = {{2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)}}, location = {{Vasteras, Sweden }}, publisher = {{IEEE}}, title = {{{Anomaly detection in hot forming processes using hybrid modeling}}}, doi = {{10.1109/etfa45728.2021.9613629}}, year = {{2021}}, } @inbook{55796, author = {{Grössl, Johannes}}, booktitle = {{Analytische und kontinentale Philosophie im Dialog }}, editor = {{Höhn , Hans-Joachim and Wendel, Saskia and Reimann, Gregor and Tappen, Julian}}, isbn = {{978-3-451-02314-9}}, pages = {{362--382}}, publisher = {{Herder}}, title = {{{Die Pluralität von Offenbarungsmodellen in der Analytischen Theologie}}}, volume = {{314}}, year = {{2021}}, } @book{50457, author = {{Tews, Karina and Aubel, Tobias and Meschut, Gerson and Duffe, Tobias and Kullmer, Gunter}}, pages = {{188}}, publisher = {{DVS Media}}, title = {{{Methodenentwicklung zur numerischen Lebensdauerprognose von hyperelastischen Klebverbindungen infolge zyklischer Beanspruchung mittels bruchmechanischer Ansätze}}}, volume = {{509}}, year = {{2021}}, } @proceedings{58117, editor = {{Paschke, Hanno and Lauth, Martin and Schaper, Mirko and Brückner, Tristan and Thewes, Alexander}}, location = {{Virtual Conference}}, title = {{{Surface modifications reducing the adhesion of aluminum in twin roll casting applications}}}, year = {{2021}}, } @inbook{57672, author = {{Fegter, Susanne and Langer, Antje and Thon, Christine}}, booktitle = {{Diskursanalytische Geschlechterforschung in der Erziehungswissenschaft. Jahrbuch Erziehungswissenschaftliche Geschlechterforschung}}, editor = {{Fegter, Susanne and Langer, Antje and Thon, Christine}}, isbn = {{978-3-8474-2484-0}}, pages = {{11--19}}, publisher = {{Barbara Budrich Verlag}}, title = {{{Diskursanalytische Geschlechterforschung in der Erziehungswissenschaft}}}, doi = {{10.3224/84742484}}, volume = {{17}}, year = {{2021}}, } @article{59439, abstract = {{AbstractWhen physicians are asked to determine the positive predictive value from the a priori probability of a disease and the sensitivity and false positive rate of a medical test (Bayesian reasoning), it often comes to misjudgments with serious consequences. In daily clinical practice, however, it is not only important that doctors receive a tool with which they cancorrectlyjudge—thespeedof these judgments is also a crucial factor. In this study, we analyzed accuracy and efficiency in medical Bayesian inferences. In an empirical study we varied information format (probabilities vs. natural frequencies) and visualization (text only vs. tree only) for four contexts. 111 medical students participated in this study by working on four Bayesian tasks with common medical problems. The correctness of their answers was coded and the time spent on task was recorded. The median time for a correct Bayesian inference is fastest in the version with a frequency tree (2:55 min) compared to the version with a probability tree (5:47 min) or to the text only versions based on natural frequencies (4:13 min) or probabilities (9:59 min).The scorediagnostic efficiency(calculated by: median time divided by percentage of correct inferences) is best in the version with a frequency tree (4:53 min). Frequency trees allow more accurateandfaster judgments. Improving correctness and efficiency in Bayesian tasks might help to decrease overdiagnosis in daily clinical practice, which on the one hand cause cost and on the other hand might endanger patients’ safety.}}, author = {{Binder, Karin and Krauss, Stefan and Schmidmaier, Ralf and Braun, Leah T.}}, issn = {{1382-4996}}, journal = {{Advances in Health Sciences Education}}, number = {{3}}, pages = {{847--863}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{Natural frequency trees improve diagnostic efficiency in Bayesian reasoning}}}, doi = {{10.1007/s10459-020-10025-8}}, volume = {{26}}, year = {{2021}}, }