@book{34397, abstract = {{Wie werden technische Systeme zeitgemäß entwickelt, die in Komplexität und Anspruch alles bisher Dagewesene übertreffen? Darauf gibt Systems Engineering Antworten. Bisherige Beschreibungen sind für den Maschinenbau aber oft schwer greifbar und zu unkonkret. Dieses Buch bricht den internationalen Stand der Wissenschaft auf das Wesentliche herunter und beschreibt praxisnah die Kernelemente des Systems Engineering. Eine konkrete Schritt-für-Schritt Anleitung ebnet den Weg für den erfolgreichen Transfer aus der klassischen Produktentwicklung hin zum Systems Engineering. Die Inhalte werden grafisch professionell und bewusst lebendig vermittelt. So wird nicht nur ein fundiertes Systems Engineering-Verständnis geschaffen, sondern auch die Basis für Kommunikation und Umsetzung im Unternehmen gelegt.}}, author = {{Gräßler, Iris and Oleff, Christian}}, isbn = {{978-3-662-64516-1}}, publisher = {{Springer Verlag}}, title = {{{Systems Engineering - verstehen und industriell umsetzen}}}, year = {{2022}}, } @inproceedings{34415, abstract = {{Challenges in the development of resource-efficient lightweight designs, such as emission and cost targets in production, lead to an increasing demand for environmentally friendly and fast joining processes. Therefore, cold-forming mechanical joining techniques provide an energy-efficient alternative in comparison to established processes, such as spot welding. However, to ensure a sufficient reliability of the product design, not only the selection of an appropriate manufacturing and joining method, but also the suitable dimensioning and validation of the entire joining process is a crucial step. In this context, thermal processes offer a large number of design principles while mechanical joining methods mainly require extensive experimental tests and the inclusion of expert knowledge. Although few contributions already investigated the data-based analysis of mechanical joints, a system for the requirement- and manufacturing-oriented dimensioning of joining components, such as different profiles and blanks, in combination with the estimation of joint properties is not available yet. Motivated by this lack, this contribution introduces an engineering workbench for the support of design engineers in the early development phases of the knowledge and data-based design of mechanical joining connections using clinching as an example. In this regard, the approach is demonstrated involving a similar material and sheet thickness combination with static loads.}}, author = {{Zirngibl, Christoph and Sauer, Christopher and Schleich, Benjamin and Wartzack, Sandro}}, booktitle = {{Volume 2: 42nd Computers and Information in Engineering Conference (CIE)}}, publisher = {{American Society of Mechanical Engineers}}, title = {{{Knowledge and Data-Based Design and Dimensioning of Mechanical Joining Connections}}}, doi = {{10.1115/detc2022-89172}}, year = {{2022}}, } @article{34417, abstract = {{Given strict emission targets and legal requirements, especially in the automotive industry, environmentally friendly and simultaneously versatile applicable production technologies are gaining importance. In this regard, the use of mechanical joining processes, such as clinching, enable assembly sheet metals to achieve strength properties similar to those of established thermal joining technologies. However, to guarantee a high reliability of the generated joint connection, the selection of a best-fitting joining technology as well as the meaningful description of individual joint properties is essential. In the context of clinching, few contributions have to date investigated the metamodel-based estimation and optimization of joint characteristics, such as neck or interlock thickness, by applying machine learning and genetic algorithms. Therefore, several regression models have been trained on varying databases and amounts of input parameters. However, if product engineers can only provide limited data for a new joining task, such as incomplete information on applied joining tool dimensions, previously trained metamodels often reach their limits. This often results in a significant loss of prediction quality and leads to increasing uncertainties and inaccuracies within the metamodel-based design of a clinch joint connection. Motivated by this, the presented contribution investigates different machine learning algorithms regarding their ability to achieve a satisfying estimation accuracy on limited input data applying a statistically based feature selection method. Through this, it is possible to identify which regression models are suitable to predict clinch joint characteristics considering only a minimum set of required input features. Thus, in addition to the opportunity to decrease the training effort as well as the model complexity, the subsequent formulation of design equations can pave the way to a more versatile application and reuse of pretrained metamodels on varying tool configurations for a given clinch joining task.}}, author = {{Zirngibl, Christoph and Schleich, Benjamin and Wartzack, Sandro}}, issn = {{2673-2688}}, journal = {{AI}}, keywords = {{Industrial and Manufacturing Engineering}}, number = {{4}}, pages = {{990--1006}}, publisher = {{MDPI AG}}, title = {{{Estimation of Clinch Joint Characteristics Based on Limited Input Data Using Pre-Trained Metamodels}}}, doi = {{10.3390/ai3040059}}, volume = {{3}}, year = {{2022}}, } @inbook{33186, author = {{Harmening, Anda-Lisa Martha Josephine Anna}}, booktitle = {{Sonderausgabe des Jahrbuchs für internationale Germanistik 2022}}, title = {{{Das Leben nehmen oder den Tod geben – Selbstbestimmtes Sterben auf der Schwelle von Utopie zur Realität?}}}, year = {{2022}}, } @book{34450, editor = {{Harmening, Anda-Lisa Martha Josephine Anna and Leinfellner, Stefanie and Meier, Rebecca}}, isbn = {{978-3-534-27585-4}}, pages = {{352}}, publisher = {{wbg Academic}}, title = {{{Wissenstransfer als Aufgabe, Herausforderung und Chance kulturwissenschaftlicher Forschung}}}, volume = {{1}}, year = {{2022}}, } @book{34453, editor = {{Tönnies, Merle and Voigts, Eckart}}, publisher = {{Walter deGruyter}}, title = {{{Twenty-First Century Anxieties: Dys/Utopian Spaces and Contexts in Contemporary British Theatre}}}, volume = {{32}}, year = {{2022}}, } @article{34452, author = {{Tönnies, Merle and Flotmann-Scholz, Christina}}, journal = {{Anglistik: International Journal of English Studies}}, number = {{2}}, pages = {{ 257--272}}, title = {{{Directing Pandemic Attention: Dystopian Corona Narratives and the Nation in British Media and Politics}}}, volume = {{33}}, year = {{2022}}, } @article{34454, author = {{Tönnies, Merle and Voigts, Eckart}}, journal = {{Twenty-First Century Anxieties: Dys/Utopian Spaces and Contexts in Contemporary British Theatre (CDE Studies)}}, publisher = {{Walter deGruyter}}, title = {{{Anger, Anxiety and Hope: The Complicit Realities and Engaged/ing Communities of Contemporary Briitsh Dys/Utopian Theatre}}}, volume = {{32}}, year = {{2022}}, } @inbook{34467, author = {{Lütje-Klose , Birgit and Grüter, Sandra and Neumann, Phillip and Weber, Antonia and Goldan, Janka and Gorges, Julia and Wild, Elke}}, booktitle = {{Qualifizierung für Inklusion. Sekundarstufe }}, editor = {{Lutz, Deborah and Becker, Jonas and Buchhaupt, Felix and Katzenbach, Dieter and Strecker, Alicia and Urban, Michael}}, isbn = {{978-3-8309-4514-7}}, pages = {{163--178}}, publisher = {{Waxmann}}, title = {{{Weil wir tatsächlich nicht voneinander wussten, was jeder Einzelne so an verborgenen Schätzen bringt}}}, doi = {{10.31244/9783830995142}}, volume = {{3}}, year = {{2022}}, } @inproceedings{34465, author = {{laeim, Huddad and Schlickriede, Christian and Chaisakul, Papichaya and Chattham, Nattaporn and Panitchakan, Hathai and Siangchaew, Krisda and Zentgraf, Thomas and Pattanaporhratana, Apichart}}, booktitle = {{Metamaterials, Metadevices, and Metasystems 2022}}, editor = {{Engheta, Nader and Noginov, Mikhail A. and Zheludev, Nikolay I.}}, publisher = {{SPIE}}, title = {{{Design and investigation of a metalens for efficiency enhancement of laser-waveguide coupling in a limited space system}}}, doi = {{10.1117/12.2629789}}, year = {{2022}}, }