@article{52828, author = {{Brüning, Florian and Kleinschmidt, Dennis and Petzke, J.}}, issn = {{https://doi.org/10.3390/polym15224406}}, journal = {{Polymers}}, pages = {{1--23}}, title = {{{Wall Slip-Free Viscosity Determination of Filled Rubber Compounds Using Steady-State Shear Measurements}}}, doi = {{https://doi.org/10.3390/polym15224406}}, year = {{2023}}, } @article{52833, author = {{Schöppner, Volker and Austermeier, Laura and Brüning, Florian and Oldemeier, Jan Philipp and Brandt, O.}}, issn = {{2190-4774}}, journal = {{EXTRUSION}}, number = {{8/2023}}, pages = {{56--59}}, title = {{{Recycling-Ansatz für mehrkomponentige Kunststoffprodukte durch thermische Verbundtrennung}}}, year = {{2023}}, } @inproceedings{52840, author = {{Schöppner, Volker and Arndt, Theresa}}, booktitle = {{76th Annual Assembly of the International Institute of Welding (IIW)}}, title = {{{Anvil-free ultrasonic welding for welding situations with one sided access}}}, year = {{2023}}, } @article{52836, author = {{Brüning, Florian and Kleinschmidt, Dennis and Petzke, J.}}, journal = {{Kunststoffland NRW Report}}, number = {{03/2023}}, pages = {{28--29}}, title = {{{Elastomerrecycling mittels Mikrowellenstrahlung}}}, year = {{2023}}, } @article{52837, author = {{Moritzer, Elmar and Kartelmeyer, S. and Kringe, R. and Jaroschek, C.}}, journal = {{Plastics Insights}}, number = {{8/2023}}, pages = {{44--48}}, title = {{{Conformal Cooling at Low Cost}}}, year = {{2023}}, } @inproceedings{52816, abstract = {{Manufacturing companies face the challenge of reaching required quality standards. Using optical sensors and deep learning might help. However, training deep learning algorithms require large amounts of visual training data. Using domain randomization to generate synthetic image data can alleviate this bottleneck. This paper presents the application of synthetic image training data for optical quality inspections using visual sensor technology. The results show synthetically generated training data are appropriate for visual quality inspections.}}, author = {{Gräßler, Iris and Hieb, Michael}}, booktitle = {{Lectures}}, keywords = {{synthetic training data, machine vision quality gates, deep learning, automated inspection and quality control, production control}}, location = {{Nuremberg}}, pages = {{253--524}}, publisher = {{AMA Service GmbH, Von-Münchhausen-Str. 49, 31515 Wunstorf, Germany}}, title = {{{Creating Synthetic Training Datasets for Inspection in Machine Vision Quality Gates in Manufacturing}}}, doi = {{10.5162/smsi2023/d7.4}}, year = {{2023}}, } @inproceedings{46450, author = {{Gräßler, Iris and Preuß, Daniel and Brandt, Lukas and Mohr, Michael}}, booktitle = {{Proceedings of the Design Society}}, location = {{Bordeaux}}, pages = {{1595--1604}}, title = {{{Efficient Formalisation of Technical Requirements for Generative Engineering}}}, doi = {{10.1017/pds.2023.160}}, year = {{2023}}, } @inproceedings{52821, abstract = {{Due to economic and ecological framework conditions, a resource-saving utilization of raw materials and energy is becoming increasingly important in particular in the mobility sector. For the reduction of moving masses and the resources consumed, lightweight construction technologies are part of modern production processes in vehicle manufacturing, for example in the form of multi-material systems. Challenging in the manufacture of multi-material systems especially in view of changing supply chains is the variety of materials and geometries that bring conventional joining processes to their limits. Therefore, new processes are required, which can react versatile to process and disturbance variables. A widely used industrial joining process is semi-tubular self-piercing riveting, which is however a rigid process. To increase the versatility, the two newly established processes multi-range self-piercing riveting and tumbling self-piercing riveting are combined and the capabilities for targeted material flow control are united. Therefore, an innovative two-stage process based on the combination is introduced in this paper. The rivet is set with the multi-range self-piercing riveting process with an overlap of the rivet head and then formed by a tumbling process. Further, a specific adaptation of the tumbling strategy is used to investigate the possibility of reducing cracks in the rivet head. Thereby, different tumbling strategies are used and similar geometric joint formations are achieved to compare the results. }}, author = {{Wituschek, Simon and Kappe, Fabian and Meschut, Gerson and Lechner, Michael}}, booktitle = {{Materials Research Proceedings}}, issn = {{2474-395X}}, publisher = {{Materials Research Forum LLC}}, title = {{{Combination of versatile self-piercing riveting processes}}}, doi = {{10.21741/9781644902417-16}}, year = {{2023}}, } @inbook{52859, author = {{de Camargo e Souza Câmara, Igor and Turhan, Anni-Yasmin}}, booktitle = {{Logics in Artificial Intelligence}}, isbn = {{9783031436185}}, issn = {{0302-9743}}, publisher = {{Springer Nature Switzerland}}, title = {{{Deciding Subsumption in Defeasible $$\mathcal {ELI}_\bot $$ with Typicality Models}}}, doi = {{10.1007/978-3-031-43619-2_36}}, year = {{2023}}, } @article{52861, author = {{Gil, Oliver Fernández and Patrizi, Fabio and Perelli, Giuseppe and Turhan, Anni-Yasmin}}, journal = {{CoRR}}, title = {{{Optimal Alignment of Temporal Knowledge Bases}}}, doi = {{10.48550/ARXIV.2307.15439}}, volume = {{abs/2307.15439}}, year = {{2023}}, }