TY - GEN A1 - Mai, Christopher A1 - Henke, Anna-Sophia A1 - Geyer, Thomas T1 - Numerische Untersuchung der Schalldämpfung von Absorptionsschalldämpfern unterschiedlicher Querschnittsform T2 - Fortschritte der Akustik - DAGA, 46. Jahrestagung für Akustik, 16.-19. März 2020 in Hannover Y1 - 2020 UR - https://www-docs.b-tu.de/fg-akustik/public/veroeffentlichungen/mai_schalldaempfer_daga2020.pdf ER - TY - GEN A1 - Geyer, Thomas A1 - Mai, Christopher A1 - Henke, Anna-Sophia T1 - Numerical analysis of the transmission loss of dissipative mufflers with polygonal cross-section T2 - INTER-NOISE and NOISE-CON Congress and Conference Proceedings Y1 - 2021 UR - https://www.ingentaconnect.com/content/ince/incecp/2021/00000263/00000003/art00016;jsessionid=8bh6jb49nteeg.x-ic-live-01 U6 - https://doi.org/10.3397/IN-2021-2308 SN - 0736-2935 SP - 2949 EP - 3943 ER - TY - GEN A1 - Lehmann, Marlon Antonin A1 - Porsch, Ronny A1 - Mai, Christopher ED - Andersen, Ann-Louise ED - Andersen, Rasmus ED - Brunoe, Thomas Ditlev ED - Stoettrup Schioenning Larsen, Maria ED - Nielsen, Kjeld ED - Napoleone, Alessia ED - Kjeldgaard, Stefan T1 - Assembly Process Digitization Through Self-learning Assistance Systems in Production T2 - Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems : Proceedings of the 8th Changeable, Agile, Reconfigurable and Virtual Production Conference (CARV2021) and the 10th World Mass Customization & Personalization Conference (MCPC2021), Aalborg, Denmark, October/November 2021 N2 - Abstract. As product specifications change, manufacturing processes have to adapt. In manual production tasks, the human worker is forced to adapt at the same pace. Fast-changing work tasks lead to high stress and therefore increase failures. Digital assistance systems aim to support the human workforce by providing assembly instructions at the right time and in the right place to reduce the cognitive load. The latest digital assistance systems provide multimodal humanmachine interfaces, such as augmented reality, haptic feedback, and voice control to provide information or react to the user’s input.However, those digital assistance systems require the manufacturing information themselves, which are mostly provided through text-based or graphical programming. Both manufacturing experts and programmers are needed to create a digital assistance system workflow or adapt it to changes. This process is costly, time-consuming, and inflexible. This work presents a gesture recognition based approach for a self-learning digital assistance system. Therefore, assembly gestures are classified based on anatomical grip descriptions. Assembly sequences are recognized and learned by the digital assistance system using machine learning techniques. The learned procedures are used to automatically generate work instructions and guide the worker through the assembly task. Y1 - 2022 SN - 978-3-030-90699-3 SN - 978-3-030-90702-0 SN - 978-3-030-90700-6 U6 - https://doi.org/10.1007/978-3-030-90700-6_24 SP - 216 EP - 223 PB - Springer International Publishing ER -