TY - JOUR A1 - Schmitt, Jan A1 - Engelmann, Bastian A1 - Manghisi, Vito Modesto A1 - Wilhelm, Markus A1 - Uva, Antonello A1 - Fiorentino, Michele T1 - Towards gestured-based technologies for human-centred smart factories JF - International Journal of Computer Integrated Manufacturing N2 - Despite the increasing degree of automation in industry, manual or semi-automated are commonly and inevitable for complex assembly tasks. The transformation to smart processes in manufacturing leads to a higher deployment of data-driven approaches to support the worker. Upcoming technologies in this context are oftentimes based on the gesture-recognition, − monitoring or – control. This contribution systematically reviews gesture or motion capturing technologies and the utilization of gesture data in the ergonomic assessment, gesture-based robot control strategies as well as the identification of COVID-19 symptoms. Subsequently, two applications are presented in detail. First, a holistic human-centric optimization method for line-balancing using a novel indicator – ErgoTakt – derived by motion capturing. ErgoTakt improves the legacy takt-time and helps to find an optimum between the ergonomic evaluation of an assembly station and the takt-time balancing. An optimization algorithm is developed to find the best-fitting solution by minimizing a function of the ergonomic RULA-score and the cycle time of each assembly workstation with respect to the workers’ ability. The second application is gesture-based robot-control. A cloud-based approach utilizing a generally accessible hand-tracking model embedded in a low-code IoT programming environment is shown. KW - gesture-based monitoring KW - gesture-based control KW - manufacturing Y1 - 2023 UR - https://doi.org/10.1080/0951192X.2022.2121424 SN - 1362-3052 VL - 36 IS - 1 SP - 110 EP - 127 ER - TY - RPRT A1 - Zinger, Benjamin A1 - Wester, Ann Marie A1 - Bauer, Marina A1 - Beckert, Johannes A1 - Bertels, Victoria A1 - Dobhan, Alexander A1 - Dölling, Hanna A1 - Hanshans, Christian A1 - Höllen, Max A1 - Kaus, Eduard A1 - Maier, Laura A1 - Martin, Vincent A1 - Metz, Jeremy A1 - Nägle, Kirsten A1 - Rammler, Melanie A1 - Rieke, Almut A1 - Schäfle, Claudia A1 - Schmitt, Markus A1 - Weidel, Antonia A1 - Wissel, Christine A1 - Zauner, Johannes A1 - Zitzmann, Tilman T1 - Lehrlabor³ - ein Netzwerk zur teambasierten Lehrentwicklung: Einblicke und Ergebnisse in ein hochschul-und rollenübergreifendes Programm zur Lehrentwicklung in der Hochschulbildung 05/2022–04/2023 Y1 - 2023 U6 - https://doi.org/10.34646/thn/ohmdok-925 VL - 4 SP - 4 EP - 64 PB - FIDL – Forschungs-und Innovationslabor Digitale Lehre CY - Nürnberg ; München ER - TY - JOUR A1 - Wilhelm, Markus A1 - Manghisi, Vito Modesto A1 - Uva, Antonello A1 - Fiorentino, Michele A1 - Bräutigam, Volker A1 - Engelmann, Bastian A1 - Schmitt, Jan T1 - ErgoTakt: A novel approach of human-centered balancing of manual assembly lines JF - Procedia CIRP N2 - Although the increasing use of automation in industry, manual assembly stations are still common and, in some situations, even inevitable. Current practice in manual assembly lines is to balance them using the takt-time of each workstation and harmonize it. However, this approach mostly does not include ergonomic aspects and thus it may lead to workforce musculoskeletal disorders, extended leaves, and demotivation. This paper presents a holistic human-centric optimization method for line balancing using a novel indicator ̶ the ErgoTakt. ErgoTakt improves the legacy takt-time and helps to find an optimum between the ergonomic evaluation of an assembly station and its balance in time. The authors used a custom version of the ErgoSentinel Software and a Microsoft Kinect depth camera to perform online and real-time ergonomic assessment. An optimization algorithm is developed to find the best-fitting solution by minimizing a function of the ergonomic RULA-value and the cycle time of each assembly workstation with respect to the worker's ability. The paper presents the concept, the system-setup and preliminary evaluation of an assembly scenario. The results demonstrate that the new approach is feasible and able to optimize an entire manual assembly process chain in terms of both, economic aspects of a well-balanced production line as well as the ergonomic issue of long term human healthy work. Y1 - 2021 U6 - https://doi.org/https://doi.org/10.1016/j.procir.2020.05.250 VL - 97 SP - 354 EP - 360 ER - TY - CHAP A1 - Rosilius, Maximilian A1 - Wirsing, Benedikt A1 - von Eitzen, Ingo A1 - Wilhelm, Markus A1 - Schmitt, Jan A1 - Engelmann, Bastian A1 - Bräutigam, Volker T1 - Evaluation of Visual Requirements and Software-Design for Immersive Visibility in Industrial Applications T2 - 2021 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct) N2 - Currently, many sources predict increasing use of AR technology in the industrial environment. The task of immersive productive assistance systems is to provide information contextually to the industrial user. Therefore, it is essential to explore the factors and effects that influence the visibility and the corresponding quality of this information. Caused by the technical limitations of additive display technology and application conditions, this new approach has evaluated the immersive visibility of Landolt Rings in various greyscales against ambient illuminance levels on different industrial-like surfaces, coupled with and without a white virtual background. For this purpose, an empirical study in a within-subjects-design with full factorial experimental design (n=23) was conducted on Microsoft HoloLens 2 hardware. The mean values of the main effects indicate that visibility is significantly affected by ambient illuminance (best results at lower level), greyscale (best results at middle level) and virtual background (best results with background). In contrast, the choice of surface is shown to have no statistically significant effect on visibility, however it affects the response time. Additionally, cross-interactions of variables were analyzed and lead to a design recommendation for immersive industrial applications. Y1 - 2021 UR - https://www.researchgate.net/profile/Rosilius_Maximilian/publication/355896115_Evaluation_of_Visual_Requirements_and_Software-Design_for_Immersive_Visibility_in_Industrial_Applications/links/6189827807be5f31b7591290/Evaluation-of-Visual-Requirements-and-Software-Design-for-Immersive-Visibility-in-Industrial-Applications.pdf SP - 234 EP - 239 ER - TY - JOUR A1 - Wilhelm, Markus A1 - Lotter, Frank A1 - Scherdel, Christian A1 - Schmitt, Jan T1 - Advancing Efficiency in Mineral Construction Materials Recycling: A Comprehensive Approach Integrating Machine Learning and X-ray Diffraction Analysis JF - Buildings N2 - In the context of environmental protection, the construction industry plays a key role with significant CO2 emissions from mineral-based construction materials. Recycling these materials is crucial, but the presence of hazardous substances, i.e., in older building materials, complicates this effort. To be able to legally introduce substances into a circular economy, reliable predictions within minimal possible time are necessary. This work introduces a machine learning approach for detecting trace quantities (≥0.06 wt%) of minerals, exemplified by siderite in calcium carbonate mixtures. The model, trained on 1680 X-ray powder diffraction datasets, provides dependable and fast predictions, eliminating the need for specialized expertise. While limitations exist in transferability to other mineral traces, the approach offers automation without expertise and a potential for real-world applications with minimal prediction time. KW - Building and Construction KW - Civil and Structural Engineering KW - Architecture Y1 - 2024 U6 - https://doi.org/10.3390/buildings14020340 SN - 2075-5309 VL - 14 IS - 2 PB - MDPI AG ER - TY - JOUR A1 - Wilhelm, Markus A1 - Lotter, Frank A1 - Scherdel, Christian A1 - Schmitt, Jan T1 - Advancing Efficiency in Mineral Construction Materials Recycling: A Comprehensive Approach Integrating Machine Learning and X-ray Diffraction Analysis JF - buildings N2 - In the context of environmental protection, the construction industry plays a key role with significant CO2 emissions from mineral-based construction materials. Recycling these materials is crucial, but the presence of hazardous substances, i.e., in older building materials, complicates this effort. To be able to legally introduce substances into a circular economy, reliable predictions within minimal possible time are necessary. This work introduces a machine learning approach for detecting trace quantities (≥0.06 wt%) of minerals, exemplified by siderite in calcium carbonate mixtures. The model, trained on 1680 X-ray powder diffraction datasets, provides dependable and fast predictions, eliminating the need for specialized expertise. While limitations exist in transferability to other mineral traces, the approach offers automation without expertise and a potential for real-world applications with minimal prediction time. Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:863-opus-56030 VL - 14 IS - 2 PB - MDPI ER -