TY - CHAP A1 - Ulmer, Jessica A1 - Braun, Sebastian A1 - Wollert, Jörg T1 - Adaptive VR-Produktionsumgebungen für Evaluations- und Schulungstätigkeiten T1 - Adaptive VR production environments for evaluation and training purposes T2 - Automation 2021: Navigating towards resilient Production N2 - Industrie 4.0 stellt viele Herausforderungen an produzierende Unternehmen und ihre Beschäf-tigten. Innovative und effektive Trainingsstrategien sind erforderlich, um mit den sich schnell verändernden Produktionsumgebungen und neuen Fertigungstechnologien Schritt halten zu können. Virtual Reality (VR) bietet neue Möglichkeiten für On-the-Job, On-Demand- und Off-Premise-Schulungen. Diese Arbeit stellt ein neues VR Schulungssystem vor, welches sich flexible an unterschiedliche Trainingsobjekte auf Grundlage von Rezepten und CAD Modellen anpassen lässt. Das Konzept basiert auf gerichteten azyklischen Graphen und einem Level-system. Es ermöglicht eine benutzerindividuelle Lerngeschwindigkeit mittels visueller Ele-mente. Das Konzept wurde für einen mechanischen Anwendungsfall mit Industriekomponen-ten implementiert und in der Industrie 4.0-Modellfabrik der FH Aachen umgesetzt. N2 - Industry 4.0 poses many challenges for manufacturing companies and their employees. Inno-vative and effective training strategies are needed to keep pace with rapidly changing produc-tion environments and new manufacturing technologies. Virtual reality (VR) offers new oppor-tunities for on-the-job, on-demand, and off-premise training. This work presents a new VR training system that can be flexibly adapted to different training objects based on recipes and CAD models. The concept is based on directed acyclic graphs and a level system. It allows a user-individual learning speed by means of visual elements. The concept was implemented for a mechanical use case with industrial components and implemented in the industry 4.0 model factory of the FH Aachen University of Applied Sciences. Y1 - 2021 SN - 978-3-18-092392-5 U6 - http://dx.doi.org/10.51202/9783181023921-55 SN - 0083-5560 N1 - 22. Leitkongress der Mess- und Automatisierungstechnik, 9. und 30. Juni 2021. SP - 55 EP - 64 PB - VDI CY - Düsseldorf ER - TY - CHAP A1 - Ulmer, Jessica A1 - Braun, Sebastian A1 - Cheng, Chi-Tsun A1 - Dowey, Steve A1 - Wollert, Jörg T1 - Adapting Augmented Reality Systems to the users’ needs using Gamification and error solving methods T2 - Procedia CIRP N2 - Animations of virtual items in AR support systems are typically predefined and lack interactions with dynamic physical environments. AR applications rarely consider users’ preferences and do not provide customized spontaneous support under unknown situations. This research focuses on developing adaptive, error-tolerant AR systems based on directed acyclic graphs and error resolving strategies. Using this approach, users will have more freedom of choice during AR supported work, which leads to more efficient workflows. Error correction methods based on CAD models and predefined process data create individual support possibilities. The framework is implemented in the Industry 4.0 model factory at FH Aachen. KW - Augmented Reality KW - Adaptive Systems KW - Gamification KW - Error Recovery Y1 - 2021 U6 - http://dx.doi.org/10.1016/j.procir.2021.11.024 SN - 2212-8271 N1 - Part of special issue: 54th CIRP CMS 2021 - Towards Digitalized Manufacturing 4.0 VL - 104 SP - 140 EP - 145 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Braun, Sebastian A1 - Cheng, Chi-Tsun A1 - Dowey, Steve A1 - Wollert, Jörg T1 - Performance evaluation of skill-based order-assignment in production environments with multi-agent systems JF - IEEE Journal of Emerging and Selected Topics in Industrial Electronics N2 - The fourth industrial revolution introduces disruptive technologies to production environments. One of these technologies are multi-agent systems (MASs), where agents virtualize machines. However, the agent's actual performances in production environments can hardly be estimated as most research has been focusing on isolated projects and specific scenarios. We address this gap by implementing a highly connected and configurable reference model with quantifiable key performance indicators (KPIs) for production scheduling and routing in single-piece workflows. Furthermore, we propose an algorithm to optimize the search of extrema in highly connected distributed systems. The benefits, limits, and drawbacks of MASs and their performances are evaluated extensively by event-based simulations against the introduced model, which acts as a benchmark. Even though the performance of the proposed MAS is, on average, slightly lower than the reference system, the increased flexibility allows it to find new solutions and deliver improved factory-planning outcomes. Our MAS shows an emerging behavior by using flexible production techniques to correct errors and compensate for bottlenecks. This increased flexibility offers substantial improvement potential. The general model in this paper allows the transfer of the results to estimate real systems or other models. KW - cyber-physical production systems KW - event-based simulation KW - multi-agent systems KW - digital factory KW - industrial agents Y1 - 2021 U6 - http://dx.doi.org/10.1109/JESTIE.2021.3108524 SN - 2687-9735 IS - Early Access PB - IEEE CY - New York ER -