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Zukünftige Montagearbeitsplätze müssen veränderten Herausforderungen, wie z. B. der zunehmenden Anzahl von Mensch Roboter-Kollaborationen, gerecht werden. Die Virtual Reality (VR)-Technik bietet im Rahmen der Arbeitsplatzgestaltung neue Möglichkeiten, diesen veränderten Planungsherausforderungen gerecht zu werden. Die Ausarbeitung stellt eine Methode zur Bewertung des sinnvollen Einsatzes der VR-Technik für einen spezifischen Arbeitsplatz vor. Außerdem wird aufgezeigt, wie die VR-Technik in den Prozess der Arbeitsplatzgestaltung integriert werden kann.
The fifth mobile communications generation (5G) offers the deployment scenario of licensed 5G standalone non-public networks (NPNs). Standalone NPNs are locally restricted 5G networks based on 5G New Radio technology which are fully isolated from public networks. NPNs operate on their dedicated core network and offer organizations high data security and customizability for intrinsic network control. Especially in networked and cloud manufacturing, 5G is seen as a promising enabler for delay-sensitive applications such as autonomous mobile robots and robot motion control based on the tactile internet that requires wireless communication with deterministic traffic and strict cycling times. However, currently available industrial standalone NPNs do not meet the performance parameters defined in the 5G specification and standardization process. Current research lacks in performance measurements of download, upload, and time delays of 5G standalone-capable end-devices in NPNs with currently available software and hardware in industrial settings. Therefore, this paper presents initial measurements of the data rate and the round-trip delay in standalone NPNs with various end-devices to generate a first performance benchmark for 5G-based applications. In addition, five end-devices are compared to gain insights into the performance of currently available standalone-capable 5G chipsets. To validate the data rate, three locally hosted measurement methods, namely iPerf3, LibreSpeed and OpenSpeedTest, are used. Locally hosted Ping and LibreSpeed have been executed to validate the time delay. The 5G standalone NPN of Reutlingen University uses licensed frequencies between 3.7-3.8 GHz and serves as the testbed for this study.
Das regelmäßige Schmieren von Maschinen verhindert Schäden, reduziert Ausfallzeiten und vermeidet Reparaturkosten. Schmiervorgänge werden jedoch oft unzureichend dokumentiert. Für die Überwachung manueller Schmierprozesse an Maschinen wird daher eine Smart-Maintenance-Lösung aufgebaut. Zusätzlich wird eine intelligente Fettpresse als cyber-physisches System entwickelt. Dadurch lassen sich Schmiervorgänge transparent dokumentieren und Fehlschmierungen verhindern.
The technologies of digital transformation, such as the Internet-of-Things (IoT), artificial intelligence or predictive maintenance enable significant efficiency gains in industry and are becoming increasingly important as a competitive factor. However, their successful implementation and creative, future application requires the broad acceptance and knowledge of non-IT-related groups, such as production management students, engineers or skilled workers, which is still lacking today. This paper presents a low-threshold training concept bringing IoT-technologies and applications into manufacturing related higher education and employee training. The concept addresses the relevant topics starting from IoT-basics to predictive maintenance using mobile low-cost hardware and infrastructure.
Digital twins enable real-time monitoring, analysis, and optimization, thus enhancing efficiency and productivity in future factories. Nevertheless, the effective integration and innovative future utilization requires the broad acceptance and understanding among non-IT-related groups. This paper presents a practical training program that provides detailed guidance on how to create digital twins from real world data. It consists of modules utilizing 3D models in Unity with associated code, MQTT connectivity and network addressability. The integration of real-world data combined with a user interface that displays values in an integrated space enhances the overall usability. This integrative approach based on low-cost hardware and open source software makes the technology accessible to a wide audience and opens up new opportunities for advances in related higher education and employee training.
Mobile robots, particularly autonomous mobile robots (AMRs), play a transformative role in Intralogistics 4.0, enabling automated material transport and handling tasks. The increasing changeability of plant layouts and the dynamic manufacturing environment are critical drivers for their use. While automated guided vehicles (AGVs) are centrally controlled and rely on supporting infrastructure, AMRs can navigate without such guidance, thanks to their onboard sensors. Many studies have delved into the reasons and effects of using AMRs in intralogistics, underscoring their potential to optimise processes and enhance efficiency. This study aims to review existing literature on AMRs in intralogistics, unearthing the latest advancements and existing limitations. As a result, this review identifies five main research subjects: complexity of the environment, safety, resource scheduling and power consumption, artificial intelligence algorithms and interoperability. The study concludes by summarising these fields and emphasising the existing limitations and research gaps for further innovations in this area.
Concept for a low-cost implementation of automatic cycle time measurements in learning factories
(2024)
Cycle time optimization is a fundamental skill for manufacturing planners to avoid bottlenecks and thus increase throughput of production. A learning factory, which replicates real-world manufacturing scenarios, provides an ideal environment for students to acquire this essential skill. Traditionally, cycle times in these scenarios have been manually recorded using stopwatches. This practice has become increasingly outdated with the proliferation of Industry 4.0 and Internet of Things systems that automatically take these measurements in industries, which the learning factories are designed to emulate. However, the high costs and implementation efforts associated with these systems can pose significant challenges for learning factories to adapt.
To address these challenges, this paper proposes a cost-effective system for automatic cycle time measurements in learning factories. The system is composed of inexpensive and commercially available hardware such as microcontroller development boards, Radio-Frequency Identification (RFID) readers and a custom software based on open-source software that is free to use. It enables fast and economical retrofitting of existing production scenarios by equipping production stations with RFID readers and product trays with RFID tags.
The solution not only enhances the realism of learning factories in terms of cycle time measurements but also introduces the students to key Industry 4.0 concepts like automation, digitalization, and real-time data tracking. By integrating this affordable system, learning factories can better align their practices with industry standards, thereby improving the training quality and preparing students more effectively for the future manufacturing environment.
Mobile Roboter sind entscheidend für die automatisierte Intralogistik der Industrie 4.0. Eine sichere drahtlose Anbindung an Flottenmanager oder Steuerungssysteme ist essenziell. Private 5G-Campusnetzwerke mit lizenzierten Frequenzen gelten als vielversprechende Lösung. Aus diesem Grund beleuchtet der Beitrag die Grundlagen der 5G-Technologie für mobile Roboter sowie die aktuelle Leistungsfähigkeit von privaten 5G-Campusnetzwerken anhand erhobener Messungen.
5G-Campusnetze sind vielversprechende Umgebungen für industrielle Anwendungen in Produktion und Intralogistik. Diese erreichen jedoch bisher nicht die versprochenen Leistungen, um intralogistischen Anwendungen das volle Potenzial von 5G bieten zu können. Die im Rahmen des Projekts 5G4KMU erhobenen und in diesem Beitrag vorgestellten Leistungsmessungen dienen zur Evaluierung der derzeitigen Praxistauglichkeit von 5G-Campusnetzen.
The fifth generation of mobile communication (5G) is a wireless technology developed to provide reliable, fast data transmission for industrial applications, such as autonomous mobile robots and connect cyber-physical systems using Internet of Things (IoT) sensors. In this context, private 5G networks enable the full performance of industrial applications built on dedicated 5G infrastructures. However, emerging wireless communication technologies such as 5G are a complex and challenging topic for training in learning factories, often lacking physical or visual interaction. Therefore, this paper aims to develop a real-time performance monitoring system of private 5G networks and different industrial 5G devices to visualise the performance and impact factors influencing 5G for students and future connectivity experts. Additionally, this paper presents the first long-term measurements of private 5G networks and shows the performance gap between the actual and targeted performance of private 5G networks.
