TY - CHAP A1 - Ulmer, Jessica A1 - Wollert, Jörg A1 - Cheng, C. A1 - Dowey, S. T1 - Enterprise Gamification für produzierende mittelständische Unternehmen T2 - Shaping automation for our future: 21. Leitkongress Mess- u. Automatisierungstechnik : Automation 2020 : 30. Juni u. 01. Juli 2020 Y1 - 2020 SN - 978-3-18-092375-8 N1 - VDI-Berichte ; 2375 SP - 157 EP - 165 PB - VDI-Verlag CY - Düsseldorf ER - TY - JOUR A1 - Kunkel, Maximilian Hugo A1 - Gebhardt, Andreas A1 - Mpofu, Khumbulani A1 - Kallweit, Stephan T1 - Quality assurance in metal powder bed fusion via deep-learning-based image classification JF - Rapid Prototyping Journal Y1 - 2019 U6 - http://dx.doi.org/10.1108/RPJ-03-2019-0066 SN - 1355-2546 VL - 26 IS - 2 SP - 259 EP - 266 ER - TY - JOUR A1 - Zabirov, Alexander A1 - Schleser, Markus A1 - Bucherer, Sebastian T1 - Füge- und Dichtkonzept für einen Leichtbauverbrennungsmotor JF - adhäsion KLEBEN & DICHTEN Y1 - 2021 U6 - http://dx.doi.org/10.1007/s35145-021-0531-5 SN - 2192-8681 VL - 65 IS - 11 SP - 12 EP - 19 PB - Springer Nature CY - Cham 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 - BOOK A1 - Feuerriegel, Uwe T1 - Wärmeübertragung mit EXCEL und VBA: Wärmetechnische Berechnungen und Simulationen effektiv durchführen und professionell dokumentieren N2 - Dieses Lehrbuch vermittelt die Grundlagen der Wärmeübertragung sowie den Umgang mit EXCEL-VBA von der Erstellung von Makros bis zu benutzerdefinierten Funktionen. Es legt damit eine Basis für die schnelle und professionelle Durchführung von Berechnungen und Simulationen. Die angeleitete Erstellung von Berechnungsmodulen mit EXCEL und VBA aus allen wichtigen Bereichen der Wärmeübertragung bildet den inhaltlichen Schwerpunkt. Dazu zählen die stationäre Wärmeleitung und der stationäre Wärmedurchgang, die instationäre Wärmeleitung, der Wärmeübergang bei freier und erzwungener Konvektion sowie die Wärmestrahlung und der Wärmeübergang beim Kondensieren und Sieden. Soweit sinnvoll und möglich werden die Stoffwertekorrelationen und die Berechnungsvorschriften aus dem VDI-Wärmeatlas verwendet. Für ausgewählte Anwendungen werden zudem komplexere Auslegungen und Simulationen von Prozessen der Wärmeübertragung sowie von Wärmeübertragern erstellt. Die Zielgruppen: Studierende in Bachelor- und Masterstudiengängen, Praktiker im Engineering KW - Wärmeübertragung KW - Energietechnik KW - Excel und VBA KW - VDI-Wärmeatlas KW - Wärmeübertrager Y1 - 2021 SN - 978-3-658-35905-8 U6 - http://dx.doi.org/10.1007/978-3-658-35906-5 N1 - In der Bereichsbibliothek Eupener Straße unter der Signatur 21 WDW 39 vorhanden PB - Springer Vieweg CY - Wiesbaden ER - TY - CHAP A1 - Adenacker, J. A1 - Gerhards, Benjamin A1 - Otten, Christian A1 - Schleser, Markus T1 - Laserstrahlschweißen von Aluminium-Kupfer-Werkstoffkombinationen für die Elektromobilität T2 - DVS CONGRESS 2021 Y1 - 2021 SN - 978-3-96144-146-4 N1 - DVS CONGRESS 2021, 14. – 17. September 2021, Essen. Große Schweißtechnische Tagung 2021, DVS CAMPUS 2021. DVS Berichte, Band: 371 SP - 31 EP - 38 PB - DVS Media GmbH CY - Düsseldorf 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 - TY - CHAP A1 - Chavez Bermudez, Victor Francisco A1 - Wollert, Jörg T1 - 10BASE-T1L industry 4.0 smart switch for field devices based on IO-Link T2 - 2022 IEEE 18th International Conference on Factory Communication Systems (WFCS) N2 - The recent amendment to the Ethernet physical layer known as the IEEE 802.3cg specification, allows to connect devices up to a distance of one kilometer and delivers a maximum of 60 watts of power over a twisted pair of wires. This new standard, also known as 10BASE-TIL, promises to overcome the limits of current physical layers used for field devices and bring them a step closer to Ethernet-based applications. The main advantage of 10BASE- TIL is that it can deliver power and data over the same line over a long distance, where traditional solutions (e.g., CAN, IO-Link, HART) fall short and cannot match its 10 Mbps bandwidth. Due to its recentness, IOBASE- TIL is still not integrated into field devices and it has been less than two years since silicon manufacturers released the first Ethernet-PHY chips. In this paper, we present a design proposal on how field devices could be integrated into a IOBASE-TIL smart switch that allows plug-and-play connectivity for sensors and actuators and is compliant with the Industry 4.0 vision. Instead of presenting a new field-level protocol for this work, we have decided to adopt the IO-Link specification which already includes a plug-and-play approach with features such as diagnosis and device configuration. The main objective of this work is to explore how field devices could be integrated into 10BASE-TIL Ethernet, its adaption with a well-known protocol, and its integration with Industry 4.0 technologies. KW - 10BASE-T1L KW - Ethernet KW - Field device KW - Sensors KW - IO-Link Y1 - 2022 SN - 978-1-6654-1086-1 SN - 978-1-6654-1087-8 U6 - http://dx.doi.org/10.1109/WFCS53837.2022.9779176 N1 - 2022 IEEE 18th International Conference on Factory Communication Systems (WFCS), 27-29 April 2022, Pavia, Italy- PB - IEEE ER - TY - CHAP A1 - Ulmer, Jessica A1 - Braun, Sebastian A1 - Cheng, Chi-Tsun A1 - Dowey, Steve A1 - Wollert, Jörg T1 - Usage of digital twins for gamification applications in manufacturing T2 - Procedia CIRP N2 - Gamification applications are on the rise in the manufacturing sector to customize working scenarios, offer user-specific feedback, and provide personalized learning offerings. Commonly, different sensors are integrated into work environments to track workers’ actions. Game elements are selected according to the work task and users’ preferences. However, implementing gamified workplaces remains challenging as different data sources must be established, evaluated, and connected. Developers often require information from several areas of the companies to offer meaningful gamification strategies for their employees. Moreover, work environments and the associated support systems are usually not flexible enough to adapt to personal needs. Digital twins are one primary possibility to create a uniform data approach that can provide semantic information to gamification applications. Frequently, several digital twins have to interact with each other to provide information about the workplace, the manufacturing process, and the knowledge of the employees. This research aims to create an overview of existing digital twin approaches for digital support systems and presents a concept to use digital twins for gamified support and training systems. The concept is based upon the Reference Architecture Industry 4.0 (RAMI 4.0) and includes information about the whole life cycle of the assets. It is applied to an existing gamified training system and evaluated in the Industry 4.0 model factory by an example of a handle mounting. KW - Gamification KW - Digital Twin KW - Support System Y1 - 2022 U6 - http://dx.doi.org/10.1016/j.procir.2022.05.044 SN - 2212-8271 N1 - 55th CIRP Conference on Manufacturing Systems VL - 107 SP - 675 EP - 680 PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Dannen, Tammo A1 - Schindele, Benedikt A1 - Prümmer, Marcel A1 - Arntz, Kristian A1 - Bergs, Thomas T1 - Methodology for the self-optimizing determination of additive manufacturing process eligibility and optimization potentials in toolmaking T2 - Procedia CIRP N2 - Additive Manufacturing (AM) of metallic workpieces faces a continuously rising technological relevance and market size. Producing complex or highly strained unique workpieces is a significant field of application, making AM highly relevant for tool components. Its successful economic application requires systematic workpiece based decisions and optimizations. Considering geometric and technological requirements as well as the necessary post-processing makes deciding effortful and requires in-depth knowledge. As design is usually adjusted to established manufacturing, associated technological and strategic potentials are often neglected. To embed AM in a future proof industrial environment, software-based self-learning tools are necessary. Integrated into production planning, they enable companies to unlock the potentials of AM efficiently. This paper presents an appropriate methodology for the analysis of process-specific AM-eligibility and optimization potential, added up by concrete optimization proposals. For an integrated workpiece characterization, proven methods are enlarged by tooling-specific figures. The first stage of the approach specifies the model’s initialization. A learning set of tooling components is described using the developed key figure system. Based on this, a set of applicable rules for workpiece-specific result determination is generated through clustering and expert evaluation. Within the following application stage, strategic orientation is quantified and workpieces of interest are described using the developed key figures. Subsequently, the retrieved information is used for automatically generating specific recommendations relying on the generated ruleset of stage one. Finally, actual experiences regarding the recommendations are gathered within stage three. Statistic learning transfers those to the generated ruleset leading to a continuously deepening knowledge base. This process enables a steady improvement in output quality. KW - Additive manufacturing KW - Laser-Powder Bed Fusion KW - L-PBF KW - Binder Jetting KW - Directed Energy Deposition Y1 - 2022 U6 - http://dx.doi.org/10.1016/j.procir.2022.05.188 SN - 2212-8271 N1 - 55th CIRP Conference on Manufacturing Systems VL - 107 SP - 1539 EP - 1544 PB - Elsevier CY - Amsterdam ER -