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The paradigmatic shift of production systems towards Cyber-Physical Production Systems (CPPSs) requires the development of flexible and decentralized approaches. In this way, such systems enable manufacturers to respond quickly and accurately to changing requirements. However, domain-specific applications require the use of suitable conceptualizations. The issue at hand, when using various conceptualizations is the interoperability of different ontologies. To achieve flexibility and adaptability in CPPSs though requires overcoming interoperability issues within CPPSs. This paper presents an approach to increase flexibility and adaptability in CPPSs while addressing the interoperability issue. In this work, OWL ontologies conceptualize domain knowledge. The Intelligent Manufacturing Knowledge Ontology Repository (IMKOR) connects the domain knowledge in different ontologies. Testing if adaptions in one ontology within the IMKOR provide knowledge to the whole IMKOR. The tests showed, positive results and the repository makes the knowledge available to the whole CPPS. Furthermore, an increase in flexibility and adaptability was noticed.
The functionality of existing cyber-physical production systems generally focuses on mapping technologic specifications derived from production requirements. Consequently, such systems base their conception on a structurally mechanistic paradigm. Insofar as these approaches have considered humans, their conception likewise is based on the structurally identical paradigm. Due to the fundamental reorientation towards explicitly human-centered approaches, the fact that essential aspects of the dimension "human" remain unconsidered by the previous paradigm becomes more and more apparent. To overcome such limitations, mapping the "social" dimension requires a structurally different approach. In this paper, an anthropocentric approach is developed based on possible conceptions of the human being, enabling a structural integration of the human being in an extended dimension. Through the model, extending concepts for better integration of the human being in the sense of human-centered approaches, as envisioned in the Industrie 5.0 conception, is possible.
Artificial intelligence is a field of research that is seen as a means of realization regarding digitalization and industry 4.0. It is considered as the critical technology needed to drive the future evolution of manufacturing systems. At the same time, autonomous guided vehicles (AGV) developed as an essential part due to the flexibility they contribute to the whole manufacturing process within manufacturing systems. However, there are still open challenges in the intelligent control of these vehicles on the factory floor. Especially when considering dynamic environments where resources should be controlled in such a way, that they can be adjusted to turbulences efficiently. Therefore, this paper aimed to develop a conceptual framework for addressing a catalog of criteria that considers several machine learning algorithms to find the optimal algorithm for the intelligent control of AGVs. By applying the developed framework, an algorithm is automatically selected that is most suitable for the current operation of the AGV in order to enable efficient control within the factory environment. In future work, this decision-making framework can be transferred to even more scenarios with multiple AGV systems, including internal communication along with AGV fleets. With this study, the automatic selection of the optimal machine learning algorithm for the AGV improves the performance in such a way, that computational power is distributed within a hybrid system linking the AGV and cloud storage in an efficient manner.
Modern production systems are characterized by the increasingly use of CPS and IoT networks. However, processing the available information for adaptation and reconfiguration often occurs in relatively large time cycles. It thus does not take advantage of the optimization potential available in the short term. In this paper, a concept is presented that, considering the process information of the individual heterogeneous system elements, detects optimization potentials and performs or proposes adaptation or reconfiguration. The concept is evaluated utilizing a case study in a learning factory. The resulting system thus enables better exploitation of the potentials of the CPPS.
The imparting of knowledge and skills in STEM education, especially under the influence of the Covid-19 pandemic, is increasingly taking place online and through digital formats. The partially asynchronous instruction eliminates, on the one hand, the social relation in the learning process and, on the other hand, the direct experience with physical objects. Here, the digital learning systems provide learning tools and controls to support the learning process on a general basis. Existing methods for simulating physical objects (digital twins) are also used to a minimal extent. The following approach presents a learning system framework that enables individualized learning, including all dimensions (social, physical). Implementing a concept that uses a personalized assistance system to orchestrate the individual learning steps enables efficient and effective learning. Applying the learning system framework exemplifies the STEM education at Reutlingen University in the logistics learning factory Werk150.
Human Digital Twin
(2022)
Man stelle sich vor, man könnte mit Unterstützung von künstlicher Intelligenz Spielabläufe von Bundesligaspielen oder sogar ganze WM-Partien simulieren. Oder der Trainer würde die Mannschaft im Endspiel anhand von Daten über den Gegner aufstellen und entsprechend psychologisch und physiologisch verschiedene Spielertypen auf den Platz schicken (vgl. Jahn). Ist das reine Fiktion? Nicht wirklich. Bereits heute werden die Leistungen von Sportlern immer häufiger digital analysiert und bewertet. Beispielsweise hat SAP eine Plattform entwickelt, die ein digitales Datenbild von Fußballspielern erstellt (vgl. SAP). Bei der letzten WM erhielt jeder Spieler über die neue Fifa Player App kurz nach der Begegnung präzise Statistiken zu seinen Leistungen während des Spiels (vgl. FIFA). Noch bessere Informationen sollen in Zukunft virtuelle Abbilder der Fußballspieler, digitale Zwillinge, liefern. Die dafür notwendigen Daten werden mithilfe von Sensoren im Trikot, in den Schuhen oder im Ball gewonnen. Durch erfassten Bewegungs- und Positionsdaten sowie Ballkontakten entsteht ein präzises Datenbild des Spielers. Solche Simulationen, die auf einem Modell des Menschen in der digitalen Welt beruhen, erfahren derzeit große Aufmerksamkeit in Wissenschaft und Praxis (vgl. van der Valk et al.). Nicht nur in der Fußballwelt, auch in der Medizin und im Kontext von Industrie 4.0 und Produktdesign, haben digitale menschliche Zwillinge das Potenzial, zu einer Schlüsseltechnologie zu werden.