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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.
Gesellschaftliche und industrielle Trends im Zuge der Digitaliserung induzieren Veränderungsprozesse in der Industrie. Eine hohe Flexibilität und schnelle Entscheidungsfindungsprozesse stellen entscheidende Wettbewerbsvorteile für Unternehmen dar, um zukünftig erfolgreich am Markt agieren zu können. Um dies zu ermöglichen, müssen aggregierte Echtzeitdaten und Prognosen unmittelbar sowohl am Ort der Wertschöpfung als auch dezentral zur Verfügung stehen. Die Entscheidungsunterstützung mit Hilfe geeigneter Visualisierungen ist ein maßgeblicher Bestandteil von Shopfloor Management Systemen. Aufgrund der steigenden Anforderungen wurde das konventionelle und analoge Shopfloor Management in den letzten Jahren verstärkt durch digitale Lösungen ersetzt. Ein ganzheitlicher Shopfloor Management Ansatz, der die Trends und die daraus resultierenden Herausforderungen für die Industrie abdeckt, ist aktuell nicht vorhanden. Zukünftige Shopfloor Management Lösungen sollen diese Lücke schließen. Hierfür wurde ein ganzheitliches System entwickelt, welches Produktionsinformationen in Echtzeit unmittelbar am Shopfloor visualisiert, eine integrierte flexible Planung und Steuerung der Produktion beinhaltet sowie die Mitarbeiterbedürfnisse berücksichtigt. Eine flexible und individuelle Schichtplanung durch die Mitarbeiter und eine umfassende automatische Beanspruchungsbeurteilung sind dazu integriert worden. Zudem ermöglicht das System die Prognose und Visualisierung von Produktionsinformationen und unterstützt die Anwender bei der Durchführung strukturierter Shopfloor-Meetings. Dadurch werden Entscheidungen direkt auf den Ort der Wertschöpfung verlagert.
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.
Conventional production systems are evolving through cyber-physical systems and application-oriented approaches of AI, more and more into "smart" production systems, which are characterized among other things by a high level of communication and integration of the individual components. The exchange of information between the systems is usually only oriented towards the data content, where semantics is usually only implicitly considered. The adaptability required by external and internal influences requires the integration of new or the redesign of existing components. Through an open application-oriented ontology the information and communication exchange are extended by explicit semantic information. This enables a better integration of new and an easier reconfiguration of existing components. The developed ontology, the derived application and use of the semantic information will be evaluated by means of a practical use case.
Learning factories on demand
(2021)
Learning Factories are research and learning environments that demonstrate new concepts and technologies for the industry in a practical environment. The interaction between physical and virtual components is a central aspect. The mediation and presentation usually occur directly in the learning factory and are thus limited in time and concerning the user group. A learning factory- on-demand- can be provided by dividing and virtualizing the individual components via containers and microservices. This enables both local operation and operation hybrid cloud or cloud systems. Physical components can be mapped either through standardized interfaces or suitable emulators. Using the example of the Learning Factory at Reutlingen University (Werk150), it will be shown how different use cases can be made available utilizing software-based orchestration, thus promoting broader and more independent teaching.
Cyber-Physical Production Systems increasingly use semantic information to meet the grown flexibility requirements. Ontologies are often used to represent and use this semantic information. Existing systems focus on mapping knowledge and less on the exchange with other relevant IT systems (e.g., ERP systems) in which crucial semantic information, often implicit, is contained. This article presents an approach that enables the exchange of semantic information via adapters. The approach is demonstrated by a use case utilizing an MES system and an ERP system.
Cyber-Physical Production Systems increasingly use semantic information to meet the grown flexibility requirements. Ontologies are often used to represent and use this semantic information. Existing systems focus on mapping knowledge and less on the exchange with other relevant IT systems (e.g., ERP systems) in which crucial semantic information, often implicit, is contained. This article presents an approach that enables the exchange of semantic information via adapters. The approach is demonstrated by a use case utilizing an MES system and an ERP system.
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.
Framework for integrating intelligent product structures into a flexible manufacturing system
(2023)
Increasing individualisation of products with a high variety and shorter product lifecycles result in smaller lot sizes, increasing order numbers, and rising data and information processing for manufacturing companies. To cope with these trends, integrated management of the products and manufacturing information is necessary through a “product-driven” manufacturing system. Intelligent products that are integrated as an active element within the controlling and planning of the manufacturing process can represent flexibility advantages for the system. However, there are still challenges regarding system integration and evaluation of product intel-ligence structures. In light of these trends, this paper proposes a conceptual frame-work for defining, analysing, and evaluating intelligent products using the example of an assembly system. This paper begins with a classification of the existing problems in the assembly and a definition of the intelligence level. In contrast to previous approaches, the analysis of products is expanded to five dimensions. Based on this, a structured evaluation method for a use case is presented. The structure of solving the assembly problem is provided by the use case-specific ontology model. Results are presented in terms of an assignment of different application areas, linking the problem with the target intelligence class and, depending on the intelligence class of the product, suggesting requirements for implementation. The conceptual frame-work is evaluated by utilising a case study in a learning factory. Here, the model-mix assembly is controlled actively by the workpiece carrier in terms of transferring the variant-specific work instructions to the operator and the collaborative robot (cobot) at the workstations. The resulting system thus enables better exploitation of the poten-tials through less frequent errors and shorter search times. Such an implementation has demonstrated that the intelligent workpiece carrier represents an additional part for realising a cyber-physical production system (CPPS).