One development in the course of industrial digitalisation, which is being driven forward particularly in the German-speaking countries and will gain in importance in the future, is that of the Asset Administration Shell (AAS) [1]. The present work deals with the distribution, instantiation and use of those AASs for assets within the domain of a fluid power engineering application, which by themselves do not currently have the corresponding hardware and software for harbouring an AAS. The Business to Manufacturing Markup Language (B2MML) is used here for the deployment and instantiation of these AASs. In the present work, B2MML is extended with the concept definitions of AAS using its enhancement mechanisms. The distribution of the AASs to computing resources then takes place using B2MML and its transaction definitions. Furthermore, B2MML is used in a submodel to orchestrate process queues. In the course of the FL4 research project, B2MML is integrated as a partial model into the AAS and is used for orchestrating the Plug-and-Produce processes one the business and on subordinate levels.

Die Bereitstellung von Benutzungsschnittstellen (UI) per Industrie 4.0 Komponenten und ihrer Verwaltungsschalen (AAS) setzt den Entwurf eines neuen UI-Teilmodells voraus. In Abhängigkeit des geplanten Anwendungsfalls und des Nutzungskontexts muss das Teilmodell in der Lage sein mehrere UI-Fragmente (Varianten des UIs) bereitzustellen, die in UI-Lösungen eingefügt werden können. Dafür ist die Identifikation relevanter Merkmale und die Spezifikation einer Teilmodellstruktur nötig, um die Fragmente beschreiben und später das richtige auswählen zu können. Für den Einsatz des entworfenen Teilmodells in einem Plug-and-Produce-Szenario, müssen diese Merkmale formal spezifiziert sein, um durch automatische Werkzeuge korrekt interpretiert werden zu können. In diesem Beitrag werden der Entwurf eines UI-Teilmodells, ein Katalog von UI-Fragment-Merkmalen sowie eine erste Fallstudie zu deren Anwendung in einem industriellen Szenario vorgestellt.:Kurzfassung 1. Einleitung 2. Anforderungen und Identifikation relevanter Merkmale 2.1. Akteure und Anforderungen an UI-Fragment-Merkmale 2.2. UI-Fragment-Merkmale 2.2.1. Anwendungsfallmerkmale (Use-Case Properties) 2.2.2. Gestaltungsmerkmale (Design Properties) 2.2.3. Technische Merkmale (Technical Properties) 2.3. Anmerkungen zu den UI-Fragment-Merkmalen 3. Stand der Technik 3.1. Komponentenbasierte Bereitstellung von UI-Fragmenten 3.2. Teilmodellspezifikation 4. Entwurf des UI-Teilmodells 4.1. Strukturentwurf des UI-Teilmodells 4.2. Katalogspezifikation für Merkmale 5. Fallstudie 6. Zusammenfassung und Ausblick 7. Danksagung Literatur

<p dir="ltr">Asset traceability systems support sustainable value creation. Use case scenarios include the transition from a linear to a circular economy (CE) and legislative initiatives in Europe and North America. Traceability systems are needed to consistently link physical assets with the corresponding digital life cycle data. However, there is a lack of system architectures for consistent asset life cycle traceability. Therefore, the work proposes a traceability system architecture using digital product passports (DPPs) and fingerprint (FP) technology. By providing asset related data, DPPs increase the transparency across value chain partners. The system architecture uses the Asset Administration Shell (AAS) to create interoperable and standardized DPPs. Besides, consistent product identification (ID) and unique (single occurrence) identifiers are a prerequisite for effective traceability systems. Using natural markers to identify assets can enhance consistent asset traceability in sustainable supply chains. When using FP technology, the inherent surface structure of an asset is captured by an imaging system and then compressed into a digital asset fingerprint. Since assets are not artificially marked, the work investigates the use of Bounding Symbols (BSs) to locate an asset’s fingerprint Region of Interest (ROI). Furthermore, four fingerprint creation algorithms are compared and evaluated regarding their feasibility for asset life cycle traceability. The research validates the proposed system architecture in an experimental setup by using aluminum raw castings (medallions) as the investigated asset type. Key findings include the successful identification of 80 medallions with a 100% success rate. The related fingerprint information was stored in a DPP as an AAS submodel.</p>