Constantin Hildebrandt

Semantische Ermittlung kinematischer Fähigkeiten aus Anlagenplanungsdaten

Zusammenfassung Zur klaren, möglichst automatisierbaren Beantwortung der Frage, ob ein Fertigungssystem einen Produktionsauftrag realisieren kann, bedarf es einer möglichst rechnerbasiert auswertbaren Fähigkeitsbeschreibung. Für heutige Fertigungssysteme liegt eine solche Fähigkeitsbeschreibung meist nicht vor und müsste durch Auswertung vorliegender Engineering-Artefakte (z.u2009B 3D-CAD-Modelle) von Experten manuell erstellt werden. Der vorliegende Beitrag zeigt eine Methode auf, um die Lücke zwischen vorliegenden Engineering-Artefakten und notwendigen Fähigkeitsbeschreibungen zu schließen. Dabei wird anhand der Ermittlung kinematischer Fähigkeiten aus 3D-CAD-Daten ein Ansatz zur automatischen, regelbasierten Ermittlung der Fähigkeiten aufgezeigt und die Repräsentation der ermittelten Fähigkeiten in Form einer Ontologie zur weiteren, Plattform-übergreifenden Verwendung dargestellt.

Reasoning on Engineering Knowledge: Applications and Desired Features

The development and operation of highly flexible automated systems for discrete manufacturing, which can quickly adapt to changing products, has become a major research field in industrial automation. Adapting a manufacturing system to a new product for instance requires comparing the systems functionality against the requirements imposed by the changed product. With an increasing frequency of product changes, this comparison should be automated. Unfortunately, there is no standard way to model the functionality of a manufacturing system, which is an obstacle to automation. The engineer still has to analyze all documents provided by engineering tools like 3D-CAD data, electrical CAD data or controller code. In order to support this time consuming process, it is necessary to model the so-called skills of a manufacturing system. A skill represents certain features an engineer has to check during the adaption of a manufacturing system, e.g. the kinematic of an assembly or the maximum load for a gripper. Semantic Web Technologies (SWT) provide a feasible solution for modeling and reasoning on the knowledge of these features. This paper provides the results of a project that focused on modeling the kinematic skills of assemblies. The overall approach as well as further requirements are shown. Since not all expectations on reasoning functionality could be met by available reasoners, the paper focuses on desired reasoning features that would support the further use of SWT in the engineering domain.

An Ontological Context Modeling Framework for Coping with the Dynamic Contexts of Cyber-physical Systems.

Cyber-physical systems are highly collaborative by nature. At runtime these systems collaborate with each other to achieve goals that a single system could not achieve on its own. For example, autonomous vehicles can dynamically form convoys at runtime to facilitate higher traffic throughput and a reduction in CO2 emissions. While the importance of context documentation and analysis in system development is well known, current model-based engineering approaches struggle with the size and complexity of cyber-physical systems’ contexts. This is due to high variety and dynamicity of the contexts to be considered. For example, a convoy to be formed at runtime may consist of different numbers of participating vehicles. Additionally, it may face different neighboring, not partaking context systems (e.g., non-equipped vehicles, equipped but not participating vehicles) and situations (e.g., speed limits, road construction sites, emergency vehicles). This paper proposes a context ontology to cope with highly dynamic contexts of cyber-physical systems by explicitly differentiating between not only the system and its context but also between the cyber-physical system network the system participates in, as well as the system network’s context.