When designing complex mechatronic systems, a team of developers will be facing many challenges that can impede progress and innovation if not tackled properly. In meeting them simulation tools play a central role. Yet it is often impossible for a single developer to foresee the overall impact a design decision will have on the system and on the other domains involved. For this task multi-domain simulation tools exists, but because of its complexity and the different levels of detail that are needed, the effort to specify a complete system from scratch is very high. Another challenge is the selection of the most suitable solution elements provided by the manufacturers. Currently they are often chosen manually from catalogues. The development engineer is therefore usually inclined to employ well-known solution elements and suppliers. To tackle both challenges our aim is an increase in efficiency and innovation by means of generally available solution knowledge, such as well-proven solution patterns, ready-to-use solution elements, and established simulation models [1].

Our paper presents a tool-supported, sequential design process. From the outset, the comprehensive functional capability of the designed system is supervised by means of multi-domain simulation. At significant points in the design process, solution knowledge can be accessed as it is stored in ontologies and therefore available via Semantic Web [2]. Thus, one can overcome barriers resulting from different terminologies or referential systems and furthermore infer further knowledge from the stored knowledge. The paper focuses on an early testing in the conceptual design stage and on the subsequent semantic search for suitable solution elements. After the specification of a principle solution for the mechatronic system by combining solution patterns, an initial multi-domain model of the system is created. This is done on the basis of the active structure and of idealized simulation models which are part of a free library and associated with the chosen solution patterns via the ontologies. In further designing the controlled system and its parameters with the completed model, the developer defines additional criteria to be fed into the subsequent semantic search for solution elements. Information on the latter is provided by the manufacturers as well as detailed simulation models, which are used to analyze the functional capability of the concretized system. Therefore, the corresponding idealized models are replaced automatically with the parameterized models of the solution elements containing for example the specific friction model for the chosen motor. We show this process using the concrete example of a dough-production system. In particular, we focus on its transport system. Resulting requirements for the simulation models and their level of detail are expound, as well as the architecture and benefits of the ontologies.

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