Abstract
Digitalization slowly but steadily transforms the modern production cells by adding an IT layer to their structure, which, subsequently, leads to the use of cyber-physical systems (CPS) as production cell components. Digital representation of those components through the definition of the corresponding description models starts in the early design phase of the engineering process. The model-based systems engineering (MBSE) approach promotes increased reusability of component models and allows an automatic generation of the system of interest model and its validation through simulation, which results in a shorter design phase. However, existing solutions for CPS description models contain mainly skills and functionalities of the component from a task-oriented perspective to achieve platform-independent code generation for the components control. Semantic information in the skills description usually is completely absent or present only to the extent, which is not enough to match the corresponding components to the product requirements and necessary production processes. Instead, production cell components are manually assigned to the manufacturing processes, which are required for the product. This manual step hinders the automatic production cell model generation based on the product requirements. To allow automatic matching from product descriptions to specific CPSs, a new approach to CPS description is necessary. This description should allow the entirely automatic generation of production cell models through the matching of CPS skills and product requirements. In this paper, a semantic metamodel for CPS is presented. This metamodel enriches CPS models with high-granularity graph-based skill descriptions, which, in combination with the appropriate manufacturing process descriptions, allow automatic matching for production planning and further simulation and ontimization.
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