Engineering of Coupled Simulation Models for Mechatronic Systems

Simulation models play a crucial role in testing and fine-tuning of control systems for complex industrial systems. They are important parts of the sustainable, service-oriented manufacturing value chain by facilitating early and efficient defect detection and risk mitigation. However, the design phase of simulation models is time-consuming and error-prone, thus it should be improved to become more efficient. The design of simulation models for mechatronic systems has to cope with two basic challenges: (1) the heterogeneous nature of mechatronic systems, which are described with various overlapping engineering plans, and (2) the separation of monolithic simulations into distributed simulation modules, to better conquer the computational complexity of simulation models. This paper addresses both challenges: (a) we propose an application of semantic integration and linked data for sharing and capturing knowledge for simulation model design between various engineering plans; (b) we explain how to structurally connect simulation modules, which are dynamically coupled. The proposed method utilizes the extended bond-graph theory. The simulation modules work independently of each other but with interaction, similar to a multi-agent system. Since the computational execution of coupled simulations is a crucial obstacle especially for mechatronic systems, we show how performance analysis can significantly improve the definition of simulation workflows.