Interoperabilität realer und simulierter Produktionssysteme mittels OPC UA - Schmidl, Elisabeth; Wenk, Matthias; Russwurm, Eva; Franke, Jörg (Simulation in Produktion und Logistik 2021, 2021)
Simulation-based tools support developers in the engineering process, as they allow decision-relevant investigations to be carried out simulatively in advance. Some of these simulation studies require models that are as close to reality as possible. For their creation, data from real plants are necessary, which is associated with an increased manual acquisition and implementation effort. Furthermore, the different available simulation tools are suitable for different levels of detail. In order to use the synergies of the different tools, they have to be coupled, for which there are often no standardized interfaces yet. This paper presents a concept based on the OPC UA communication standard, which enables automated data transfer from real to simulated production plants, as well as flexible data exchange between different simulation tools<br

Reinforcement learning for energy reduction of conveying and handling systems - Schmidl, Elisabeth; Fischer, Eva; Steindl, Johanna; Wenk, Matthias; Franke, Jörg (Procedia CIRP, 2020)
One important reason for wasting energy in manufacturing processes is that individual plant components often consume too much energy despite not being needed for production or nothing being produced at that moment. The implementation of standby-strategies in the PLC to switch each component into an optimal energetic state is applied seldom due to the great programming effort. This is because there are many functional dependencies between the plant components and therefor the PLC program must be adapted manually to the respective manufacturing process. The presented solution shows an intelligent system, which can adapt to plant-specific process requirements and derive decisions about the optimal energetic state of each component autonomously. In this paper, we provide an approach that uses reinforcement learning algorithms, which train on virtual plant models. The results are verified on a conveying and handling system in our learning factory.
CRIS-Team Scopus Importer:2021-04-19
Keywords: Energy reduction; Reinforcement learning; Virtual commissioning