Simulations offer possibilities for the investigation of production processes. They depict various aspects of the production process and the associated production systems. However, a single simulation is often not sufficient to obtain a comprehensive understanding of certain process settings. Instead, a combination of different simulations is required, e.g., when the results of one simulation serve as input parameters for another, resulting in a sequence of simulations. Manual planning of these sequences is considered a labor-intensive task that requires careful consideration of factors such as simulation time, simulation cost and quality of simulation results to select the optimal simulation scenario for a given request. The SiS information model represents simulations, their ability to generate specific knowledge and their respective quality criteria and serves as a basis for the automatic generation of simulation sequences.
Reif, Jonathan; Jeleniewski, Tom; Fay, Alexander (2023): An Approach to Automating the Generation of Process Simulation Sequences. In: 2023 IEEE 28th International Conference on Emerging Technologies and Factory Automation (ETFA). 2023 IEEE 28th International Conference on Emerging Technologies and Factory Automation (ETFA). Sinaia, Romania, 12.09.2023 - 15.09.2023: IEEE, S. 1–4. 10.1109/ETFA54631.2023.10275718
Reif, J.; Jeleniewski, T.; Köcher, A.; Frerich, T.; Gehlhoff, F. and Fay, A. (2024). Semantic Capability Model for the Simulation of Manufacturing Processes. In Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD, ISBN 978-989-758-716-0, ISSN 2184-3228, pages 39-50.