Converging material flow simulation models as digital twins of production systems

• Applicant: Professorin Dr.-Ing. Gisela Lanza
• Subject Area: Production Systems, Operations Management, Quality Management and Factory Planning
• Term: since 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 535966478

In an increasingly volatile market environment, production companies must constantly adapt to new environmental conditions, react to external events and evaluate alternative courses of action under great time pressure and uncertainty. This can be efficiently and precisely supported by a material flow simulation model of the production system, which can handle the complex interactions, dynamic behavior and stochastic processes in the system. Since the model maintenance required to keep such a simulation model available with the required high accuracy is very labor-intensive and, in particular, know-how-intensive, this shall be automated in this project. To this end, procedures for using real production data (that is increasingly available because of digitalization) for the automated adaptation of the model to reality (the so-called convergence) are developed. The conventional simulation model thus becomes a digital twin of the production system. For this purpose, first a holistic concept in which, in addition to the overall procedure, the initial state of the digital twin development, the manually created seed model, is defined, must be developed. Based on this, the necessary methods for data collection and processing must be described. Therefore, systematics for the definition of numerous setup parameters, such as the validation metrics used, threshold values and the update period length, are to be developed. Since in practice simulation models are often created in the planning phase of production systems and digital twins should accompany the entire life cycle of the production system, the transition from the planning to the operating phase of the digital twin is also being investigated in the research project. The procedure and its algorithms will be implemented and tested for exemplary use cases from industry and the institute's own learning factory. In this way, the behavior of a digital twin after implementation can be examined in greater detail in order to gain further insights into its development and use.

• DFG Programme: Research Grants