Abstract
Digital twins are used to digitally map properties and states of real manufacturing systems. Based on relevant data-driven and physics-based digital models, digital twins are employed to monitor, analyze and predict aspects of the corresponding manufacturing system. Therefore, digital twins are used on different manufacturing levels, e.g. the factory, machine, or process level. This paper presents a concept for digital twins that are modeled by physics-based and data-driven models. Furthermore, this concept is validated with the help of a use case that controls an energy management system of a model-scale factory by finding the optimal time for charging and using the battery.
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