Cognitive abilities allow robots to learn and reason from their environment. The gained knowledge can then be incorporated into the robot’s actions which in turn affect the environment. Therefore, a cognitive robot is no longer a static system that performs actions based on a pre-defined set of rules but a complex entity that dynamically adjusts over time. With this, challenges arise for production systems that need to observe and ideally anticipate the cognitive robot’s behavior. Often, digital twins are employed to test and optimize production control systems. This paper presents a generic approach to characterize, model and simulate learning processes and formalized knowledge in hybrid production systems assuming different station types with learning effects. Thereby, quantitative and qualitative learning processes are mapped including knowledge sharing and transfer across entities. A modular and parameterizable design enables the adjustment to different use cases. Eventually, the model is instantiated as a digital twin of a real production system for product disassembly employing cognitive-autonomous robots among human operators and rigidly automated machines. ... mehrThe model shows great potential to be integrated into test beds for planning and control systems of cognitive factories.