Mechatronic systems have to fulfill increasingly advanced functions and requirements to serve future customer needs and create reliable, resource-efficient and user-friendly systems. To realize tomorrow’s technical systems, solutions in context of self-optimization can be used. Thus, intelligent behavior can be integrated in technical systems. These systems are able to adapt their behavior autonomously and react to outer influences. The Leading-Edge Cluster “Intelligent Technical Systems OstWestfalenLippe (it’s OWL)” focuses on the described innovation leap from mechatronics to intelligent technical systems. Within this contribution we explain the capabilities of solutions in context of self-optimization on the example of machine learning methods. Furthermore, an approach for the identification of potentials for the integration of self-optimization in mechatronic systems will be introduced.
