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AI has become a valuable trend in the mechanical engineering industry. The incorporation of AI algorithms into, for example, control systems for condition monitoring or process optimization, is currently an active topic of research and development. A typical AI algorithm requires data in a specific format for its training and performance. It comes into action after extracting and preprocessing information into learning datasets, and into recorded or even runtime data inputs for evaluation. In a production environment with a wide range of machines under operation, to collect data from each individual machine and make it available for the AI framework is a challenging task and a deciding factor.

Interoperability is an essential aspect of Industry 4.0 and is undergoing rapid implementation in the mechanical engineering industry. When machines must communicate with each other in a digital environment, there are some usual compatibility constraints.  These constraints may, for example, arise when machines of different types produced by different manufacturers from different countries come together.  Interoperability is the technology that removes the previously known constraints of compatibility in the digital collaboration of machinery. By standardized information models and interfaces, it enables a digital network of communication that forms the foundation for intelligent production.

Interoperable interfaces are designed with the goal of realizing plug-and-play and provide data and semantic knowledge in standard formats. This can facilitate the integration of AI and broaden its application range in the automation pyramid. This talk will explain how both technologies connect in realizing digitalization and automation in the industry.