Fields of Technology

The Digital Innovation Lab focuses on three technology fields: Extended Reality, Modeling/Simulation, and Data Science/Artificial Intelligence. Practice has shown that the combination of these technologies leads to the most innovative solutions.

 

Extended Reality

The Digital Innovation Lab specializes in industrial applications of Extended Reality (XR). This research is conducted in close collaboration with corporate partners and is continuously tested in production cycles using in-house infrastructure.

Research is also conducted on the development of digital twins of machine tools, used in conjunction with VR for remote training and service. Virtual Reality combined with numerical simulation and Machine Learning enables projects that can be associated with the vision of the "Industrial Metaverse." This involves human-machine interaction in the virtual space, where machines are represented by digital twins realized with state-of-the-art numerical methods.

 

Modeling/Simulation

Virtual models of your products and production processes allow for a better understanding of processes through numerical simulations and computer-based optimizations. This approach enables the virtual examination of hundreds of variants, leading to significantly better results compared to manufacturing and testing only two or three variants.

The foundation for this lies in mathematical/physical models of reality, typically formulated with (partial) differential equations. We use commercially available simulation programs or develop customized solutions for your application.

 

Data Science/Artificial Intelligence

In the past decade, Data Science and Artificial Intelligence have gained significant importance. In the Digital Innovation Lab, we focus on data in the technical domain. We analyze your production processes using statistical process control methods, optimize your product designs using Design-of-Experiments (DoE) techniques, and develop algorithms for sensor data evaluation. We design decision-making systems with specified risks, find optimal solutions under uncertain conditions, or create complete AI systems to learn from your data.

Even "classic" topics like image processing have been revolutionized with neural networks and can be used, for example, for production monitoring. In the field of Data Science, our approach includes considering the origins of the data and not relying solely on "Black-Box methods."

We strive to take into account the physical processes underlying the data to achieve better results.