Results

This study identifies opportunites for the use of AI in optimizing process designs.

For many processes, companies require a secure transfer across company boundaries. In this project, we built a permissioned blockchain network that enables a secure transfer between two different companies.

In this project, active and semi-supervised learning principles were used to test efficient and interactive training of deep learning systems.

This project researched and evaluated approaches to image data synthesis for machine learning systems in manufacturing, which were implemented for two use cases: tool classification and classification of surface structures after laser polishing.

How can energy consumption be monitored and mapped for energy modelling?

Making the Digital Twin tangible: How can a hands on implementation be transferred in production?

How can you quickly integrate your machines on the shop floor?

How can wireless IIoT be self-sustainable?

How can businesses and companies benefit from industrialized AI?

In this project, a tool was developed to quantify and evaluate the quality of production data. The evaluation system focuses on time series and cross-sectional data and uses 41 criteria in four categories.