A central focus of the AI Innovation Center is direct cooperation with industrial companies: Feasibility studies (Quick Checks) and projects to develop initial prototypes (Exploring Projects) of AI applications. In addition, consortium projects and innovation networks are aimed at cross-company exchange.
Automatic model-free specification of packing patterns for shipping cartons.
Automation of the packaging process for various objects.
Flexible assembly of sensitive valve needles is optimized using deep reinforcement learning and physics simulation. The aim is to automate repetitive processes in order to increase efficiency and precision.
Automated handling of dimensionally unstable components optimizes assembly processes through AI-supported perception, handling and inspection to increase efficiency and quality in production.
The aim is to identify AI applications to improve production program planning. Employees receive training to develop use cases and support implementation.
Machine learning is used to detect errors in the cutting process at an early stage. Data from log files and service reports enable reliable prediction and minimization of machine failures.
The combination of AI methods and simulation techniques enables the precise monitoring and improvement of assembly processes in order to drive forward automation in enclosure assembly.
The final acceptance requirements for employees were reviewed with the help of UNAs. AI potentials were identified and suggested.
The semi-automated labeling tool can identify objects in images and draw bounding boxes around the defective areas using Segment Anything. Although the dataset is limited, the object detection model has shown promising results and is expected to be further improved with a larger dataset.
The solution for semantic navigation improves the environmental perception of autonomous robots through AI-supported object recognition. This enables robots to navigate more efficiently and react according to the situation.
The sensor-based adjustment of welding parameters optimizes the welding process through automatic control, improves seam quality and simplifies programming under varying production conditions.