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.
The use of robots and AI methods is automating the portioning of meals in hospitals. This improves efficiency and relieves staff of monotonous tasks.
An AI-supported assistance system improves assembly processes through real-time feedback, minimizes error rates, relieves skilled workers and optimizes the final inspection to increase quality and competitiveness.
An EEG analysis system is being developed that combines electrophysiological measurements and AI algorithms to objectively classify the willingness to exert effort in medical reports.
An AI prototype uses traffic light logic to suggest the preferred time slots for scheduling appointment requests in order to achieve the most efficient capacity utilization possible. This is based on past appointment data and takes into account no-shows and appointment cancellations.
Creation of a labeled data set for the regression of quality parameters with a neural network. AI-supported tool selection and automated machine parameterization enable efficient, low-disruption production and shorter delivery times.
AI accelerator chips enable lower latencies and higher throughput rates for industrial applications. An analysis of the Hailo-8 AI chip shows great potential for increasing efficiency in machine control.
Automatic model-free specification of packing patterns for shipping cartons.
Automation of the packaging process for various objects.
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.