A central focus of the AI Progress 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 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.
A sensor-guided TIG welding cobot automates labor-intensive weld seams on containments, reduces costs by 20 % and reduces the ergonomic burden on employees.
The aim is to investigate transfer learning and optical character recognition to improve the recognition of small objects, adapting existing models to new use cases.