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 final acceptance requirements for employees were reviewed with the help of UNAs. AI potentials were identified and suggested.
By building on the extensive design knowledge already available, AI can reduce the number of prototypes and tests required and thus also cut costs.
An AI-based system for automated tooth classification is being developed that analyzes images of teeth in different orientations and achieves an accuracy of 99.6 %, regardless of tooth color.
By calculating the angle between a cleaning tool and the orthogonal viewing angle of the bore, an automated cleaning process is to be made possible.
As part of the Quick Check, a concept for equipping machines for punching screw heads has been developed as a collaborative application. The application is designed as a mobile robot platform and by means of a bin-picking solution.
The defects of the thermal deformation of aircraft landing gear are detected by the SFS algorithm. The results of the SFS algorithm can be further processed using AI algorithms. Material defects can be visualized with suitable lighting.
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.