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 AI Innovation Center develops solutions to optimize production at Ansmann AG, including predicting production times and detecting installation errors in battery cells.
The integration of AI in the mechanical engineering industry requires standardized interfaces for reliable data transmission. A technology comparison identifies suitable protocols for optimizing process monitoring and control.
The AI Explorer identifies and prioritizes promising AI use cases for the h-aero® platform in order to make optimal use of resources and promote rapid implementation.
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.
An ML model predicts the change in deflection of bolts during the straightening process using measurement data and process parameters, enabling automation and quality assurance. Classification, in particular, is highly successful, predicting with high accuracy whether the bolts will be within tolerance after pressing.
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 in the thermal deformation of aircraft landing gear are detected by the SFS algorithm. The results can be further processed with AI algorithms and material defects can be visualized with suitable lighting.