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.
In the quick check, historical data from purchasing was analyzed and dashboards were designed to enable more effective supplier monitoring.
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.
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.
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.
Outsourcing of compute-intensive operations to a 5G edge cloud.
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.
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.
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.