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.
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.
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.
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.
Automated handling of dimensionally unstable components optimizes assembly processes through AI-supported perception, handling and inspection to increase efficiency and quality in production.