Projects

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.

Development and innovation

Logistics and merchandise management

Marketing and communication

Production and quality management

Administration and organization

AI-supported evaluation of 3D point clouds of high-voltage switchgear

The quality of the scanned point clouds is crucial for the creation of digital twins for high-voltage switchgear. By using AI methods, the point clouds can be evaluated automatically.

AI industrialization support for galvanic processes

In the introduction of a new Cr(III)-based electroplating hard chrome plating process, machine learning is used to analyse complex process relationships and accelerate industrialization. The aim is to increase the efficiency and quality of the process by accurately predicting the electrolyte composition.

AI solutions in productive use

The initiative offers companies support in the sustainable integration of AI, promotes the exchange of experience and develops concrete solutions for optimizing operational processes with the help of AI technologies.

AI-optimized patient planning

As part of the Quick Check, existing approaches to efficient surgery planning and patient appointments were improved with as little waiting time as possible.

AI potential in automation in the service sector

Workshops are used to assess the potential of AI-based services in the service sector. A data map and priority list help to plan the implementation of intelligent automation solutions.

AI potential

The AI Progress Center develops solutions to optimize production at Ansmann AG, including predicting production times and detecting installation errors in battery cells.

Source: Fraunhofer IPA / Rainer Bez

AI-ready machining

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.

Decorative image AI progress center

AI use cases for the h-aero® platform

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.

KIAFAM (AI-based assistance functions in facility management)

Process data from facility management is analyzed with process mining in order to gain valuable insights into workflows and enable sustainable process optimizations.

Collaboration between man and machine 2.0

The final acceptance requirements for employees were reviewed with the help of UNAs. AI potentials were identified and suggested.