Production and quality management

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.

FlexCab - flexible, intelligent cable assembly

A flexible, intelligent robot system for the automated assembly of high- and low-voltage connectors is being developed to reduce manual labor and increase efficiency.

High speed picking

The analysis of the patient journey at Klinikum Stuttgart identifies AI applications to optimize care, increase employee satisfaction and improve economic and ecological criteria.

Instant calculation of CNC components

Algorithms learn from existing production data and CAD geometries, estimate component complexity in real time, improve cost transparency, automate costing and relieve teams of manual assessments and time-consuming customer communication on a day-to-day basis.

Jewelry image AI-based image recognition

AI-based valuation of returns when renting out valuables

In the sharing economy, efficient returns pose a major challenge. This can be largely automated through the use of AI.

AI-based print image control for effervescent tablet tubes at Jaco

The project developed an AI-based, reliable and fully automated solution for print image monitoring. This reduces false alarms by an average of 85.7% through anomaly detection.

AI bending tool selection

Creation of a labeled data set for the regression of quality parameters with a neural network. AI-supported tool selection and automated machine parameterization enable efficient, low-disruption production and shorter delivery times.

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 potential

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

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.