The “FCTRY CLD” is a highly reliable and real-time-capable local cloud solution specifically for manufacturing companies. In contrast to conventional cloud systems, it enables the operation of real-time-critical applications, such as the control of machines and robots, sensor data processing or the monitoring of production facilities, as microservices.

Our solution offers a user-friendly interface that is specifically tailored to the requirements of industry and enables simple management of applications. Thanks to TSN-capable communication interfaces, deterministic communication on the store floor is guaranteed. The real-time Kubernetes system in the “FCTRY CLD” enables the execution of time-critical microservices. In addition, the integrated energy monitoring function provides an overview of the system's current CO2 footprint.

The digital networking of machines and systems offers enormous potential for networked and adaptive production. However, it is still unclear to many companies which IT solutions are suitable and what benefits they actually offer.

The "ICNAP Explorer", initiated by the "International Center for Networked, Adaptive Production (ICNAP)", offers a free digital platform for manufacturing companies. It provides access to various ICNAP project results from the fields of machine learning, digital twins and data analytics. Companies can evaluate the performance of these technologies in their production environments through free test offers. With our system, even employees without special training are able to process data with machine learning models and suggest suitable algorithms. In addition, the ICNAP Explorer offers specialized tools such as the battery module configurator for industries such as electromobility.
Based on the secure Fraunhofer Edge Cloud, it fosters an active community of industry and research partners to advance intelligent production digitization. Companies have the opportunity to collaborate with technology providers and benefit from their expertise. Visit our booth and learn more about the ICNAP Explorer. You will get exclusive insights into all functions and their benefits for you.

Our system for automated defect detection sets new standards in quality assurance. It utilizes AI-based image processing to inspect weld seams on battery cell modules in real-time. The module is manually placed under a camera, and the system analyzes the images using a pre-trained deep learning model. Defects are instantly highlighted with color indicators: red signals flaws, while green confirms a flawless product.

The system enhances production efficiency through consistent, objective quality control and can be adapted for other products requiring visual surface inspections. With an increasing amount of data, the system continually improves, reducing the error rate over time. It can be operated locally or via cloud systems, with our "FCTRY CLD" solution offering secure and fast processing of sensitive production data on-site.

The result: a reliable and automated defect detection system that relieves skilled workers and ensures product quality during the manufacturing process.

Connectivity, edge computing, and precise control of automation systems are crucial for the competitiveness of many industrial sectors: they increase the efficiency of production processes, enable real-time communication between machines and systems, and create new opportunities for the integration of robotics into conventional manufacturing processes. With the "TSN-Kicker", the Fraunhofer IPT demonstrates the potential of new technologies such as TSN, artificial intelligence (AI) and Digital Twin for industry in a playful way.

The players on the TSN-Kicker are controlled by an artificial intelligence (AI) that has been trained using a digital twin of the real foosball table, allowing it to intuitively react to the opponent's moves and dynamically adjust its strategy. The rapid movements of the AI players are made possible by TSN, which ensures smooth transmission without delays. Don't miss this opportunity to test the future of industrial control and gain insight into the potential of these technologies.