Project References

Working together with Düspohl, Fraunhofer IEM developed a novel grinding system incorporating AI-based software and, for the first time, automated a wholly manual production step at the mechanical engineering company. The robotic cell will autonomously learn how to grind the complex profile wrapping rollers. The RoboGrinder takes a hybrid approach to this challenge by combining powerful AI technologies with established control engineering methods. A machine learning module programmed specifically for the process also predicts any grinding errors. They are used to correct the basic control and enable the rubber-like rollers to be ground directly to size.

The entire automotive industry is facing major challenges: In the course of technological change, new or expanded interfaces between a vehicle and the outside world are continuously evolving - for example, in communication with charging stations or digital services in the cloud. They also open up the potential for cyberattacks. The aim of the research project was to support developers with an automated analysis of the influences of safety and security.

The digital transformation is promoting the creation of large amounts of data in companies' value networks, the data analysis of which using artificial intelligence offers enormous potential for transparent and optimised supply chain management (SCM). The aim of the MOVE research project is to enable companies to utilise new data-driven SCM approaches. The focus is on developing methods and tools for rapid implementation.

The use of machine learning makes it possible: knowledge is gained from data and introduced at all levels of the company's processes in order to generate added value. This allows design processes to be improved, product weaknesses to be identified and developments to be accelerated. The aim of the Machine Learning for Production and its Products (ML4Pro2) project is to make machine learning accessible to companies for intelligent products and production processes.

Predicting and even preventing machine downtime with data from production: This is the aim of the "BOOST 4.0 - Big Data for Factories" project, in which the Fraunhofer IEM and Benteler are setting up a pilot factory for industrial data analysis and utilization in the it's OWL cluster of excellence.

The introduction of automation solutions has not been appealing for the company so far due to low quantities, as these often require high investments. What is missing are efficient solutions that can also be used economically for batch sizes of 1. Fraunhofer IEM and MIT are demonstrating how Industry 4.0 in the form of flexible automation measures and collaborative robots (cobots) can also make its way into medium-sized companies in the TALENTED joint research project.

Arbeitswelt.Plus aims to develop and establish a regional competence centre for work research. By combining AI research and AI application as well as establishing transfer structures, the competence centre is intended to support companies in recognising and exploiting the potential of artificial intelligence for the world of work.

Nowadays, automated and very complex production plants handle the efficient production of cookies. The SensoBack research project aims to lower waste in cookie production and increase product quality. Fraunhofer IEM has established communication between sensors and systems and created technical communication standards.

Complex software systems are the basis of our networked industry and at the same time pose a security risk that is technically costly to control on a regular basis. The goal of the IntelliSecTest project is to develop a software tool for efficient, cost-effective and easy-to-use security testing of software applications. To this end, static and dynamic code analysis are to be combined with test case generation techniques to create a fully automated, intelligent testing software tool - a so-called fuzzer.

Complex technical systems and increasing customer requirements: Systems engineering (SE) offers companies - including small and medium-sized enterprises (SMEs) - tools and approaches to master these challenges and remain competitive in the future. Especially when systems engineering is designed holistically and integrated into the company organisation. The SE4OWL project is working on how this organisational and cultural change can succeed.