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 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.

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.

In the CoGeP project, the Fraunhofer IEM is specifically focussing on semi-automated robots: this makes it possible to exploit the advantages of automation while at the same time ensuring the versatility of the testing systems. Companies should be able to integrate cobot workstations into existing manufacturing processes cost-effectively and with little effort, allowing them to change their production at short notice or adapt it to small quantities through to the production of individual items. Based on a holistic system model, the IT architecture of the future system will enable interfaces and the connection of different robots and test systems and software.

With "Miele@home technology", numerous appliances can be networked and integrated into smart home applications. Access via the Internet in particular requires special protection, for example to ward off hacker attacks. For this reason, Miele decided to have the technology's security concepts comprehensively tested by Fraunhofer IEM early on in the development process. This ensures that the highly sensitive data, which requires protection, cannot fall into the hands of third parties and that the security of the devices is guaranteed.

Air pollution is on the rise. To reduce CO² pollution, emissions must be lowered - also in shipping. The aim of the TrAM project is to develop a new class of emission-free, modular high-speed passenger ferries. The methods devised should reduce production and development costs and thus make such ships with electric propulsion competitive. The project will build the world's first zero-emission, electrically powered high-speed passenger ferry.