Project References

Satisfied customers: the best proof of successful collaboration

However young we may be as a Fraunhofer Institute, we can still look back on longstanding partnerships. Numerous customers from the areas of automation technology and mechanical and plant engineering as well as the automotive sector place their trust in our expertise in the fields of virtual prototyping, systems engineering and software development. Besides the cordial collaboration within our partnerships, our clients appreciate the transparent and reliable project management, as well as the customized solutions that are tailored to their needs.

Below you will find a selection of testimonials that relate to projects with companies of different sizes and from a variety of industries.

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  • Schleifprozess
    © David Gense / Fraunhofer IEM

    KI und Regelungstechnik – eine vertrauenswürdige Kombination.

    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.

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  • Industrieprojekt Safety&Security-Interplay
    © YK / Adobe Stock

    Industrieprojekt Safety&Security-Interplay

    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.

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  • Detail einer Maschine, welche goldenen Draht formt.
    © Hesse Mechatronics

    Maschinelles Lernen für bessere Produkte und Produktion

    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.

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  • Mann vor einer Maschine mit Greifarm.
    © Fraunhofer IEM / Wolfram Schroll

    Kollaborative Robotik für flexibles Schweißen

    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.

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  • Freisteller einer Berlineranlage auf weißem Hintergrund.

    Entwicklung eines Sensor-, Steuerungs- und Leitstandsystems für die Kleingebäcksproduktion

    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.

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  • Auf der rechten Seite vergrößert eine Lupe einen HTML Code. Auf der rechten Seite ist eine Grafik zur Veranschaulichung der Erkennung von Softwareschwachstellen zu sehen.
    © Fraunhofer IEM

    Ziel des IntelliSecTest-Projekts ist es, ein Softwarewerkzeug für effiziente, kostengünstige und leicht anwendbare Security-Tests von Softwareanwendungen zu entwickeln.

    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.

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  • Hand hält ein kleines Haus in die Kamera, welches umgeben von virtuellen Smart Home Symbolen ist.
    © thanmano / Adobe Stock

    Sichere Smart Home-Geräte in jedem Haushalt mit der Miele@home-Technologie.

    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.

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  • Ein Horizont über dem Meer bei Abendlicht. Darauf eine virtuelle Darstellung eines Schiffs.
    © TrAM

    Model-Based Systems Engineering ermöglicht emissionsarme modulare Passagierfähren

    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.

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  • Ausschnitt eines Computerbildschirms mit Quelltext.
    © achelos

    CogniCrypt unterstützt Entwickler bei der sicheren Benutzung von Kryptographie-Bibliotheken.

    Kryptographie ist die Schlüsselkomponente vieler Sicherheitsfunktionen in Software. Bei der Verwendung von Softwarebibliotheken, die kryptografische Funktionen bereitstellen, können jedoch leicht Fehler auftreten. Ein Missbrauch der Bibliotheken kann sogar die Sicherheit der zu entwickelnden Software gefährden. Gemeinsam mit dem Softwarehersteller achelos arbeitet das Fraunhofer IEM an einer Lösung für den richtigen Einsatz von Kryptographie-Bibliotheken. Das Werkzeug CogniCrypt ist so konzipiert, dass es an verschiedenen Stellen in den Softwareentwicklungsprozess integriert werden kann. Es verbessert die Sicherheit der Software, indem es den richtigen Einsatz von Kryptographie-Bibliotheken sicherstellt.

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  • Holztisch mit Tablet und ausgeschnittenen Icons aus Papier, nach denen eine Hand greift.
    © Connext Communication GmbH

    Modellbasierte Methoden für die Entwicklung sicherer Software.

    Connext Communication supports institutions and organizations in the social and healthcare sector with innovative IT solutions. Vivendi, its in-house software suite, provides tools for the management of outpatient, inpatient and semi-residential nursing and care facilities. In particular, the mobile use of the Android-based app and access to sensitive data requires innovative approaches to threat analysis in order to uncover any previously undetected vulnerabilities and take appropriate protective measures.

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