Industry meets science

Training employees using virtual reality and augmented reality is more flexible and efficient than conventional training. In a doctoral research project at FAU, tools are being developed to create this type of training material more easily. (Image: Schaeffler Technologies AG & Co. KG)

Schaeffler and FAU work together on future-oriented concepts

Made in Germany is uncontested around the world as representing leading engineering, products and innovation. But globalisation and digital transformation are increasingly challenging the German economy to adapt in the international competition for markets and labour. Major companies are making significant investments in their own research and development facilities and collaborating with research institutions to ensure technological leadership. This research transfer benefits both sides: Companies can identify trends, market potential and innovations from external cutting-edge research and universities and research institutions can validate their findings in practical applications.

Collaboration between Schaeffler and FAU

Schaeffler is also taking this collaborative approach to research and development. Based in Herzogenaurach, Germany, Schaeffler is a global automotive and industrial supplier with approximately 92,500 employees worldwide at more than 170 locations in 50 countries. Its portfolio includes precision components and systems for engines, transmissions and chassis as well as rolling and plain bearing solutions. With innovative technology in the fields of e-mobility, digitalisation, and industry 4.0, Schaeffler is already making a decisive contribution today to mobility for tomorrow.

As part of the initiative “Schaeffler Hub for Advanced Research” – SHARE – Schaeffler has signed a cooperation agreement with Friedrich-Alexander-Universität-Erlangen-Nürnberg (FAU). “The aim of the agreement is to jointly work on research topics in the field of digitalisation and manufacturing processes and to test their industrial application at an early stage,” explains Prof. Dr. Marion Merklein, Chair of Manufacturing Technology and spokesperson for SHARE at FAU. The topics range from innovative materials and manufacturing processes to digital networking of machines and systems, and new training concepts.

Company on campus

As a company on campus, Schaeffler can draw on expert knowledge and research at FAU. For example, FAU is one of the ten leading research institutions in the field of additive manufacturing worldwide. In the recently extended Collaborative Research Centre 814, nine chairs of the Faculty of Engineering are working on new processes for the production of high-precision lightweight components made of plastic, metal and composite materials. Computer scientists are researching forward-looking concepts for the automatic processing of sensor data, the digital networking of machines and systems, and simulation models for design.

Close to science – the “Schaeffler Hub for Advanced Research” – SHARE at FAU. SHARE at FAU has its own offices at the Röthelheimcampus in Erlangen. (Image: Stefan Werner)

The company on campus concept is a key feature of the SHARE initiative. Schaeffler employees are based at the Röthelheim Campus in Erlangen with direct contact to professors, doctoral candidates and students. The partnership also allows both organisations to share expertise and infrastructure – knowledge networks as well as offices, conference rooms, test rigs and laboratories. Strategic research and development topics are selected by a steering committee consisting of four representatives each from Schaeffler and FAU. The committee is also responsible for setting milestones for the cooperation.

Doctoral research as core projects

Each SHARE at FAU project is based on a doctoral research project; related topics are also dealt with in Bachelor’s and Master’s theses. Four cooperation projects have already been launched:

Application of deep learning for signal analysis

Computer Science 14 (Machine Learning and Data Analytics Lab)

The aim of this project is to investigate and implement the benefits of deep learning methods for Schaeffler. This research has two specific use cases: Firstly, sensor data from production machines will be analysed to establish early indicators of limit values being exceeded and thus avoid unplanned maintenance and downtime. Related research will investigate whether more physical quantities can be extracted from a sensor to reduce the number of sensors employed in a machine. Secondly, characteristic loads on mechatronic components in vehicles will also be obtained by analysing sensor data. This could identify early indicators of malfunction, material fatigue and similar problems.

IT security in IoT and cloud use for embedded systems

Chair of Computer Science 12 (Hardware/Software Co-Design)

In a pilot project with Schaeffler, the Chair developed a platform for enabling machines and sensors to communicate directly. A major challenge facing the project was evaluating large amounts of data and computing control tasks in real time. As part of SHARE at FAU, the researchers are now investigating how sensor data can be stored and transferred securely. Researchers are also investigating how the platform’s hardware and software can be protected from attack, for example by malware that could be introduced into the system by loading configurations and updates.

Training and support through augmented and virtual reality

Chair of Computer Science 9 (Visual Computing)

“New Work” is an important concept for the transformation of the workplace at Schaeffler. This includes a thorough induction, training and professional development programme for employees. The large number of locations and Schaeffler’s international presence require considerable resources, either in terms of training personnel or scheduled downtime of production machines for the purpose of familiarisation. New virtual reality (VR) technologies that do not require access to production machines or training personnel on site could be used for greater efficiency and flexibility in training. The lack of haptic feedback from a real machine in VR could be compensated for with augmented reality (AR) training. The aim of this doctoral project is to develop tools that significantly simplify creating VR and AR training material.

Industrial value chain and product application potential of additive manufacturing

Chair of Photonic Technologies and Chair of Manufacturing Technology

What is the best way to integrate additive manufacturing into production and what are the possible applications? This question is being investigated in a doctoral research project at FAU.
(Image: Schaeffler Technologies AG & Co. KG)

The increasing digitalisation of production lines requires rethinking the design of value chains and upstream product development. This means not only modernising existing system technology and design methods but also integrating new technologies such as additive manufacturing into production. Before making the decision to integrate this technology into Schaeffler’s manufacturing processes, the possible applications in the value chain must be demonstrated. The objectives of the project are the development of a method for component identification in additive manufacturing and the development of hybrid process chains in order to fully exploit the potential of additive manufacturing at Schaeffler.

Worldwide innovation network

SHARE at FAU is the second partnership in Schaeffler’s SHARE programme. Schaeffler launched its first SHARE at the Karlsruhe Institute for Technology (E-mobility). Following FAU, Schaeffler set up international partnerships with Nanyang Technological University in Singapore (Urban mobility) and Southwest Jiaotong University in China (Interurban mobility). The innovation network promotes not only the exchange between industry and science, but also between the participating research institutions.

Read more about Schaeffler’s innovation network and SHAREs at the Schaeffler-Website.

Further information:

Prof. Dr. Marion Merklein
Phone: +49 9131 85-27140

Dr. Indra Pitz

Addition information