From Research to Application: Projects at Fraunhofer EMFT

Gear oils make a significant contribution to minimising friction and thus ensure the safe operation of production facilities. In the Smart Gear project, researchers at Fraunhofer EMFT are developing solutions to detect a drop in the performance of gear oils with the help of sensor technology and machine learning methods and to predict when the oil needs to be changed.

For Bavaria, the agricultural sector has a significant economic importance with a turnover of around 121 billion euros per year. However, conflicts with the economic goals of farmers and the sustainable goals of environmental and animal protection initiatives occur from time to time due to the different interests involved. Especially the emission of ammonia gases leads to social discussions. Researchers at Fraunhofer EMFT are developing a more efficient measurement method for detecting ammonia gases in agriculture. The aim is to contribute to more environmentally friendly and animal-friendly agriculture.

Fraunhofer EMFT is conducting research on miniaturized suction probes for soil water analysis. The goal is to enable demand-based fertilization by real-time monitoring of the nitrate content in soil water.

Autonomous driving has a strong position in almost all concepts for tomorrow's mobility. Since automated driving no longer involves human intervention, the corresponding sensors and electronics must meet the highest reliability requirements: The system must be able to respond to unforeseen events as well as remain stable in case of faults or functional impairments - for example, delayed or missing information, the failure of a component or the loss of the electrical power supply.

Edge computing is considered a key for new IoT applications. In order to bring artificial intelligence into future edge products, Fraunhofer EMFT researchers are working together with Fraunhofer IIS and Fraunhofer IPMS as part of the EU ANDANTE project to develop innovative mixed-signal artificial neural network (ANN) accelerator with computation-in-memory (CIM) ability. These are intended to enable the construction of solid hardware and software platforms for AI application development.

Whether in automobiles – especially in the context of autonomous driving – or future industrial manufacturing: plugs and electrical connection technologies have a key role to play in digital networking. They are the main interface between machines, control units and data processing systems and so they provide the basis for the functionality, simple handling and reliability of automation technology.

In the project ‘Insect cells as sensors for environmental toxins’, Fraunhofer EMFT is developing a novel test method in which living insect cells are used as sensitive biosensors. This allows even the smallest toxic effects of pesticides to be detected quickly, without labelling and automatically. The aim is to identify risks at an early stage and support the development of bee-friendly pesticides – an important contribution to the protection of insects and biodiversity.

The aim of predictive maintenance is to maintain operating resources proactively and with foresight. This should reduce downtime and maintenance costs to a minimum. Researchers at Fraunhofer EMFT are testing new concepts that use machine learning methods to efficiently process even extremely heterogeneous data and make accurate maintenance predictions for production facilities.

Move2THz is an EU-funded research project to develop an innovative InP-on-silicon platform for energy-efficient sub-THz semiconductor technology. The aim is to strengthen Europe's technological leadership in 6G, radar, imaging, and photonics.

Aquaplaning and black ice turn streets into dangerous slides, which often leads to serious accidents. Within the framework of the high-performance center »secure intelligent systems« (LZSiS), Fraunhofer EMFT and Uedelhoven Studios are working together on a solution to the problem. A real-time warning system shall identify the potential danger due to water or ice on the road surface, thus enabling a predictive detection of the road conditions. Such an early warning system would increase the road safety for all traffic participants.