The sensing material: AI helps to detect material damage

The University of Koblenz is researching the foundations of material-integrated damage monitoring with complex materials in the DFG Research Group FOR 3022. Thanks to innovative sensor nodes, smaller than a coin, hidden damage in fiber-metal laminates can be detected and evaluated. Since October 2024, the University of Koblenz has been the fifth location in the research group.

At first glance, the identification of component damage seems simple. But what happens if the damage is minor and hidden? Even minimal defects can lead to a total failure at a later point in time, especially in composite materials such as those used, for example, in wind turbines, where more and more wing breaks occur.

One solution could be to identify and evaluate hidden damage in materials using AI. This is the research goal of the interregional DFG Research Group FOR 3022 in the second funding period funded with over 3 million euros. The research group, which had its origin in the central scientific institution ISIS (Integrated Solutions in Sensorial Structure Engineering) of the University of Bremen under the leadership of Dr. Dirk Lehmhus, has now made progress in the monitoring of fiber-metal laminates with integrated sensors. In addition, the first results were published in renowned international journals. The DFG research project FOR 3022 involves partner institutions such as the Faserinstitut e.V. Bremen, the Institute for Microsystem Technology and Actuators (Bremen), technical mathematics in Bremen, the University of Siegen, the TU Braunschweig and the HSU Hamburg.

A special focus of the research work is on the development of measuring systems that can be integrated directly into the material.

Distributed sensor networks for automatic feature extraction in sensor signals.

The interdisciplinary cooperation of computer science, microsystem technology, electrical engineering and measurement technology is crucial for the success of the project. "Our research highlights the promising possibilities of AI methods. Our goal is to understand how important information can be generated from complex data and then used by people to make informed decisions." He adds: "From the laboratory to the practice, so that technology effectively supports people.“

Further information:

Information on TP 4 - 1st funding period

Information on TP 4 - 2nd funding period