Non-destructive detection of impact damage in CFRP components

Key Info

Basic Information

01.01.2015 to 31.12.2019
Organizational Unit:
Chair of Production Metrology and Quality Management, Model-based Systems
German Research Foundation DFG, Brazilian Federal Agency for Support and Evaluation of Graduate Education CAPES

Research partner

  • Universidade Federal de Santa Catarina (UFSC) – Florianopolis, Brazil



Dominik Wolfschläger



work Phone
+49 241 80 27337




Components made of carbon fibre reinforced plastics (CFRP) are becoming increasingly popular in various sectors. In particular, industries with low energy consumption, low weight or special mechanical properties are increasingly relying on CFRP in their material selection. Since the manufacturing process on the one hand and maintenance on the other hand is very complex due to the complex composition, it is important to prevent production-related faults and repair damage in the CFRP components used in order to conserve resources and reduce costs. The DFG-funded research project IDD Metro - Non-destructive Impact damage Detection on Carbon Fiber Reinforced Plastics, in cooperation with the Universidade Federal de Santa Catarina in Brazil, is therefore investigating how damage in finished CFRP components can be reliably detected and quantified.


Initially, the establishment of a database should make it possible to describe damage and defects unambiguously on the basis of their characteristics and thus enable classification, for example by means of learning procedures, on the basis of these characteristics. The development of multisensor systems is necessary because only the fusion of data from several different measuring methods allows a comprehensive description of the characteristics and only in this way a clear classification of potential defects is possible. In addition, new image processing algorithms and methods for the automated surface and depth determination of defects in CFRP components are being developed. Finally, it will be investigated whether the measurement process can be mapped by data-based models in addition to existing physical models.