Experimental-numerical approach to detect manufacturing- and load-induced damages of fiber-metal laminates based on altered vibration and damping behavior

• Applicants: Professorin Dr.-Ing. Luise Kärger; Dr.-Ing. Wilfried Liebig
• Subject Area: Lightweight Construction, Textile Technology ; Polymeric and Biogenic Materials and Derived Composites
• Term: since 2025
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 555131611

Fiber-metal-laminates allow to combine the advantages of metals and conventional fiber-reinforced polymers, thus exceeding the properties of monolithic materials. Especially, higher specific strength and increased fatigue resistance can be achieved. Compared to conventional metals, fiber-metal-laminates feature higher fracture toughness and tolerance to damage, making these laminates predominantly applicable in the aviation industry. There however, possible defects within the material are of critical importance. Occurring defect are differentiated into manufacturing and loading induced defects, which both influence a part’s mechanical performance and its continued use. Non-destructive detection of defect is often done using ultrasound procedures, which, however, are limited in their applicability to fiber-metal-laminates due to the high number of interfaces in these materials. Considering conventional fiber-reinforced polymers, defects are often detected by the damaged material’s deviating vibration and damping characteristics. This project aims at the development of an experimental-numerical methodology, which detects defects in fiber-metal-laminates based on deviations in vibration and damping characteristics. Besides manufacturing of undamaged and damaged materials, focus lies on the fundamental characterization and modeling of the present defects and damages. Validated parameterized models of miscellaneously damaged laminates allow for the investigation of the influence, which the individual types of damage have on the vibration and damping characteristics, such as natural frequencies, modal damping ratios and mode shapes. An artificial neural network is consequently trained to detect sources of these deviations. The quality of these predictions is assessed experimentally with the help of previously not considered states of damage. Furthermore, critical defect and damage sizes need to be determined which define the limits of the developed methodology. In a potential successor project, this methodology could be transferred to three-dimensional structures which are not within the scope of this project.

• DFG Programme: Research Grants