Nano-reinforcements in carbon fibre-reinforced polymer (CFRP) have proven to enhance the mechanical properties considering quasi-static, as well as fatigue load and, are a promising option with regard to CFRP performance optimisation. While general knowledge about the nanofiller content and its influence in CFRP is well documented, the use of alignment techniques for a specific orientation of the nano-reinforcements is still insufficiently studied. In this work, the influence of oriented carbon nanofibres (CNF) on the mechanical properties of bidirectional CFRP is investigated. CFRP was produced CNF-reinforced with and without orientation using a hot press, where an electric field was applied during curing. The laminates were characterised with respect to dispersion quality, pore volume, quasi-static properties (tensile and bending tests) and dynamic properties (fatigue tests). Electrical resistance measurement was applied together with digital image correlation and in situ computed tomography to generate knowledge about the fatigue-related damage evolution and evaluate the sensors for viable use of condition monitoring. Results show that the orientation of CNF has a significant impact on both quasi-static and fatigue properties, increasing the strength while reducing and slowing down the introduced damage. Orientation of nanofillers thus shows large optimization potential of mechanical properties of CFRP components.

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The aim of this work is the comparative characterization of the fatigue and damage behaviors of GFR-polyurethane and GFR-epoxy with application-relevant quasi-isotropic layer setup in the high cycle and very-high cycle fatigue regimes. Therefore, a high-frequency test method based on a resonant testing system (1 kHz) has been further developed and assessed with special consideration of self-heating. In intermittent test procedures, the damage state has been explored by in situ X-ray computed tomography analysis after certain numbers of cycles. It was shown that the overall damage state in the VHCF regime is reduced by a factor of three compared to the HCF regime and accompanied by delayed initiation and propagation of delamination. The latter was proven to be the main reason for a decreased inclination of the S/N-curve in the VHCF regime by 50-60%.

In order to achieve realistic simulations of the chip formation, high quality input data regarding the flow stress and damage behavior of the materials are required. The split Hopkinson pressure bar (SHPB) test setup for the characterization of highly dynamic material properties offers a suitable method for generating high strain rates, similar to those in the chip formation zone. However, the strain measurement in SHPB is usually performed by means of strain gauges. This leads to an unreliable evaluation of strain rate and flow stress/shear flow stress in the case of an inhomogeneous material deformation, since this method presents the total strain whilst excluding local deformations. Inhomogeneous deformations are induced deliberately in special shear specimens, as they are also observed in the investigated cylindrical specimens. The present work deals with this issue by providing two additional measurement techniques, which are applied in SHPB tests for cylindrical specimens made of AISI 1045 and Ti6Al4V. To enable a local strain resolution, digital image correlation (DIC) is applied to high-speed images of the deformation process. In order to allow for the detection of shear bands in the specimens, a deep-learning-based approach is presented. The two measurement methods (strain gauges and DIC) are compared and discussed. In particular, the findings regarding the inhomogeneous deformation of Ti6Al4V allow for future improvements in the result quality of SHPB tests. The presented algorithm shows promising predictions for shear band detection and creates the basis for an automated evaluation of shear sample results, as well as an AI-based pre-selection of frames for the DIC evaluation of SHPB tests.