The third dataset dedicated to the Open Guided Waves platform aims at carbon fiber composite plates with an additional omega stringer at constant temperature conditions. The two structures used in this work are representative for real aircraft components. Comprehensive measurements were recorded in order to study (I) the impact of the omega stringer on guided wave propagation, and (II) elliptical reference damages of different sizes located at three separate positions on the structure. Measurements were recorded for narrowband excitation (5-cycle toneburst with varying carrier frequencies) and broadband excitation (using chirp waveforms). The paper presents the results of a technical validation including numerical modelling, and enables further research, for example related to probability of detection (POD) analysis.

Fibers made from biopolymers are one solution for conserving both resources and the environment. However, these fibers currently have limited strengths, which limit their use for textile applications. In this paper, a biopolymer stereocomplex poly(-lactide) (scPLA) formation on a technical scale of high-molecular-weight poly(D-lactide) (PDLA) and poly(L-lactide) (PLLA) is presented. This scPLA material is the basis for further research to develop scPLA yarns in melt spinning with technical strengths for technical application. scPLA is compared with standard and commercially available semi-crystalline PLA for the production of fibers in melt spinning (msPLA) with textile strengths. Differential scanning calorimetry (DSC) gives a degree of crystallization of 59.7% for scPLA and 47.0% for msPLA. X-ray diffraction (XRD) confirms the pure stereocomplex crystal structure for scPLA and semi-crystallinity for msPLA. scPLA and msPLA are also compared regarding their processing properties (rheology) in melt spinning. While complex viscosity of scPLA is much lower compared to msPLA, both materials show similar viscoelastic behavior. Thermal gravimetric analysis (TGA) shows the influence of the molecular weight on the thermal stability, whereas essentially the crystallinity influences the biodegradability of the PLA materials.

Moisture diffusion in carbon fiber composites changes the mechanical properties of the composite. Therefore, a monitoring method of the actual content of moisture in the composite is important. However, at the moment there are no online methods established. A common method is the measurement of the mass changes due to water uptake. This method is not suitable for online monitoring of a real composite part in service. We demonstrate that miniaturized flexible interdigital sensors are suitable for moisture measurement inside the carbon fiber composite. These sensors are directly integrated inside the composite. It was already demonstrated that these can be successfully used for resin-curing monitoring as a primary application. Here we demonstrate that the same sensors are also suitable for moisture measurement inside the material. In order to do so, we expose samples with and without integrated sensors to hot-wet conditions and measure the dielectric changes with the sensors and the mass gain. The moisture concentration and the measured admittance can be directly correlated to each other. This demonstrates that the sensors can be used for moisture measurement as a secondary application. In addition, it is shown that the sensors have the potential to measure the moisture locally inside the material.

Ultrasonic welding of fibre-reinforced thermoplastics is a joining technology with high potential for short welding times and low energy consumption. While the majority of the current studies on continuous ultrasonic welding have so far focused on woven reinforcements, unidirectional materials are preferred for highly loaded aerospace components due to their better mechanical performance. Therefore, this paper investigates the influence and interdependence of the welding speed, amplitude, and energy director thickness on the weld quality of adherends made of unidirectional composites. The quality of the welded joints is assessed by a single-lap shear strength and fracture surface analysis complemented by the microscopic analysis of cross-sections and comparison to a co-consolidated reference. The results showed that the welding process is highly affected by changing welding speeds for a given amplitude. Furthermore, while lower amplitudes lead to significant scatter in the welding quality, higher amplitudes result in increased heating rates and a fully molten energy director even for high welding speeds. Nevertheless, insufficient consolidation at high welding speeds results in porosity in the weld line. Finally, it was observed that thicker, and therefore more compliant, energy directors lead to more uniform melting of the energy director and less deviation in the weld quality for a wider range of welding speeds.

Machine learning techniques such as deep learning have already been successfully applied in Structural Health Monitoring (SHM) for damage localization using Ultrasonic Guided Waves (UGW) at various temperatures. However, a common issue arises due to the time-consuming nature of collecting guided wave measurements at different temperatures, resulting in an insufficient amount of training data. Since SHM systems are predominantly employed in sensitive structures, there is a significant interest in utilizing methods and algorithms that are transparent and comprehensible. In this study, a method is presented to augment feature data by generating a large number of training features from a relatively limited set of measurements. In addition, robustness to environmental changes, e.g., temperature fluctuations, is improved. This is achieved by utilizing a known temperature compensation method called temperature scaling to determine the function of signal features as a function of temperature. These functions can then be used for data generation. To gain a better understanding of how the damage localization predictions are made, a known explainable neural network (XANN) architecture is employed and trained with the generated data. The trained XANN model was then used to examine and validate the artificially generated signal features and to improve the augmentation process. The presented method demonstrates a significant increase in the number of training data points. Furthermore, the use of the XANN architecture as a predictor model enables a deeper interpretation of the prediction methods employed by the network.

Premature rain erosion damage development at the leading edges of wind turbine rotor blades impair the efficiency of the turbines and should be detected as early as possible. To investigate the causes of premature erosion damage and the erosion evolution, test specimens similar to the leading edge of a rotor blade were modified with different initial defects, such as voids in the coating system, and impacted with waterdrops in a rain erosion test facility. Using CT and XRM with AI-based evaluation as non-destructive measurement methods showed that premature erosion arises from the initial material defects because they represent a weak point in the material composite. In addition, thermographic investigations were carried out. As it shows results similar to the two lab-based methods, active thermography has a promising potential for future in-situ monitoring of rotor blade leading edges.