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In ultrasonic testing, the time of flight (ToF) of a signal can be used to infer material and structural properties of a test item. In dispersive media, extracting the bulk wave velocity from a received signal is challenging as the waveform changes along its path of propagation. When using signal features such as the first peak or the envelope maximum, the calculated velocity changes with the propagation distance. This does not occur when picking the signal onset. Borrowing from seismology, researchers used the Akaike information criterion (AIC) picker to automatically obtain onset times. In addition to being dependent on arbitrarily set parameters, the AIC picker assumes no prior knowledge of the spectral properties of the signal. This is unnecessary in ultrasonic through-transmission testing, where the signal spectrum is known to differ significantly from noise. In this contribution, a novel parameter-free onset picker is proposed, that is based on a spectral entropy criterion (SEC) to model the signal using the AIC framework. Synthetic and experimental data are used to compare the performance of SEC and AIC pickers, showing an improved accuracy for densely sampled data.
The aim of this work is to achieve reliable nondestructive evaluation (NDE) of adhesively bonded aerospace components by developing novel multidimensional data fusion techniques, which would combine the information obtained by ultrasonic and X-ray NDE methods. Separately, both NDE techniques have their advantages and limitations. The integration of data obtained from pulse echo immersion ultrasound testing and radiography holds immense potential to help improve the reliability of non-destructive evaluation. In this study, distinctive features obtained from single techniques, traditional ultrasonic pulse echo testing, and radiography, as well as fused images, were investigated and the suitability of these distinctive features and fusion techniques for improving the probability of defect detection was evaluated. For this purpose, aluminum single lap joints with brass inclusions were analyzed using ultrasound pulse echo and radiography techniques. The distinctive features were extracted from the data obtained, and images of features obtained by both techniques were fused together. Different combinations of features and fusion algorithms were investigated, considering the desire to automate data evaluation in the future.
Validation of novel ultrasonic phased array borehole probe by using simulation and measurement
(2022)
Low-frequency ultrasonic testing is a well-established non-destructive testing (NDT) method in civil engineering for material characterization and the localization of cracks, reinforcing bars and delamination. A novel ultrasonic borehole probe is developed for in situ quality assurance of sealing structures in radioactive waste repositories using existing research boreholes. The aim is to examine the sealing structures made of salt concrete for any possible cracks and delamination and to localize built-in components. A prototype has been developed using 12 individual horizontal dry point contact (DPC) shear wave transducers separated by equidistant transmitter/receiver arrays. The probe is equipped with a commercially available portable ultrasonic flaw detector used in the NDT civil engineering industry. To increase the sound pressure generated, the number of transducers in the novel probe is increased to 32 transducers. In addition, the timed excitation of each transducer directs a focused beam of sound to a specific angle and distance based on the previously calculated delay time. This narrows the sensitivity of test volume and improves the signal-to-noise ratio of the received signals. In this paper, the newly designed phased array borehole probe is validated by beam computation in the CIVA software and experimental investigations on a half-cylindrical test specimen to investigate the directional characteristics. In combination with geophysical reconstruction methods, it is expected that an optimised radiation pattern of the probe will improve the signal quality and thus increase the reliability of the imaging results. This is an important consideration for the construction of safe sealing structures for the safe disposal of radioactive or toxic waste.
In this study, we explored the detection of weak bonds (WBs) due to contamination and faulty curing (FC) using linear ultrasound and machine learning. For this purpose, aluminium single-lap adhesive joints containing three variants of bonding quality were investigated: perfect bond, WB due to release agent (RA) contamination, and WB due to FC. The data, according to the deviation of the bonding protocol, were arranged in two groups, creating two datasets: distinct and complete. Each dataset included all bonding conditions (perfect, RA, and FC), although the distinct dataset contained only marginal cases, which were expected to be well separable, whereas the complete dataset included data with minor deviations from the bonding protocol. Pulse-echo C-scan images were acquired for all prepared samples in the immersion tank, and 45 features were initially extracted from the time traces representing each bonding group. The initial data were analysed via a t-test and pairwise correlation analysis to reveal statistically significant features. Then, we performed dimensionality reduction using tree-based, recursive, sequential, and linear discriminant analysis (LDA) feature selectors to explore feature importance and classification accuracy with different feature subsets. Finally, the important features identified with the different feature selectors were fed to support vector machine (SVM) classifiers, and the classification accuracies were compared amongst the different feature subsets. The classification accuracy using a distinct dataset in some cases demonstrated nearly 99% accuracy, indicating that significant bonding protocol deviations could be easily detected. It was demonstrated that classification accuracy increased with the number of features. However, even in the case of the 2D feature space obtained using linear discriminant analysis, the bonding quality classification accuracy remained higher than 84%. The feature subspace reduction with LDA demonstrated sufficient classification accuracy and an improvement of nearly 40% in training time compared with that for the initial feature set. Thus, the classical ultrasonic pulse-echo C-scan with an LDA feature transformation and SVM classifier could be used to identify the deviations in the bonding protocol in aluminium single-lap adhesive joints.
A recent upgrade of key equipment of the BAMline widens its imaging capabilities: shorter scan acquisition times are now possible, in situ and operando studies can now be routinely performed, and different energy spectra can easily be set up. In fact, the upgraded double-multilayer monochromator brings full flexibility by yielding different energy spectra to optimize flux and energy resolution as desired.
Geführte Ultraschallwellen sind für die Materialcharakterisierung hervorragend geeignet, da ihr Ausbreitungsverhalten abhängig von den Materialeigenschaften des untersuchten Werkstoffs ist.
Um aus dem messtechnisch ermittelten Ausbreitungsverhalten geführter Ultraschallwellen Rückschlüsse auf die Materialparameter zu ziehen, werden in der aktuellen Forschung verschiedene inverse Methoden diskutiert. Dispersionsabbildungen im Frequenz-Wellenzahl-Bereich repräsentieren das Ausbreitungsverhalten geführter Ultraschallwellen. Maschinelles Lernen und insbesondere Convolutional-Neural-Networks (CNNs) sind eine Möglichkeit der automatisierten inversen Bestimmung der Materialparameter aus den Dispersionsabbildungen.
In diesem Beitrag wird anhand synthetischer Daten gezeigt, wie das Ausbreitungsverhalten von geführten Ultraschallwellen unter Verwendung von CNNs und Dispersionsabbildungen genutzt werden kann, um die elastischen Konstanten einer isotropen plattenförmigen Struktur zu bestimmen. Anhand dieses Beispiels wird das generelle Vorgehen zur Anwendung maschineller neuronaler Lernverfahren aufgezeigt. Hierfür werden die verwendeten Daten analysiert, das Preprocessing erläutert und eine einfache CNN-Architektur gewählt. Im Rahmen der Auswertung wird insbesondere Wert auf die Erklärbarkeit und Zuverlässigkeit des verwendeten CNNs gelegt und so Grenzen und Möglichkeiten aufgezeigt.
In non-destructive testing and structural health monitoring with ultrasonic waves, the quantification of damage in components is one of the main tasks. In many shell-like structures, such as plates, pipes, or laminate components, ultrasonic waves propagate as guided waves. Although guided waves enable the testing of large areas, their multimodal and dispersive properties make it challenging to analyze signals. So, there is a need for more advanced algorithms to handle these properties, especially when reconstructing damage position and geometry.
The reconstruction can be formulated as an inverse problem where the measured signals are fitted with a simulative forward model. Due to the small wavelength of ultrasonic waves, classic forward models based on, e.g., the Finite Element Method are computationally intensive. In contrast, the authors use the semi-analytical Scaled Boundary Finite Element Method (SBFEM) to reduce the computational effort. The SBFEM approximates arbitrary long, undamaged parts of the structure with only a few degrees of freedom.
This contribution summarizes a general inverse procedure based on algorithmic differentiation in combination with the SBFEM. Results are presented for damaged 2D cross-sectional models of waveguides. These results include an analysis of the robustness of the proposed algorithms against noise.
This paper considers an indirect measurement approach to reconstruct a defect in a two-dimensional waveguide model for a non-destructive ultrasonic inspection via derivative-based optimization. The propagation of the mechanical waves is simulated by the scaled boundary finite element method that builds on a semi-analytical approach. The simulated data are then fitted to given data associated with the reflected waves from a defect which is to be reconstructed. For this purpose, we apply an iteratively regularized Gauss-Newton method in combination with algorithmic differentiation to provide the required derivative information accurately and efficiently. We present numerical results for three kinds of defects, namely, a crack, delamination, and corrosion. The objective function and the properties of the reconstruction method are investigated. The examples show that the parameterization of the defect can be reconstructed efficiently as well as robustly in the presence of noise.
LIBS is a complementary method to XRF and can detect all elements without the need for vacuum conditions. Automated systems are already commercially available capable of scanning surfaces with a resolution of up to 0.1 mm within a few minutes. In addition to possible applications in R&D, LIBS is also used for practical applications in building materials laboratories and even on-site.
In view of ageing infrastructure facilities, a reliable assessment of the condition of concrete structures is of increasing interest. For concrete structures, the ingress of potential harmful ions is affecting the serviceability and eventually structural performance. Pitting corrosion induced by penetrating chlorides is the dominant deterioration mechanism. Condition assessment based on frequently performed chloride profiling can be useful to identify the extent and evolution of chloride ingress. This could prove to be more economical than extensive repairs, especially for important infrastructure facilities.
Currently the most common procedure for determining the chloride content is wet chemical analysis with standard resolution of 10 mm. The heterogeneity is not considered. LIBS is an economical alternative for determining the chloride content at depth intervals of 1 mm or less. It provides 2D distributions of multiple elements and can locate spots with higher concentrations. The results are directly correlated to the mass of binder and can also be performed on-site with a mobile LIBS-System.
The application of a LIBS-system is presented. Calibration is required for quantitative analysis. Concrete cores were drilled, sliced and analyzed to determine the 2D-distribution of harmful elements. By comparing the chloride ingress and the carbonation, the interaction of both processes can be visualized in a measurement that takes less than 10 minutes for a 50 mm x 100 mm drill core.
A leaflet on the use of LIBS for the chloride ingress assessment has been completed.
Due to the ageing of the infrastructure facilities, a reliable assessment of the condition of concrete structures is of great interest to plan timely and appropriate measures. In concrete structures, pittingcorrosion of the reinforcement is the predominant deterioration mechanism affecting serviceability and eventually structural performance. Determination of quantitative chloride ingress is not only necessary to obtain valuable information on the current condition of a structure, but the data obtained can also be used to predict future developments and the associated risks. An overview of the progress and the possibilities of the application of laser-induced breakdown spectroscopy for concrete analysis in daily civil engineering practice is given. High-resolution 2D measurements of drill cores to determine the penetration of harmful species into concrete is presented. Furthermore, the application of a mobile LIBS system in a parking garage is shown. The system consists of a diode-pumped low-energy laser (3 mJ, 1.5 ns, 100 Hz) and a compact NIR spectrometer. A scanner allows two-dimensional element mapping. Progress towards the establishment of LIBS in a leaflet for the analysis of chlorine ingress into concrete in civil engineering is presented.