Analytische Chemie
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In the last two decades automated ultrasonic inspection devices took over a lot of applications that prior have been carried out using manual inspection with the evaluation of A-scans only. In parallel phased array systems have been developed and brought to the market which offer detailed and fast control over the sound field. When applying automated inspection phased array systems for UT measurements imaging of the recorded data in combination with the probe positioning data is used for the evaluation of inspections. B-Scan, C-Scan and S-Scan images are typically used with this setup.
For more sophisticated applications with linear arrays echo tomography and syntethic aperture focusing technique (SAFT) are well known methods and often applied for high resolution image reconstruction. Since channel count of phased array systems is constantly rising, matrix arrays with up to 256 elements entered the market. Signal processing in the matrix domain became 3D. Since some years the Total Focusing Method (TFM) is an additional imaging tool for these type of application. It is based on the Full Matrix Capture (FMC) using the elements of phased array probes as separate transmitters and receivers.
In this contribution we discuss the common ground of SAFT and TFM as well as the differences between these imaging tools. The combined use of automated inspection, matrix arrays and signal processing for high resolution measurements is a challenging task where a very long parameter list has to be taken into account. Under which conditions which elements of the full matrix should be taken for the reconstruction for best results?
Based on examples taken from measured and simulated echo signals it will be shown how image resolution can be optimized in dependence of different parameters like the distance between transmitters and receivers and their directivity patterns, the depth of echo source and the specimen geometry.
High sensitivity is an important requirement for air-coupled ultrasonic sensors applied to materials testing. With a lower acoustic impedance than any piezoelectric material, charged cellular polypropylene (PP) offers better matching to air with a similar piezoelectric coefficient. The piezoelectric properties of charged cellular PP originate from their polarization, creating permanent internal voltage. The sensitivity of the sensor can be increased by applying additional dc bias voltage, as it has been done already for transmitters. This work presents the first ultrasonic sensor based on charged cellular PP including a high-voltage module providing dc bias voltage up to 2 kV. This bias voltage led to an increase in the signal-to-noise ratio of up to 15 ± 1 dB. The measurement of the received signal depending on the applied bias voltage is proposed as a new method of determining the internal voltage of ferroelectrets. The sensor combined with a cellular PP transmitter was applied to nondestructive testing of a rotor blade segment and glued-laminated timber, enabling imaging of the internal structure of these specimens with a thickness around 4 cm.
Air-coupled ultrasound was used for assessing natural defects in wood boards by through-transmission scanning measurements. Gas matrix piezoelectric (GMP) and ferroelectret (FE) transducers were studied. The study also included tests with additional bias voltage with the ferroelectret receivers. Signal analyses, analyses of the measurement dynamics and statistical analyses of the signal parameters were conducted. After the measurement series, the samples were cut from the measurement regions and the defects were analyzed visually from the cross sections. The ultrasound responses were compared with the results of the visual examination of the cross sections. With the additional bias voltage, the ferroelectret measurement showed increased signal-to-noise ratio, which is especially important for air-coupled measurement of high-attenuation materials like wood. When comparing the defect response of GMP and FE sensors, it was found that FE sensors had more sensitive dynamic range, resulting from better s/n ratio and short response pulse. Classification test was made to test the possibility of detecting defects in sound wood. Machine learning methods including decision trees, k-nearest neighbor and support vector machine were used. The classification accuracy varied between 72 and 77% in the tests. All the tested machine learning methods could be used efficiently for the classification.
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.