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Ultrasonic testing (UT) is a commonly used approach for inspection of material and defect detection without causing harm to the inspected component. To improve the reliability of defect detection, the material is often scanned from various angles leading to an immense amount of data that needs to be analyzed. Some of the defects are only seen on B-scans taken from a particular angle so discarding some of the data would increase the risk of not detecting all of the defects. Recently there has been significant progress in the development of methods for automated defect analysis from the UT data. Using such methods the inspection can be performed quicker, but it is still necessary to inspect all of the angles to detect defects. In this work, we test a novel approach for accelerating the analysis by merging the images from various angles. To reduce the information loss during the process of merging, we develop a new model with a weighting module that dynamically determines the importance of each of the scanning angles. Using the proposed module, the loss of information is minimal, so the precision of the detection model is comparable to the model tested on each of the images separately. Using the merged images input, the analysis can be accelerated by almost 15 times.

Non-destructive ultrasonic analysis of materials is a method for assessing the integrity of the inspected components. It is commonly used in monitoring critical parts of the power plants, in aeronautics, oil and gas, and the automotive industry. Since most ultrasonic inspections rely on expert's previous experience they must constantly practice on new, unseen data. Acquiring enough data for training human experts on non- destructive ultrasonic scan analysis can be an expensive and time-consuming task. The only possibility to get new data for practicing is to implant synthetic defects in real metal blocks. Artificial defects are made by temperature strain, electrical discharge, and physical damage. All of those methods are very complicated and expensive to perform. Also metal blocks have to be taken from the components of the power plants to have the same structure and be realistic. In this work, some attempts have been made to generate 3D ultrasonic scans using computer vision and deep learning methods.

The analysis of the data in non-destructive ultrasonic testing of materials is a very time- intensive task. To alleviate the aforementioned strain on the human expert inspectors, a plethora of assisted analysis methods based on deep learning have been developed recently. However, most of these methods are based on the automated detection of flaws in A-scans and B-scans and therefore we propose a method based on the detection of flaws in C-scans that can reduce the complexity of manual detection of flaws in B- scans. The proposed method classifies each row of the C-scan based on whether it contains any flaws or not. Afterward, the positively classified rows are forwarded for further automated (and manual) inspection. The results show that the developed method significantly reduces the number of B-scans that have to be further analyzed.

Electromechanical characterization of piezoceramic bulk elements around resonance is usually done with low-level continuous excitation signals at room temperature, but in real applications such elements are driven with different types of electrical signals, usually at higher levels and at different ambient temperatures. Both homemade and commercial soft and hard PZT piezoceramic elements were characterized using the established characterization methods that include the measurements of electrical admittance and surface displacement of the piezoceramic elements around the series resonance frequencies of two modes of vibration (radial and thickness extensional). The measurements included fast frequency sweeps at constant voltage excitation levels, burst measurements, at different temperatures and at different levels of excitation. A novel method for electromechanical characterization of piezoceramic elements that utilizes the resonance frequency tracking at different excitation levels (electric fields up to 5 kV/m, currents up to 1.3 A at resonance) and temperature conditions (up to 150 °C) has been proposed. The main idea is to keep the investigated element in resonance as the excitation level changes by constant tracking of its resonance frequency. The electromechanical parameters of the considered elements change mostly due to the nonlinear effects and the changes due to different thermodynamic conditions can be neglected when fast algorithm is applied. The decrease of the input electrical admittance magnitude is more expressed than the change of the resonance frequency when algorithm is applied.

The orientation dependence of the longitudinal piezoelectric coefficient is investigated as a function of temperature in BaTiO/sub 3/ and PbTiO/sub 3/ crystals using Landau-Ginsburg-Devonshire theory. We show that a presence of the ferroelectric - ferroelectric phase transitions in BaTiO/sub 3/ leads to an enhanced piezoelectric longitudinal response along nonpolar directions. The reason for this is the abrupt increase of the shear piezoelectric coefficients in the vicinity of a FE - FE phase transition temperature. In PbTiO/sub 3/, which does not exhibit such phase transitions, the shear piezoelectric effect is weak and the longitudinal piezoelectric coefficient has its maximum along the polar axis at all temperatures. These results can be generalized to include phase transitions induced by electric-field and composition variation and are valid for all perovskite materials. Further, using Landau-Ginzburg-Devonshire theory and tetragonal BaTiO/sub 3/ as an example, we demonstrate that a strong electric bias fields applied anti-parallel to the spontaneous polarization may lead to a large enhancement of piezoelectric properties in monodomain perovskite crystals. The enhancement is due to the field-induced dielectric softening of the crystal.