Russian Journal of Nondestructive Testing最新文献

In this paper, we propose a wireless strain sensor utilizing a spoof localized surface plasmon (SLSPs) resonant structure for strain measurement in both metallic and nonmetallic materials. The resonant element consists of a metal spiral structure (MSS) on a defected ground, which excites the SLSPs. This resonator is connected to linearly polarized, ultra-wideband, high-gain microstrip patch antennas to enable wireless extraction of strain information. A coupled metallic plate is incorporated in the SLSPs resonator to form a capacitive coupling, thereby completing the wireless strain sensor design. As the material under test (MUT) undergoes stress-induced strain, the separation between the sensor’s two plates changes, shifting the resonant frequency. Two log-periodic antennas are employed for wireless signal transmission and reception, allowing the strain magnitude to be determined from this frequency shift. Experimental results show that the proposed sensor can detect a minimum deformation of 0.01 mm, with a sensitivity up to 574.2 kHz/με, representing a substantial improvement over traditional near-field resonant strain sensors. The proposed sensor enables high-precision wireless monitoring of small strains in the MUT, offering a highly flexible and noninvasive approach for structural health sensing.

For extended uniformly magnetized bodies, a practical implementation of a numerical algorithm for solving an integro- differential equation on a function that defines the localization, shape, and size of a cavity in such a magnet based on the measured resulting field outside of it has been investigated. A program in the FORTRAN language that implements the above algorithm has been compiled. The shape, dimensions, and position of a noncoaxial cylindrical cavity in the magnet were reconstructed as a test and illustrative example of the studied algorithm for a uniformly magnetized cylindrical magnet.

Based on the principles of acoustoelasticity, the critically refracted longitudinal (L_CR) wave ultrasonic method for detecting planar principal stresses in isotropic materials is derived through a combination of theoretical derivation and experimental validation. The influence of various welding parameters on the residual stresses in welded workpieces is analyzed using finite element simulation software. The results indicate that the maximum equivalent stress in the weld seam is inversely proportional to the welding speed and directly proportional to the welding current. Subsequently, two SUS301L austenitic stainless steel plates are selected as the welding materials, and metal inert gas (MIG) welding is employed for flat plate welding. Residual stresses in different regions of the welded workpieces are measured using a three-directional ultrasonic testing method and validated against the simulation data. A high degree of agreement is observed between the two, thereby demonstrating the feasibility of this ultrasonic method for detecting planar stress.

The present study is carried out within the framework of a semi-analytical computer model. The model is based on the solution of a three-dimensional boundary value problem concerning the interaction of the acoustic field generated by an air-coupled ultrasonic transducer with a composite plate made of fiber-reinforced pregs. The investigation focuses on the influence of composite’s anisotropy and the tilt of the non-contact transducer on the directivity diagrams, frequency response, and dispersion properties of the guided waves excited in the plate. The wave field is described by the solution of the coupled problem for the system source–acoustic-medium–composite-plate obtained in the form of the inverse Fourier transform path integrals of the waveguide Green’s matrix and source parameters. The residual technique and the stationary phase method gives an explicit physically visual representation for the guided waves excited contactlessly in the composite plate. Utilizing this framework, the optimal transducer tilt angles for exciting waves of the desired type at specific center frequencies are determined. Numerical results demonstrating the dependence of the amplitude–frequency characteristics of the excited waves and the optimal transducer tilt angle on the sample’s structure and elastic properties are presented.

This article reports on the effectiveness of infrared thermography (IRT) in detecting blind holes of varying depth and diameter in carbon fiber reinforced polymer (CFRP) sample. It utilises halogen lamps as the heat source and implements three excitation techniques: pulse thermography (PT), lock-in thermography (LT) and frequency modulation thermal wave imaging (FMTWI); along with that, it compares two post-processing approaches, cross-correlation (CC) and frequency domain phase (FDP) on the obtained thermal images. The signal-to-noise ratio (SNR) is considered a figure of merit for evaluating the effectiveness of each technique and its associated post-processing approaches. The results demonstrate that the CC post-processing technique consistently outperforms the FDP method in enhancing defect visibility and improving SNR values across all excitation techniques and configurations. This research highlights the potential of IRT as a reliable, non-destructive testing method for detecting and characterising defects in a chosen CFRP test sample.

In this paper we propose to automate the classification of reflector types by TOFD echoes using the ResNet-18 convolutional neural network. The main focus is on modeling and classification of reflectors such as cracks, pores, nonwelds, and void areas. Experiments included training the model on TOFD echoes calculated both in a numerical experiment and TOFD echoes measured during ultrasonic inspection. The results showed high classification accuracy: 96.2% in the numerical experiment, 97% on experimentally measured TOFD echoes with various types of reflectors. The study confirmed the possibility of using neural networks to determine the reflector type based on TOFD echo signals; this allows automating the process of nondestructive testing and reduce the influence of human factor. For further development of the method it is suggested to use segmentation models for processing images with several reflectors.

The article discusses the possibility of using the acoustic-electrical transformation method to detect cracks and mechanical compressive strength of concrete. Numerical and experimental studies of changes in the parameters of the electromagnetic response of model samples of concrete made of a cement-sand mixture with a crack to a deterministic pulsed acoustic impact are presented. It is shown that the presence of a crack is determined by changes in the amplitude-frequency parameters of the electromagnetic response from the sample. An example of  determining the locations of weakening of the mechanical strength of a concrete construction beam based on the parameters of electromagnetic signals is given. The results of comparative tests for determining the mechanical compressive strength of concrete, obtained using a calibrated sclerometer and an acoustic-electric method, are shown. The results of monitoring the mechanical strength of concrete structures of an operating bridge crossing over a river are also presented based on the parameters of the electromagnetic response that arise during impact probing with acoustic pulses.

Oil and gas pipelines are crucial infrastructures in the oil and gas industry, responsible for transporting resources and connecting supply and demand. However, the complex operational environment, influenced by external and internal factors, leads to varying degrees of damage or structural failures as service time increases. If these defects are not identified and repaired promptly, they can result in serious safety incidents, endangering lives and property. To address the problems of uneven recognition accuracy and insufficient generalization ability of traditional oil and gas pipeline defect recognition and classification methods under different working conditions, the paper utilizes convolutional neural network (CNN) to extract spatial features from the ultrasonic echo sequences, which are then cascaded to long short-term memory (LSTM) network to mine the temporal features hidden within the ultrasonic echo sequences. Next, by employing a multi-head self-attention mechanism to dynamically adjust weights based on feature importance, the accuracy of defect identification and classification is improved. Validation using actual ultrasonic echo data from pipeline defects shows that the accuracy rates for identifying and classifying signals with no defects, as well as with defects at depths of 2, 5, and 8 mm, are 94, 89, 100, and 100%, respectively. The corresponding precision, recall, and F1-score all exceed 90%, significantly outperforming traditional methods. Furthermore, under the multi-condition noise resistance and generalization validation, the model consistently maintains an accuracy rate of over 90%, demonstrating robust noise resistance and strong generalization capabilities.

Accurately predicting impact energy levels in glass fiber-reinforced polymer (GFRP) composites is crucial for assessing material performance under varying impact conditions. This study presents a novel methodology that integrates sequential backward selection (SBS) and adaptive neuro-fuzzy inference system (ANFIS) to enhance the precision of impact energy estimation using non-destructive evaluation techniques. The proposed approach begins with the application of controlled impact energies to composite specimens, followed by ultrasonic inspection using the Mistras system to acquire B-scan and C-scan images. These images are subsequently converted into representative signals, from which key indicators are extracted. To optimize computational efficiency and improve predictive accuracy, SBS is employed to systematically select the most relevant features, minimizing redundancy and noise. The refined feature set is then used as input for an ANFIS model, which effectively captures nonlinear relationships between ultrasonic data and impact energy levels. The results demonstrate the potential of integrating advanced machine learning techniques with ultrasonic non-destructive evaluation for precise and reliable impact energy prediction in composite materials. This methodology provides a robust framework for structural health monitoring and predictive maintenance in industries where composite integrity is a critical concern.

The ability to precisely determine the level and height of liquids in industrial reactors and vessels that operate at high pressure and temperature plays a crucial role in the petrochemical, oil, and steel industries. Since the exact measurement of fluid or liquid levels is impossible due to high pressures and temperatures in vessels, a technique has been presented to calibrate gamma level gauges. To achieve this aim, the nuclear level gauge of a petrochemical stripper was simulated using Monte Carlo N-Particle eXtended (MCNPX) in real and operational conditions in the oil district in two stages. First, the nuclear level gauge consisting of a source, detector, and vessel (stripper), including water and air for calibration, was simulated with different height percentages. The results were compared, analyzed, and validated with experimental data in operational conditions. According to the results, the mean relative error (MRE%) was less than 6.71% and the root mean square error (RMSE) was predicted to be 0.01. The results showed that the acquired data from the simulation are in good agreement with real data (experimental). Then, the level gauge and stripper containing urea and gases at high temperature and pressure, and with similar height percentages in the first stage, were simulated. The results, which are completely consistent with the experimental findings, were converted into the required format and input into the nuclear electronic system for final calibration.