Russian Journal of Nondestructive Testing最新文献

Results of applying active thermal nondestructive testing for the detection of water ingress in horizontally oriented aviation honeycomb panels and quantitative evaluation of water content are presented. Unlike ultrasonic inspection, thermal testing allows one to detect water and evaluate its quantity in the presence of air gaps between water and inspected honeycomb skin. The proposed algorithm based on using an artificial neural network has enabled estimating water content with errors under 15% in the cases where water contacts a honeycomb skin, as well as in the presence of air gaps between the skin and water.

It is shown that when performing surgical operations to remove foreign metal particles from the human body, it is advisable to use metal detectors of various types: flux-gate detectors for localizing ferromagnetic particles and eddy current detectors for localizing nonferromagnetic metal particles. The sensitivity of medical equipment must be sufficient to detect small ferromagnetic fragments and particles from a distance of at least 10 mm. The feasibility of preliminary magnetization of the search area with a strong permanent magnet is shown. Methods for setting up metal detectors are given. The need to minimize extraneous electromagnetic fields in the operating room is shown.

Anchors constitute a common form of structural support in geotechnical engineering. Precise identification of ultra-early-stage (UES) anchoring quality is crucial to ensure the integrity of the secondary lining. To address grout defects in the UES of anchors, a calculation method for UES anchor wave velocity was introduced. Indoor experiments and numerical simulations were conducted for non-destructive testing (NDT) of anchors in the UES, analyzing time-domain waveform characteristics and wave velocity variations. A method for identifying grout defects in the UES of anchors was proposed. The results indicate that the proposed wave velocity calculation method offers a more precise estimation of UES wave velocity for anchors compared to the traditional approach. This enhancement enables a more precise evaluation of the development of solid phases in the anchoring medium. As the solid phase develops, the wave velocity and first wave amplitude of the anchor gradually decline, while the response time of the bottom reflection increases. Grout defects lead to amplified amplitudes in both time-domain and frequency-domain signals, accompanied by a heightened occurrence of peaks in the frequency domain. The waveform distortion region before the bottom reflection is caused by grout defects. In the time-domain signals of defective anchors, a waveform distortion region is observed before the bottom reflection. By assessing the magnitude of the absolute value of the ratio between the amplitude of characteristic reflection points within the distortion region and the amplitude of the first wave, effective identification of grout defects in the UES of anchors can be accomplished.

Wall-climbing robot are seeing increasing adoption to automated remote and in situ inspection of industrial assets, removing the need for hazardous manned access. The ultrasonic dry-coupling detection device installed on the wall-climbing robot detects the defects of the tank wall. Aiming at the difficulty that the ultrasonic A-scan signal obtained by the ultrasonic dry-coupling detection method has waveform cross-aliasing, which makes it difficult to obtain effective information in traditional feature extraction, Herein, we combine the fast Fourier transform, wavelet packet decomposition and empirical mode decomposition techniques to propose a 3D-SFE method performs multi-scale feature extraction on dry coupled signals. At the same time, in view of the difficulty that traditional nondestructive testing models cannot quantify the defect area accurately, we introduce the XGBoost model to better quantify the defect area. Our proposed defect area quantification model based on multi-scale feature extraction achieves 99.9% accuracy on the training set and 81.5% on the test set. Furthermore, we also analyzed the influence of defect characteristics, sample number, defect shape and depth on the model, and then provided certain guiding significance for the detection of tank defects.

The aerospace and automotive sectors widely use carbon fiber reinforced plastic because of its exceptional properties, including its high specific modulus, strength, and resistance to fatigue. However, defects such as cracks in the matrix, separation of layers, and separation from bonding can occur during manufacturing and low-velocity impacts, often remaining undetected. As these defects worsen over time, they can significantly weaken the material. To reduce the risk of major failures, regular assessments of carbon fiber reinforced plastic structures are crucial. This study introduces a structural health monitoring technique that minimizes human involvement while effectively tracking the growth of damage in carbon fiber reinforced plastic structures. The approach employs the acoustic emission method and the hilbert transform technique to identify and quantify the progression of damage in carbon fiber reinforced plastic materials. Experimental outcomes from a fatigue test conducted on cross-ply laminates are presented. To precisely predict damage and evaluate the condition of the composite specimen, researchers use the bidirectional long short-term memory model alongside envelope analysis for forecasting. The suggested method achieves a root mean square error of less than 0.03, proving its capability to precisely predict damage and evaluate the condition of the Composite structure. This novel deep learning-driven method adeptly captures the deterioration in performance of carbon fiber reinforced plastic, enhancing predictive accuracy.

The issues of classification and characterization of surface operational defects according to the results of ultrasonic, eddy current, and visual inspection methods of nondestructive testing are considered. At the same time, the visual inspection method was realized with the use of a television inspection camera equipped with a computer vision function and a laser triangulation sensor. The paper presents a dataset containing 5760 images of pipelines with and without pitting corrosion. A convolutional neural network (CNN) is presented that was applied to classify the images obtained from the TV inspection camera into images without corrosion and images with pitting corrosion. The paper presents a dataset containing 269 measurements of planar and volumetric surface defects. A model for surface defect sizing based on gradient boosting is presented. The paper develops an algorithm for classification and sizing of surface defects in complex diagnostics in which the obtained models are applied, and determines the accuracy of this algorithm in the RMSE metric, which was calculated within the studied test dataset and amounted to 0.011 mm.

The paper presents the possibilities to estimate 304 steel plate’s plastic deformation using spectral analysis of the amplitude of difference and sum frequencies resulting from mixing of two Lamb waves with different frequencies. An electromagnetic acoustic transducer (EMAT) is used for the excitation of Lamb waves. The numerical modelling of the results of Lamb wave interference in a plate under tensile deformation is carried out; a comprehensive nonlinear factor is proposed and its sensitivity to the tensile deformation has been evaluated. Experimental verification of the modelling results is carried out. The experimental results show that this factor can be used to evaluate 304 steel plate’s tensile deformation quantitatively. This evaluation can eliminate the redundancy between difference frequency nonlinear parameter and sum frequency nonlinear parameter, as well as, improve the complementarity between the two parameters.

Efficient processing and stimulation mechanisms facilitating subsurface feature analysis are of prime concern in composite inspection. Being capable of presenting depth resolution and depth scanning with frequency sweep at low powers makes quadratic chirp an attractive stimulation mechanism and chirp Z-phased post-processing mechanism. This paper explores this mechanism with existing contemporary approaches and presents its novel feature exhibition enhancement capability through an inspection carried over a carbon fiber reinforced polymer (CFRP) composite specimen with embedded flat bottom holes. The defect detection performance is evaluated using the defect signal-to-noise ratio (SNR) for all the feature extraction algorithms. The SNR, characteristic parameter versus defect size and depth parameters reveal that the time domain PC and frequency domain CZT phase exhibit significantly high SNR and good correlation with the defect depth.

A method is proposed to determine uncontrolled areas of aircraft engine parts in penetrant testing, taking into account the specifics of applying developers of different types (all forms according to ISO 3452-3). The main technological factors affecting the testability of surfaces of parts of complex geometry are presented. A method has been experimentally tested, which makes it possible to determine the uncontrolled zones of engine parts in penetrant testing, due to the specifics of applying different forms of developers. It has been established that when carrying out penetrant testing of parts, especially with complex configurations/geometry, the existing technologies for applying developers may not ensure its high-quality application to all controlled surfaces of parts, as was previously assumed based on the results of an expert assessment. It has been experimentally proven that such structural elements of parts as holes are controlled to a depth much less than the diameter.

Recent achievements in TWDAR (thermal wave detection and ranging) technology has made it possible to utilize a range of thermal imaging techniques for analyzing the characteristics of materials used in various industries. Moreover, the distinctive features of nonstationary thermal imaging have piqued attention of researchers in non-destructive evaluation (NDE). For a detailed defect visualization, it is essential to employ a dependable processing technique that accurately extracts the relevant time–frequency components from the chirped thermal response. In this study, a nonstationary thermal wave imaging technique is utilized by using quadratic frequency modulation (QFM) in conjunction with a cutting-edge technique of fractional Fourier transform (FrFT), to assess material quality. An experimentation has been carried out on carbon fiber reinforced polymer (CFRP) and glass fiber reinforced polymer (GFRP) samples with defects of different sizes at varying depths, to evaluate their characteristics. Experimental results have validated the efficiency of the proposed FrFT processing approach through rigorous qualitative and quantitative analysis, which has involved measurements of some merit figures, such as signal-to-noise ratio (SNR), full width at half maxima (FWHM), and probability of detection (PoD). From the results, it is evident that the proposed method provides a distinct and precise visualization of defects promising to be a useful technique in identifying and retrieving information of internal defects in materials.