Russian Journal of Nondestructive Testing最新文献

{"title":"Effects of Sensitization Time on Tensile Properties and Acoustic Emission Characteristics of s30408 Austenitic Stainless Steel","authors":"Qingshan Li,&nbsp;Xumeng Xie,&nbsp;Yanran Wang,&nbsp;Yuli Guo,&nbsp;Zhen Gao,&nbsp;Wenjie Pan,&nbsp;Muda Jin,&nbsp;Mengyu Chai,&nbsp;Xi Huang","doi":"10.1134/S1061830925605562","DOIUrl":"10.1134/S1061830925605562","url":null,"abstract":"<p>The sensitization of s30408 austenitic stainless steel, induced by exposure within the critical temperature range, significantly deteriorates its mechanical properties and corrosion resistance, posing substantial risks to structural applications. This study systematically examines the influence of sensitization time at 650°C on the tensile properties and acoustic emission (AE) characteristics of s30408 stainless steel at room temperature with a strain rate of 1.4 × 10^–3 s^–1. The results demonstrate that sensitization treatment led to a significant deterioration in the material’s ductility, evidenced by a consistent decrease in elongation to failure with prolonged sensitization time. Although strength changes were negligible, the fracture morphology revealed by SEM analysis transitioned from primarily ductile (transgranular) dimples to predominantly brittle intergranular fracture. Correspondingly, AE monitoring captured a dramatic increase in signal activity, particularly in high-amplitude (&gt;60 dB) hits. A key finding is the distinct evolution in the AE frequency spectrum , where sensitized samples exhibited a broadened centroid frequency distribution (100–400 kHz) compared to the unsensitized sample (100–300 kHz). The mean centroid frequency increased from 177.6 kHz in the unsensitized state to 273.7 kHz after 24 h of sensitization, with a marked narrowing of the confidence intervals (CI) indicating a more consistent AE signature. Microstructural analysis revealed that these changes are attributable to the precipitation of chromium-rich carbides along austenite grain boundaries. These carbides act as obstacles to dislocation motion, generating high-amplitude AE bursts, and serve as stress concentration sites for the nucleation of micro-voids and microcracks, ultimately promoting intergranular crack propagation. This work establishes a quantitative correlation between sensitization time, microstructural evolution, mechanical property degradation, and AE response, providing crucial insights for assessing damage states and predicting the service life of stainless steel components in sensitive environments.</p>","PeriodicalId":764,"journal":{"name":"Russian Journal of Nondestructive Testing","volume":"62 3","pages":"309 - 323"},"PeriodicalIF":1.3,"publicationDate":"2026-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148394843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Drill Pipe Thread Detection System Based on Alternating Current Field Measurement","authors":"Chuan Wang,&nbsp;Zhongsheng Sun,&nbsp;Xu Luo","doi":"10.1134/S1061830925605811","DOIUrl":"10.1134/S1061830925605811","url":null,"abstract":"<p>Drill pipe threads, as critical structural components connecting drill pipes, are subjected to harsh downhole environments and complex loads. They are prone to defects such as cracks, galling, wash-out, and other failures, which compromise the safety of drilling operations. To address the inspection requirements for drill pipe threads, this paper proposes a defect inspection system based on alternating current field measurement (ACFM) technology. Using Maxwell simulation software, we analyzed the influence of the bottom geometry of the magnetic core leg on the characteristic magnetic field signals of defects and optimized the magnetic core structure. Furthermore, the influence of varying crack lengths and depths on the detection signals was systematically investigated. The results reveal the mapping relationship between the signal features and the defect dimensions, providing a basis for quantitative inversion. Subsequently, the inspection device was designed, a prototype was manufactured, and the complete system was established. The performance of the system was evaluated using drill pipe joints containing defects at the thread tooth crest, thread tooth side, and thread tooth root. The results demonstrate that the system can effectively identify defects at various positions along the drill pipe thread, verifying its reliability.</p>","PeriodicalId":764,"journal":{"name":"Russian Journal of Nondestructive Testing","volume":"62 3","pages":"389 - 404"},"PeriodicalIF":1.3,"publicationDate":"2026-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148394845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated Identification of Internal Defects in Asphalt Pavements Based on Ground Penetrating Radar B-Scan","authors":"Weizhen Liu,&nbsp;Xin Yu,&nbsp;Luming Wang","doi":"10.1134/S106183092560529X","DOIUrl":"10.1134/S106183092560529X","url":null,"abstract":"<p>Ground-penetrating radar (GPR), as an effective and rapid nondestructive testing technique, has been widely applied in pavement distress detection. However, large-scale GPR data still rely heavily on manual interpretation, which is inefficient and prone to significant variability. To address this issue, a B-scan-based GPR method for detecting subsurface distresses in asphalt pavements is proposed in this study. First, the radar signature characteristics of subsurface distresses are determined through field coring, and a distress dataset is constructed. Second, a suitable detection model is selected and optimized to enhance performance. Finally, the model performance is validated using field project data.The results show that when subsurface distresses occur, the in-phase axis of the radargram exhibits high-amplitude, smooth, and discontinuous features, which can be effectively used for distress identification. Among ten YOLOv8 and YOLOv5 models compared, the YOLOv5s model achieves a good balance between detection accuracy and training speed. By replacing the backbone network with MobileNetV3, the model parameters are reduced by 48.8%, and the training time is shortened by 43.1%. Based on cross-validation with manually labeled test sections, the improved YOLOv5s-MobileNetV3 model achieves an accuracy of 80.0% for structural looseness and 87.1% for interlayer defects, demonstrating its effectiveness in practical applications.</p>","PeriodicalId":764,"journal":{"name":"Russian Journal of Nondestructive Testing","volume":"62 3","pages":"369 - 388"},"PeriodicalIF":1.3,"publicationDate":"2026-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148394844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Model-Based 3D Ultrasonic Imaging Method for Defect Detection and Evaluation in Welds Using Phased Arrays","authors":"Shijian Zhou,&nbsp;Yuxin Du,&nbsp;Guanghao Zhou,&nbsp;Shuzeng Zhang","doi":"10.1134/S1061830925605240","DOIUrl":"10.1134/S1061830925605240","url":null,"abstract":"<p>A three-dimensional (3D) imaging method using ultrasonic phased array technology is proposed for the detection and evaluation of defects in welds. An ultrasonic measurement model (UMM) is developed for the phased array system to correct the time-corrected gain (TCG) in sector scan (S-scan) images and establish a reliable inspection baseline. A defect-free reference weld specimen is scanned to statistically determine the maximum signal amplitudes at various positions across the S-scan image, thereby setting appropriate threshold values to suppress interference signals. Defect signals are extracted based on the model-based TCG and statistical threshold from the 2D S-scan results and then stacked to reconstruct a 3D image of the weld. Experimental results on a planar V-groove full-penetration weld specimen demonstrate that the proposed 3D imaging method provides a clearer and more comprehensive visualization of defect characteristics compared to conventional 2D imaging techniques. Furthermore, based on existing standards for weld quality levels and acceptance criteria for phased array ultrasonic testing (PAUT), the method enables quantitative evaluation of weld defects. The proposed approach significantly enhances both the intuitiveness of defect detection and the accuracy of quantitative assessment, offering theoretical and technical support for the practical application of ultrasonic phased array testing in weld inspection.</p>","PeriodicalId":764,"journal":{"name":"Russian Journal of Nondestructive Testing","volume":"62 3","pages":"294 - 308"},"PeriodicalIF":1.3,"publicationDate":"2026-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148394887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Limitations of Conventional Acoustic Emission Techniques of Parts Fabricated by Selective Laser Sintering","authors":"I. Y. Kinzhagulov,&nbsp;T. V. Basova,&nbsp;M. M. Lobastov","doi":"10.1134/S1061830926600553","DOIUrl":"10.1134/S1061830926600553","url":null,"abstract":"<p>The authors conducted experimental studies assessing acoustic emission (AE) testing capabilities for parts fabricated by selective laser sintering (SLS) from EP810 and EK94 materials. Key AE signal generation characteristics in SLS materials were identified within hydraulic pressure tests with simultaneous AE monitoring. These include abnormally high activity during internal stress relaxation and the recurrence of the Kaiser effect after the interval between tests. It has been demonstrated that there is a correlation of AE parameter time dependences with loading stages, which was verified by the measured geometric parameters of the test objects. The study has confirmed that the existing standard criteria for classifying AE source severity are inapplicable to SLS-fabricated components. The results have revealed the necessity for developing new specialized assessment criteria and adapting regulatory documentation for AE testing of additively manufactured objects, including SLS.</p>","PeriodicalId":764,"journal":{"name":"Russian Journal of Nondestructive Testing","volume":"62 3","pages":"267 - 279"},"PeriodicalIF":1.3,"publicationDate":"2026-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148394847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Wavelet Packet Denoising Method for Ultrasonic Testing in Austenitic Stainless Steel Welds","authors":"D. Z. Chi,&nbsp;Y. H. Liu","doi":"10.1134/S1061830925605598","DOIUrl":"10.1134/S1061830925605598","url":null,"abstract":"<p>The inhomogeneity and anisotropic characteristics of the grains in austenitic steels weld cause significant ultrasonic wave scattering. This leads to substantial noise contamination in the collected signals and makes it difficult to identify weld defects. To solve the problem, a novel ideal is put forward for ultrasonic signal denoising, in which ultrasonic dynamic scanning is considered, i.e., both the target signal and the signal collected at adjacent measurement points are taken into account. Based on this, a modified wavelet packet denoising method is proposed. In this method, firstly, the wavelet packet decomposition is performed on both the target signal and its adjacent signal. Secondly, according to the theory of signal cross-correlation, a set of optimized wavelet packet coefficients are obtained using the decomposed data. At last, threshold denoising and signal reconstruction are carried on the optimized wavelet packet coefficients. Artificial defects contained austenitic steels weld block are made and tested using ultrasonic pulse-echo technique. The collected ultrasonic signals are denoise processed using both the conventional and modified method and the results are compared and analyzed. The experimental findings demonstrated that the suggested approach can efficiently reduce noise in ultrasonic testing signals from austenitic stainless steel welds, so as to realize the identification and localization of defects. Moreover, the modified denoising method is more stable and better than the conventional one.</p>","PeriodicalId":764,"journal":{"name":"Russian Journal of Nondestructive Testing","volume":"62 3","pages":"324 - 337"},"PeriodicalIF":1.3,"publicationDate":"2026-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148394886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization of an Algorithm for Solving a Two-Dimensional Magnetostatic Problem for a Body with a Model Shape of a Surface Defect","authors":"V. Ya. Rayevsky,&nbsp;O. V. Kudryashova","doi":"10.1134/S1061830926600759","DOIUrl":"10.1134/S1061830926600759","url":null,"abstract":"<p>For the two-dimensional problem of a magnetic half-space with a surface defect modeled by the “cap” function, an approach has been implemented that makes it possible to reduce the integral equation for the normal component of the desired field on the entire magnetic bounding curve to an integral equation only for the fragment modeling the defect. Equations for expressing the components of the resulting field in terms of the value of the normal component on the defective fragment are obtained. An algorithm for numerical solution of the problem is constructed and implemented in a computer program.</p>","PeriodicalId":764,"journal":{"name":"Russian Journal of Nondestructive Testing","volume":"62 3","pages":"356 - 368"},"PeriodicalIF":1.3,"publicationDate":"2026-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148394842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of Geometric Parameters of Volumetric Defects in Dielectric Coatings by the Electrospark Method of Nondestructive Testing (High-Voltage Spark Testing)","authors":"V. A. Syasko,&nbsp;A. S. Musikhin,&nbsp;I. S. Gnivush,&nbsp;M. S. Stepanova","doi":"10.1134/S1061830926600309","DOIUrl":"10.1134/S1061830926600309","url":null,"abstract":"<p>The paper examines the feasibility of detecting nonthrough defects (internal defects and interfacial defects at the metal-coating interface) in dielectric coatings using the electrospark method of nondestructive testing. It is shown that partial discharges, which induce a characteristic distortion of the test-voltage pulse, serve as an informative indicator of a coating defect. Based on an equivalent capacitive model of a coating with a defect, a relationship between the apparent charge and the geometry of the air gap is derived. The results are validated by numerical simulation of the electric-field distribution in COMSOL and by experiments on plexiglas specimens with blind holes of varying depth. An algorithm is proposed for extracting the partial-discharge signal using a weighted sum of increments in the root-mean-square deviations of the second derivative of the voltage. The obtained dependences make it possible to estimate the residual coating thickness in the defect region.</p>","PeriodicalId":764,"journal":{"name":"Russian Journal of Nondestructive Testing","volume":"62 3","pages":"347 - 355"},"PeriodicalIF":1.3,"publicationDate":"2026-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148394841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Convolution Neural Network for Defect Depth Estimation in Non-Linear Frequency Modulated Thermal Wave Imaging","authors":"G. T. Vesala,&nbsp;Naga Prasanthi Yerneni,&nbsp;A. Vijaya Lakshmi,&nbsp;N. Swapna,&nbsp;M. Suman,&nbsp;V. S. Ghali","doi":"10.1134/S1061830925605446","DOIUrl":"10.1134/S1061830925605446","url":null,"abstract":"<p>Defect depth estimation in composite structures evaluated by active infrared thermography is a difficult task. Deep learning (DL) approaches are becoming more and more popular to automate the process and provide quicker insights. Since the performance of the DL model depends on the amount of noise contained in the raw thermal data under harsh inspection conditions, a proper denoising technique is necessary before feeding the raw data to depth estimation models. The one-dimensional convolution neural network (1D-CNN) for estimating the depth of defects in carbon fiber reinforced polymer (CFRP) examined using logarithmic frequency modulated thermal wave imaging is presented in this research. The noise is simulated by adding 5 and 10 dB of additive white Gaussian noise (AWGN) to the thermal profiles’ signal power. Furthermore, these thermal data are denoised using the recently developed stacked denoising autoencoder (SDCAE), which is thought to be a practical denoising method, prior to being fed into the 1D-CNN. The depth estimation performance of the suggested 1D-CNN is confirmed using a variety of performance indicators after it has been trained and evaluated on original, noisy, and SDCAE denoised thermal data separately. According to the results and performance measures, the 1D-CNN exhibits over 95% accuracy even in situations with high levels of noise, and it gets better in denoised cases and original thermal response. The slightly improved performance for denoised thermal data confirms that SDCAE is a good denoising method to use before estimating the depth of a defect.</p>","PeriodicalId":764,"journal":{"name":"Russian Journal of Nondestructive Testing","volume":"62 3","pages":"405 - 418"},"PeriodicalIF":1.3,"publicationDate":"2026-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148394846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigation of an Air-Coupled Nondestructive Testing System for Composite Structures Using Electrodynamic Acoustic Transducer and Laser Vibrometry","authors":"V. O. Nekhoroshev,&nbsp;D. A. Derusova,&nbsp;V. Yu. Shpil’noi,&nbsp;M. P. Kravetskaya","doi":"10.1134/S1061830926600747","DOIUrl":"10.1134/S1061830926600747","url":null,"abstract":"<p>This study investigates an air-coupled acoustic excitation system combined with laser Doppler vibrometry for nondestructive testing of composite materials. The original air-coupled system based on a modified electrodynamic acoustic transducer was characterized. The proposed approach features a pulsed operating mode in which the transducer’s peak power substantially exceeds its nominal rating (by approximately a factor of 400). The radiated sound field was quantified in terms of sound-pressure amplitude (<span>( geqslant {kern 1pt} 0.2)</span> Pa at a distance of ~10 cm from the source) and directivity pattern. In addition, it was demonstrated that varying the discharge-circuit resistance provides effective control over both the peak power and the spectral content of the generated acoustic waves. An experimental modal analysis was performed on a small-scale (<span>(5 times 5)</span> mm) model specimen in the form of a thin multilayer plate, and defect detection was carried out on a large-scale (<span>(200 times 200)</span> mm) composite sandwich panel with a honeycomb core. Analysis of the experimental data confirmed the feasibility of using electrodynamic transducers for pulsed acoustic excitation in defect detection and nondestructive testing over a frequency range of 3 Hz to 50 kHz. It is shown that combining air-coupled acoustic excitation with laser vibrometry overcomes the limitations of contact-based excitation and vibration measurement methods, significantly extending the applicability of acoustic techniques in defect detection and nondestructive testing.</p>","PeriodicalId":764,"journal":{"name":"Russian Journal of Nondestructive Testing","volume":"62 3","pages":"280 - 293"},"PeriodicalIF":1.3,"publicationDate":"2026-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148394885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}