Journal of Nondestructive Evaluation最新文献

{"title":"Frequency-Optimized Ultrasonic and Machine Learning Framework for Early Detection of Carburization in HP Steel Tubes","authors":"Francirley P. da Silva,&nbsp;Carlos O. D. Martins,&nbsp;Henrique D. da Fonseca Filho,&nbsp;Robert S. Matos,&nbsp;Ivan C. Silva","doi":"10.1007/s10921-025-01261-2","DOIUrl":"10.1007/s10921-025-01261-2","url":null,"abstract":"<div><p>Carburization is a critical degradation mechanism in high-performance (HP) steel furnace tubes, impairing structural integrity during prolonged high-temperature service. This study proposes a machine learning-assisted ultrasonic testing framework to classify four levels of carburization damage in Cr‒Ni‒Nb HP steel alloys. A total of 80 A-scan signals were acquired per frequency (2.25 and 5 MHz) across four distinct damage classes, with spectral features extracted via discrete cosine transform (DTC). Microstructural analysis confirmed a linear increase in the volumetric fraction of chromium carbides from 9.5% (SP01, low carburization) to 40.5% (SP04, severe carburization). Among the classifiers evaluated, the K-Nearest Neighbors (KNN) and Quadratic Support Vector Machine (QSVM) achieved 100% accuracy (AUC = 1.00) at 2.25 MHz for advanced damage levels. However, early-stage detection remained challenging, with GNB attaining only 83.1% accuracy and AUC = 0.91 for SP01. Classification performance deteriorated significantly at 5 MHz due to increased signal attenuation and noise, with accuracy falling to 47.3–53.5%. These findings underscore the influence of ultrasonic frequency on damage detectability and model reliability. The integration of frequency-optimized ultrasonic inspection with machine learning delivers a scalable approach for real-time, non-destructive monitoring of carburization in industrial HP steel components, offering critical insights for predictive maintenance and structural health assessment.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 4","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144923174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Superimposing Synthetic Defects into Real XCT Data and Segmentation-Based Comparison for Advanced Probability of Detection Evaluation","authors":"Miroslav Yosifov,&nbsp;Bernhard Fröhler,&nbsp;Jan Sijbers,&nbsp;Jan De Beenhouwer,&nbsp;Johann Kastner,&nbsp;Christoph Heinzl","doi":"10.1007/s10921-025-01262-1","DOIUrl":"10.1007/s10921-025-01262-1","url":null,"abstract":"<div><p>This research proposes an approach for integrating realistic defects into computed tomography (XCT) scans by using X-ray simulations. It allows full control over different scenarios and measuring the detection algorithm efficiency in real-world situations. Using real XCT data of a pin-fin cooler made of aluminum alloy with complex internal structures, synthetic spherical and irregular defects ranging from 56 <span>(upmu )</span>m to 300 <span>(upmu )</span>m in diameter are superimposed to create a comprehensive dataset that mimics a wide range of realistic scenarios. This XCT dataset with superimposed defects is then utilized to apply a probability of detection analysis to detect defects of varying sizes and shapes. This analysis shows that for spherical pores, the detectability limit is up to 2.5 times higher in the superimposed case with a minimum voxel similarity of 95%, while for irregular pores, this limit is 3.3 times higher when a minimum voxel similarity of 80%. The integration of synthetic defects into real XCT images allows for a more rigorous and controlled assessment of detection algorithms, providing valuable insights into their performance under realistic conditions. Our findings demonstrate that this method can significantly improve the accuracy and reliability of measurements of defect detectability, offering a powerful tool for quality assurance in critical manufacturing processes.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 4","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10921-025-01262-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144923161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simultaneous Thickness Measurement of Double-Sided Coatings Using Acoustic Resonant Imaging Technique","authors":"Hyelin Kim,&nbsp;Hironori Tohmyoh","doi":"10.1007/s10921-025-01252-3","DOIUrl":"10.1007/s10921-025-01252-3","url":null,"abstract":"<div><p>In this study, the coating thickness of a double-sided coating processed by cathodic electrodeposition was measured and visualized using the acoustic resonant imaging technique. This method analyzes the resonant frequency and amplitude spectrum of echoes, enabling the visualization of thin coating thickness. Because changes in the coating thickness owing to the characteristics of the electrodeposition process are expected to influence the properties, we experimentally investigated the relationship between the coating thickness and the resonant frequency. An experimentally established relationship between coating thickness and resonant frequency indicated that a 33% reduction in thickness corresponded to an approximate 10% increase in sound velocity. Based on this relationship, the coating thicknesses on both surfaces were simultaneously visualized. Validation through cross-sectional microscopy confirmed the accuracy of the thickness measurements, with an average error of less than 2%, demonstrating the accuracy of this method for precise and simultaneous coating thickness evaluation.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 4","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10921-025-01252-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144923165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Computed Tomography-Based Pore Mesh Models Through Matching with Microscope Cross-Section Images","authors":"Sebastian Mansky,&nbsp;Malte Becker,&nbsp;Dirk Herzog,&nbsp;Ingomar Kelbassa","doi":"10.1007/s10921-025-01240-7","DOIUrl":"10.1007/s10921-025-01240-7","url":null,"abstract":"<div><p>X-Ray Computed Tomography (CT) is a widely adopted tool in the non-destructive quality assurance of additive manufacturing (AM). Porosity in AM can be assessed via CT without compromising the integrity of the part and without reliance on witness specimen. Reliable pore criticality analysis, essential for AM fatigue assessments, hinges on precise determination of pore dimensions. This work investigates CT data by comparing the pore sizes and shapes from two different data sources (CT and metallography), originating from the same samples. The comparison indicates a pore size underestimation in the CT data by an average of 20%. A subsequent rescaling and smoothing workflow on the CT pore data compensates this underestimation. This workflow reduces the mean pore size deviations between both data sources by up to 50% compared to the original data, allowing a more accurate pore assessment. Additionally the smoothing process reduces errors introduced by the CT reconstruction, lowering the average and scatter in mean curvature between pores. The rescaled and smoothed pores serve as an improved starting point for investigations regarding the effect of porosity on fatigue in AM.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10921-025-01240-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144861563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A New Dual-Mode Electromagnetic Acoustic Transducer for Preload Measurement of Superalloy Bolts","authors":"Yujia Zeng,&nbsp;Wenze Shi,&nbsp;Chao Lu,&nbsp;Yigang Cheng,&nbsp;Xuewei Zhang,&nbsp;Quanshi Cheng","doi":"10.1007/s10921-025-01233-6","DOIUrl":"10.1007/s10921-025-01233-6","url":null,"abstract":"<div><p>Electromagnetic ultrasonic measurement of preload in highly attenuating superalloy bolts faces two critical challenges: the low ultrasonic signal amplitude and impracticality of measurement without prior knowledge of the bolt’s initial condition. To address these issues, this study proposes a dual-mode electromagnetic acoustic transducer (EMAT) featuring a new permanent magnet configuration. For superalloy bolt specimens, the proposed configuration not only resolves the challenge of insufficient Longitudinal Wave (LW) excitation inherent to traditional EMAT but also achieves a 280.78% enhancement in Shear Wave (SW) signal amplitude. The experimental results on bolt preload measurement demonstrate that the regression model established from measurement data obtained by the new permanent magnet-configured EMAT exhibits a 0.0672 higher <i>R</i>² coefficient compared to that of the traditional EMAT. In the comparative analysis of bolt preload measurement accuracy between mono-wave and bi-wave methods, the relative errors for SW mono-wave, LW mono-wave, and bi-wave methods are 0.45%, 0.18%, and 0.51%, respectively. The new dual-mode EMAT proposed in this study provides a robust methodology and critical data references for aerospace engine bolt preload monitoring.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144861578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Drilled Shafts Imaging with 2D Ultrasonic Waveform Tomography","authors":"Bingkun Yang,&nbsp;Khiem T. Tran,&nbsp;Rodrigo Herrera,&nbsp;Kelly Shishlova","doi":"10.1007/s10921-025-01238-1","DOIUrl":"10.1007/s10921-025-01238-1","url":null,"abstract":"<div><p>Drilled shafts are the foundation of choice for heavily loaded structures, particularly in urban areas. However, their in-situ concrete casting process is vulnerable to the formation of foundation defects, requiring full-volume imaging of as-built drilled shafts for quality assurance. This study presents a novel two-dimensional (2D) acoustic full-waveform inversion (AFWI) method for high-resolution ultrasonic imaging of drilled shafts, capturing details both inside and outside the rebar cage at centimeter-scale resolution. The method is formulated using 2D acoustic wave equations and adjoint-state optimization, integrating Tikhonov and Total Variation (TV) regularizations to enhance solution stability and preserve sharp structural boundaries. Additionally, an approximate Hessian matrix is incorporated in the regularization gradient, significantly improving inversion accuracy, particularly in regions beyond the rebar cage. Validated through synthetic experiments, the method successfully reconstructs shaft boundaries and detects defects without requiring prior knowledge of design diameter. The mean radial boundary errors of 2.4 m diameter shafts without and with defect are 1.2 cm and 4.4 cm, respectively. To further evaluate its real-world performance, the method is applied to a full-scale drilled shaft measuring 2.4 m in diameter and 21.3 m in length. Experimental ultrasonic data are collected by the standard cross-hole sonic logging (CSL) at depths along the shaft length and inverted to obtain a 2D image of P-wave velocity (<i>V</i>_p) at each depth. Individual 2D <i>V</i>_p images are then combined into a 3D image of the whole drilled shaft. Results confirm that the AFWI approach effectively characterizes the entire shaft, providing high-fidelity imaging and precise boundary delineation with the mean radial error of about 3 cm. To our knowledge, this is the first reported application of full-waveform inversion on an actual drilled shaft, marking a significant advancement in quality assurance of cast-in-place foundations.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144861592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying Lead Water Service Lines Using Ultrasonic Stress Wave Propagation and 1D-Convolutional Neural Network","authors":"K. I. M. Iqbal,&nbsp;John DeVitis,&nbsp;Kurt Sjoblom,&nbsp;Charles N. Haas,&nbsp;Ivan Bartoli","doi":"10.1007/s10921-025-01236-3","DOIUrl":"10.1007/s10921-025-01236-3","url":null,"abstract":"<div><p>Water utilities across the United States face challenges in identifying lead water service lines without excavation, as existing non-destructive methods have notable limitations. This study introduces a non-invasive technology based on stress wave propagation to detect service line materials on both the public (utility) and private (customer) sides. Stress waves are generated at the curb-stop valve of the service line by striking an extension rod with an instrumented hammer, which records the input impact signal. Piezoelectric accelerometer sensors placed on the soil surface then detect the pipe’s responses (output signals). This technology was field-tested in 419 service lines across 20 cities of the US. The collected data underwent several signal processing steps for the calculation of the frequency response function (FRF). Since the data was collected from various cities and locations, there were significant variations in soil depth, soil properties, and surface conditions. These variations made it challenging to develop a physics-based algorithm that accurately differentiates lead from non-lead materials (such as copper, galvanized steel, and plastic). A 1D-Convolutional Neural Network (1D-CNN) was developed that uses combined real and imaginary FRF components as input to classify lead versus non-lead materials. The model was trained on 80% of the service line FRF data, with 10% used for validation and the remaining 10% for testing. To evaluate the model’s performance, a confusion matrix was employed to calculate accuracy, precision, recall, and F1 score using the testing data. The model achieved 80% accuracy on test data and 80.5% accuracy on 41 blind-tested service lines. These results indicate that the stress wave technology proposed in this study, combined with signal processing and 1D-CNN model, offers a promising solution for non-invasively identifying lead service lines in diverse field conditions.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10921-025-01236-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144861605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Metal-Polymer Assembly Dimensional Evaluation by X-Ray Computed Tomography: An Experimental Approach Through Relative Intensity Intercomparison","authors":"Daniel Gallardo,&nbsp;Lucía-Candela Díaz,&nbsp;José Antonio Albajez,&nbsp;José A. Yagüe-Fabra","doi":"10.1007/s10921-025-01234-5","DOIUrl":"10.1007/s10921-025-01234-5","url":null,"abstract":"<div><p>Accuracy of metrological inspection by X-ray computed tomography (XCT) relies on a good adjustment of evaluation settings. This can be challenging in multi material objects, especially if the differences of density are high. A good indicator of the attenuation of X-rays is the relative intensity (<i>I/I</i>_<i>0</i>): the difference between the beam energy emitted by the tube and received by the detector; however, it is not clear if it could be used alone for generalization. In this paper, an analysis of the attenuation ratio, represented by relative intensity, and its usage to define the expected quality variation of XCT measurements of metal-polymer assemblies is presented. An ad hoc test object has been designed including a polymeric base, interior polymeric cylinders and several outer metallic cylinders with two purposes: (i) obtain similar relative intensity in all projections, and (ii) create different scenarios with a range of <i>I/I</i>_<i>0</i> values. Experimental results confirm the trend observed in simulations, as better quality of the measurements in terms of metrological data and contrast-to-noise ratio (CNR) is directly related to higher <i>I/I</i>_<i>0</i> values. The threshold of <i>I/I</i>_<i>0</i> ≈ 0.16 has been found to be determinant for dimensional evaluation, as in presence of elements with higher radiopacity, lower– density features could present non– acceptable errors in their measurements. As well, it has been found that same attenuation values do not work similarly on different materials, as higher attenuation coefficient materials (in this case, steel vs. aluminium) create bigger noise levels (in the form of scatter). These findings will help to predict more easily the expected results on metal– polymer assemblies’ evaluation by XCT, being able to estimate more precisely the errors on dimensional measurements.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10921-025-01234-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144861606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SwinIR-based Dual-Domain Reconstruction for Sparse-View Computed Tomography","authors":"Jonas Van der Rauwelaert,&nbsp;Caroline Bossuyt,&nbsp;Stijn E. Verleden,&nbsp;Jan Sijbers","doi":"10.1007/s10921-025-01244-3","DOIUrl":"10.1007/s10921-025-01244-3","url":null,"abstract":"<div><p>Sparse-view computed tomography (CT) remains a significant challenge due to undersampling artifacts and loss of structural detail in the reconstructed images. In this work, we introduce DDSwinIR, a dual-domain reconstruction framework that leverages Swin Transformer-based architectures to recover high-quality CT images from severely undersampled sinograms. DDSwinIR operates in three stages: sinogram upsampling, deep learning-based reconstruction, and a residual refinement module that addresses domain-specific inconsistencies. While previous dual-domain deep learning (DD-DL) approaches improve reconstruction quality, they lack a systematic analysis of component contributions and do not generalize to unseen number of projections. DDSwinIR addresses these gaps through a modular and transparent design, allowing quantification of each network’s module. Our results highlight that early application of data consistency, especially after initial sinogram reconstruction, yields the most substantial and reliable improvements, particularly under extreme sparsity. We also introduce sparse-view concatenation, which enhances performance by improving feature propagation in highly undersampled settings. Extensive evaluation across varying numbers of projections reveal strong generalization when trained on sparse data and tested on denser configurations, but not vice versa, underscoring the importance of low-sparsity training. Compared to conventional reconstruction methods, DDSwinIR achieves superior artifact suppression and detail preservation. This work establishes DDSwinIR as an interpretable and generalizable solution for sparse-view CT, responding to the need for DD-DL reconstruction frameworks for practical applicability.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10921-025-01244-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144861562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantitative Analysis of Anisotropic Magnetic Memory Signals of Wire and Arc Additively Manufactured Low Carbon Steel","authors":"Yan Li,&nbsp;Sheng Bao,&nbsp;Jingxuan Hong","doi":"10.1007/s10921-025-01242-5","DOIUrl":"10.1007/s10921-025-01242-5","url":null,"abstract":"<div><p>In this paper, the anisotropic magnetic memory signals of wire and arc additively manufactured (WAAM) low carbon steel is investigated and quantitatively analyzed by tensile test. Five specimens (200 × 40 × 4 mm) with different print directions (0°, 30°, 45°, 60°, 90°) were extracted from the WAAM rectangular tubes for testing under tensile loads up to 60 kN. The residual magnetic field (RMF) on the surface of the specimens was measured using a TSC-PC-16 magnetometer. The distribution and evolution of RMF signals were presented and quantitatively analyzed. The findings demonstrate a clear anisotropic behavior in the tangential RMF responses, with orientation-dependent sensitivity to stress, while the normal component is less sensitive to material orientation. The correlation between magnetic memory parameters and the applied load was revealed. The linear relationship between the characteristic magnetic memory parameter and the printing angle has been established. There is a certain correlation between the RMF gradient signals and the surface morphology of materials, which can be used to characterize the roughness of additive parts. The results suggest that magnetic memory techniques show potential for non-destructive evaluation of WAAM-produced steel components, providing insights into stress distribution. These findings contribute to the advancement of quality control measures in additive manufacturing, promoting safer applications in critical structural environments.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144861564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}