Journal of Nondestructive Evaluation最新文献

{"title":"Non-Destructive Inspection of BGA Solder Joints: A Review of Existing Techniques and the Potential of Laser Ultrasonic Inspection","authors":"Yong Hao Wong,&nbsp;Yew Hoong Wong,&nbsp;Zhi Ling Tai,&nbsp;Zhi Chao Ong,&nbsp;Chou Yong Tan","doi":"10.1007/s10921-026-01448-1","DOIUrl":"10.1007/s10921-026-01448-1","url":null,"abstract":"<div><p>Ball Grid Array (BGA) packages are widely adopted in modern electronic systems due to their excellent interconnect density, compact footprint, and superior electrical performance. However, detecting hidden solder connections and guaranteeing assembly reliability has become much more challenging due to the gradual reduction in solder joint pitch and standoff height. The detection of fine-scale internal defects, such as micro-voids, delamination, and early-stage cracking, is still a challenge for conventional non-destructive testing (NDT) methods including X-ray imaging and infrared thermography, especially in high-density fine-pitch Ball Grid Array (FBGA) assemblies. This review critically examines existing inspection techniques for BGA and FBGA solder joint defect detection, with emphasis on their capabilities, limitations, and applicability in production-like environments. This paper specifically highlighted acoustic-based inspection methods, including scanning acoustic microscopy and emerging acoustic mapping approaches such as laser ultrasonic inspection, which offer high sensitivity to subsurface discontinuities and potential for contactless, high-resolution inspection. Recent developments in acoustic imaging, coupling techniques, and scanning modalities are discussed to address significant drawbacks inherent in conventional inspection methods. The review identifies current research gaps and outlines future directions toward rapid, high-resolution, and manufacturing-compatible acoustic inspection solutions for advanced electronic packaging.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149339082","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":"Virtual ACFM-Type Butterfly Plot Analysis Based on Signal Synthesis using Uniform Eddy Current Probe Signals for Detecting Cracks in Coated Welds","authors":"Daigo Kosaka,&nbsp;Hiroyuki Nakamoto","doi":"10.1007/s10921-026-01449-0","DOIUrl":"10.1007/s10921-026-01449-0","url":null,"abstract":"<div><p>Periodic inspection of fatigue cracks in welded steel oil storage tanks is critical for structural integrity. Conventional techniques, such as Magnetic Particle Testing, often necessitate costly and time-consuming coating removal, which generates industrial waste and increases downtime. To address these challenges, this study proposes a novel \"Virtual ACFM-type Butterfly Plot Analysis\" using Uniform Eddy Current (UEC) probes to enable high-accuracy crack evaluation through coatings without complex hardware. The butterfly plot is a well-known technique associated with Alternating Current Field Measurement (ACFM) probes. The proposed method derives a synthesized horizontal magnetic field signal, denoted as <span>({V}^{prime}_{H})</span>, by spatially integrating the vertical magnetic field component, denoted as <span>({V}_{V})</span>, measured by a single detection coil of the UEC probe. This mathematical transformation facilitates the generation of ACFM-type \"butterfly plots\" from UEC signals. The effectiveness of this methodology was validated through finite element method simulations and experimental benchmarks on welded steel plates containing fatigue cracks. A significant advantage is that the integration process functions as a digital spatial filter, effectively mitigating high-frequency noise arising from surface roughness and improving the signal-to-noise ratio in the presence of complex weld bead geometries. Consequently, the synthesized <span>({V}^{prime}_{H})</span> signal demonstrated a superior signal-to-noise ratio in detecting fatigue cracks in welded joints compared to direct physical <span>({V}_{H})</span> measurements obtained from conventional ACFM probes. The proposed method is highly synergistic with automated measurement systems, where the integration of precise spatial data from a scanner maximizes its diagnostic potential. Furthermore, by utilizing a signal feature plane derived solely from the <span>({V}_{V})</span> signal, it is possible to determine whether a detected crack requires repair without performing complex calculations. This research demonstrates that sophisticated ACFM-level diagnostics can be achieved using the UEC probe. Ultimately, the combination of the UEC probe and the proposed virtual butterfly plot analysis offers a robust, efficient, and cost-effective solution for the non-destructive evaluation of coated welded structures in industrial environments.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149338663","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":"FT-CAT Net: A Nondestructive Image-Based Tool Condition Monitoring Framework for Wear Classification in Dry Turning of Inconel 718","authors":"V. Nagamalleswari,&nbsp;B. Surendra Babu","doi":"10.1007/s10921-026-01444-5","DOIUrl":"10.1007/s10921-026-01444-5","url":null,"abstract":"<div><p>Accurate tool wear monitoring is essential for maintaining machining quality, productivity, and tool life during the dry turning of Inconel 718. Conventional image-based approaches and standalone convolutional neural networks (CNNs) may have difficulty distinguishing different wear stages because of similarities in wear morphology. To address these limitations, this study proposes FT-CAT Net (Fine-Tuned CNN–Attention Transformer Network), a hybrid nondestructive image-based framework that integrates fine-tuned CNN feature extraction with Transformer-based contextual attention for automated tool wear classification. Microscopic flank-wear images were collected during controlled dry-turning experiments using a Taguchi L27 experimental design and categorized into early-stage, intermediate, and severe wear according to the ISO 3685 <span>({VB}_{MAX})</span> criterion. Following preprocessing and data augmentation, the final dataset comprised 1,572 images, including 515 early-stage, 554 intermediate, and 503 severe-wear images.Four pretrained CNN backbones, namely AlexNet, GoogleNet, ResNet50, and VGG19, were evaluated for spatial feature extraction. Among these architectures, VGG19 achieved the best standalone CNN performance with an accuracy of 97.5% and was selected as the backbone for the proposed FT-CAT Net. The CNN feature representations were subsequently transformed into token sequences and processed using a Transformer encoder with multi-head self-attention to capture contextual relationships among tool-wear features. The proposed FT-CAT Net achieved 98.2% accuracy, 98.0% precision, 97.9% recall, and 97.6% F1-score for three-stage tool wear classification. The results demonstrate that integrating fine-grained CNN spatial representations with Transformer-based contextual modeling provides an effective nondestructive, vision-based approach for automated tool wear monitoring during dry turning of Inconel 718.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149339102","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":"A Large Lift-Off Laser-Electromagnetic Ultrasonic Resonance Method with Magnetic Flux Concentration","authors":"Jing Hu,&nbsp;Wenze Shi,&nbsp;Chao Lu,&nbsp;Shiwen Cheng,&nbsp;Weiwei Chen","doi":"10.1007/s10921-026-01450-7","DOIUrl":"10.1007/s10921-026-01450-7","url":null,"abstract":"<div><p>Laser-Electromagnetic Acoustic Transducer (Laser-EMAT) is an important technique for evaluating damage and performance of metal materials in high-temperature and vibration environments. However, traditional EMAT has limitations including low signal-to-noise ratio (SNR), limited lift-off distance, and poor energy conversion efficiency. This research proposes a novel EMAT configuration with a magnetic concentrator (MC) placed between the laser source and the test specimen, referred to as MC-EMAT. MC-EMAT enhances magnetic flux density by adding the MC, thereby improving the amplitude of the ultrasonic echo signal and significantly increasing the lift-off distance. This enables the detection of defects such as corrosion thinning in structural components under complex environments. In this study, we introduce two configurations of MC-EMAT: one with a ring-shaped magnetic concentrator (REMAT) and another with a staircase-shaped magnetic concentrator (SEMAT). Given that the configuration and dimensions of MC-EMAT significantly affect the amplitude of the echo signal, this study employs orthogonal experimental methods to optimize these parameters. Subsequently, we conducted lift-off sensitivity experiments for Laser-MC-EMAT. Experimental results demonstrate that the optimized REMAT achieves a lift-off detection of 26 mm, while the optimized SEMAT achieves 30 mm.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149338664","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":"Temporally Gated Active Thermography for Defect Detection in Laser-Based Powder Bed Fusion of Metals","authors":"Christian Sauer,&nbsp;Dennis Höfflin,&nbsp;Andreas Schiffler,&nbsp;Alexander Versch,&nbsp;Jürgen Hartmann","doi":"10.1007/s10921-026-01419-6","DOIUrl":"10.1007/s10921-026-01419-6","url":null,"abstract":"<div><p>Laser Powder Bed Fusion of Metal is a key additive manufacturing technology, but its industrial adoption is limited by inherent process imperfections. While in-situ non-destructive testing is essential, current monitoring methods face limitations. In-line active thermography, as a promising testing approach, faces significant challenges with overpowering reflections from the high-energy process laser, which can saturate sensors and mask faint, defect-related thermal emissions. This paper introduces a novel stroboscopic active thermography methodology designed to overcome this limitation. The approach utilizes the Synchronized Path Infrared Thermography dual scanhead setup, which employs the processing laser as a modulated thermal excitation source. This system is based on precise temporal gating of the acquisition of the IR camera: it isolates the desired thermal emission signal from interfering laser reflections, and it ensures that the same, specific segment of the material cooling curve is consistently sampled for every measurement point. The stroboscopic approach was validated on specimens with artificial subsurface defects. It successfully eliminates sensor saturation, providing a high-fidelity thermal signal for analysis. The system identifies voids with actual mean diameters as small as 111 <span>(upmu )</span>m at depths of down to 40 <span>(upmu )</span>m. The study also quantifies the technical boundaries of the method, where signal attenuation from thermal diffusion limits detection at 120 <span>(upmu )</span>m depth to the largest defect measuring 977 <span>(upmu )</span>m.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10921-026-01419-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149235032","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":"Automated Quality Assessment and Non-Destructive Grading of Wood Veneers for Structural Veneers Using A Hybrid Transformer-CNN Model","authors":"Xinyi Wang,&nbsp;Haiwang Huang,&nbsp;Pengyan Zhuang,&nbsp;Bingzhen Wang,&nbsp;Jianping Sun,&nbsp;Linhong Zhao,&nbsp;Ziyin Xu","doi":"10.1007/s10921-026-01451-6","DOIUrl":"10.1007/s10921-026-01451-6","url":null,"abstract":"<div><p>Veneer constitutes a critical structural component in engineered wood products such as plywood, laminated veneer lumber (LVL), and cross-laminated timber (CLT), which are extensively utilized in modern civil engineering infrastructure. However, the inherent heterogeneity and rapid growth of raw timber frequently introduce structural defects, including knots, cracks, and voids, that severely compromise the mechanical integrity and load-bearing capacity of the final assembly. Traditional non-destructive evaluation (NDE) methods and standard convolutional neural networks (CNNs) often fail to distinguish these structural anomalies from complex natural grain patterns, leading to potential safety risks in construction applications. To address these challenges, this paper proposes a material-property-aware feature co-evolution network named Veneer-AEPNet (VAEPNet), specifically engineered for the high-precision non-destructive grading of structural veneers. The architecture explicitly integrates a dual-stage Transformer mechanism to establish a global texture baseline, effectively identifying macro-scale topological deformations. Simultaneously, we introduce the Adaptive Anisotropic Granular Pooling (AAGP) module to capture directional fiber perturbations and the Global-Contrastive Feature Refinement (GCFR) module to suppress natural background noise by modeling deviations from a global texture reference. The methodology was validated on a large-scale industrial dataset containing 25,000 high-resolution images. Experimental results demonstrate that VAEPNet achieves a test accuracy of 94.47% and an F1-score of 93.91%, achieved the best overall performance among the evaluated baseline models. Furthermore, visualization analysis confirms that the model effectively decouples structural defects from natural wood features, providing a robust solution for automated quality control in NDE 4.0 environments.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149189507","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":"Contactless Non-destructive Testing of Railway Axles with the GCLAD Optical Technique","authors":"Dario Vangi,&nbsp;Michelangelo-Santo Gulino,&nbsp;Giovanni Zonfrillo,&nbsp;Lorenzo Pugliese,&nbsp;Giacomo Boscagli","doi":"10.1007/s10921-026-01440-9","DOIUrl":"10.1007/s10921-026-01440-9","url":null,"abstract":"<div><p>Railway axles, though designed to last over 20 years, are fatigue-stressed components that may suffer premature failures with severe consequences. Ultrasonic techniques are among the most effective inspection methods, but their limitations–such as the need for physical coupling with the axle and long inspection times–have encouraged the exploration of innovative, contactless solutions. This paper presents the setup and analysis of ultrasonic data collected using Gas-Coupled Laser Acoustic Detection (GCLAD), while employing a pulsed laser as the emitting source. GCLAD is a contactless technology that enables point detection of waves in transmission and reflection at once, offering multiple opportunities to identify defects and discontinuities. The present preliminary study demonstrates the feasibility of inspecting railway axles without disassembling wheelset components, through a fully optical system that acquires and detects ultrasonic waves without contact. This approach maximizes automation and reduces inspection-related times. The devised experimental layout successfully identified defects in the wheel seat area (down to 2.7 mm), brake seat area (7.5 mm), and axle body (0.95 mm), even with mounted wheels. An industrial application highlights the potential suitability of GCLAD-based instrumentation for integration into actual inspection apparatus, supporting efficient monitoring of railway axles while paving the way for improved automation and reduced inspection times.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10921-026-01440-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149190021","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":"Distortion Mechanisms and Physics-Informed Intelligent Compensation of Eddy Current Testing Signals for Curved Surface Components","authors":"Tuan Zhu,&nbsp;Chao Lu,&nbsp;Wenze Shi,&nbsp;Jianshen Liu,&nbsp;Gongpeng Yang,&nbsp;Yiping Liang,&nbsp;Bingwen Wang","doi":"10.1007/s10921-026-01441-8","DOIUrl":"10.1007/s10921-026-01441-8","url":null,"abstract":"<div><p>The curvature characteristics of aero-engine turbine blades introduce significant interference in eddy current testing signals, readily causing waveform distortion and thereby hindering quantitative defect assessment. To address this issue, a physics-informed random forest compensation method is proposed, and the signal distortion mechanism induced by curvature variation is systematically investigated. Considering that non-differentiable models such as random forests cannot directly incorporate physical priors, a physics information embedding mechanism based on iterative target-value correction is developed. A curvature-domain Laplace operator is employed to construct the physical residual, which is subsequently fed back to the training labels to dynamically refine the prediction targets. To validate the effectiveness of the proposed approach, comparative scanning simulations on curved and flat surfaces as well as experimental verification using Inconel 718 specimens with artificial defects were conducted. Experimental results demonstrate that, compared with the standard random forest model, the improved method reduces the root-mean-square error (RMSE) for flat reference signal compensation under varying curvatures by an average of 72.4% and decreases the standard deviation (STD) by 18.9%. Under the extreme curvature condition of 5 mm, the RMSE improvement reaches 75.5%. In experimental validation, the RMSE values of the compensated signals for different curvatures are reduced by 18% and 96.7%, respectively, while the STD values are reduced by 14% and 18.9%. The compensated signals obtained by the proposed algorithm exhibit substantially higher fidelity to the flat reference signal, effectively mitigating distortion phenomena and facilitating quantitative defect signal analysis. The algorithm demonstrates pronounced physical robustness in curved-surface eddy current signal compensation, providing an effective technical pathway for the correction of eddy current signals acquired on curved geometries.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149189081","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":"Efficient Analytical Modeling Method for Pulse Eddy Current Testing Based on Morlet Wavelet Transform","authors":"Zhian Xue,&nbsp;Junxi Bi,&nbsp;Yujiao Fu,&nbsp;Dongdong Wen","doi":"10.1007/s10921-026-01425-8","DOIUrl":"10.1007/s10921-026-01425-8","url":null,"abstract":"<div><p>Pulsed eddy current (PEC) testing is a key non-destructive evaluation technique, but conventional FFT-based analytical modeling is computationally expensive due to full-spectrum impedance change evaluations up to the Nyquist limit. This paper proposes a novel modeling method using the Morlet wavelet transform. The method establishes a direct mapping between the wavelet scale factor and angular frequency, enabling time-domain reconstruction from discrete scale-dependent frequencies without global FFT sampling. An adaptive scale-truncation strategy further reduces computational cost by omitting negligible high-frequency components. Numerical results demonstrate that, with the optimal parameters (wavenumber 25 and discretization parameter 0.1), the proposed method requires only 96 frequency points, as opposed to 1000 at a sampling frequency of 1 MHz. This translates to a computational saving of over 90%, while maintaining relative errors below 1% at signal peaks. The method's accuracy and robustness are validated across wide parameter ranges and against experimental data. This work offers an efficient, theoretically sound alternative to FFT-based modeling, with potential for real-time inverse applications.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149189101","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":"PI-TransUNet: A Physics-Informed TransUNet Model for Steel Plate Defect Detection in Electromagnetic Tomography","authors":"Xianglong Liu,&nbsp;Kun Zhang,&nbsp;Ying Wang,&nbsp;Huilin Feng,&nbsp;Nan Wang","doi":"10.1007/s10921-026-01446-3","DOIUrl":"10.1007/s10921-026-01446-3","url":null,"abstract":"<div><p>Steel plate defect inspection based on electromagnetic tomography (EMT) has attracted increasing attention because of its ability to perform non-invasive imaging without direct contact with the inspected object. Nevertheless, accurate image reconstruction remains difficult owing to the highly ill-conditioned inverse mapping and the diffuse nature of electromagnetic fields, which often result in distorted defect boundaries and unstable reconstructions. Although deep neural networks have demonstrated considerable potential in EMT imaging, their predictions may deviate from underlying physical principles when trained solely from data. To improve both reconstruction accuracy and physical reliability, a physics-guided image reconstruction framework named PI-TransUNet is developed. The framework integrates a TransUNet-based inverse solver with a pretrained differentiable forward surrogate model, forming a closed-loop learning architecture. Spatial positional information is incorporated into the measurement sequence to strengthen topological awareness, while a physics-consistency objective is introduced to regularize the reconstruction process. Furthermore, a progressive weighting strategy is employed to stabilize optimization and facilitate the incorporation of physical constraints. Numerical simulations and laboratory experiments conducted on a nine-coil planar EMT platform demonstrate that the proposed approach achieves superior defect localization, enhanced boundary preservation, and improved robustness against measurement noise. Quantitative results indicate consistently high structural similarity and low reconstruction error, highlighting the effectiveness of PI-TransUNet for EMT-based steel plate defect imaging.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149188999","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}