Journal of Nondestructive Evaluation最新文献

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 (V_p) at each depth. Individual 2D V_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.

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.

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/I₀): 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/I₀ 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/I₀ values. The threshold of I/I₀ ≈ 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.

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.

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.

Additive manufacturing (AM) facilitates the creation of complex-geometry parts, driving advancements in lightweight aerospace components, high-efficiency engine cooling channels, and customized medical implants. However, ensuring the quality and reliability of AM parts remains challenging due to internal defects, surface irregularities, porosity, and residual trapped powder, which are often inaccessible to traditional inspection methods. Recent developments in X-ray computed tomography (XCT) and 3D X-ray microscopy (XRM), particularly systems equipped with resolution-at-a-distance (RaaD™) capabilities, enable high-resolution, non-destructive evaluation of AM components across multiple scales, from sub-micrometer to macroscopic levels. This paper explores modern XCT and XRM techniques for multiscale characterization of AM parts, focusing on their ability to detect and analyze defects such as porosity, cracks, inclusions, and surface roughness, while offering insights into defect formation mechanisms, material properties, and process-induced variations. The integration of deep learning (DL) frameworks, including Simurgh, DeepRecon, and DeepScout, enhances XCT/XRM workflows by reducing scan times, improving resolution recovery, and enabling accurate defect detection even with limited projection data. These DL-based methods overcome limitations of traditional reconstruction techniques, enabling faster, more reliable characterization of dense materials like Inconel 718 and novel alloys such as AlCe. Applications include process parameter optimization, high-throughput quality control, and multistage AM process evaluation, with DL-enhanced workflows accelerating analysis times from weeks to days. Correlative imaging approaches further validate XCT and XRM data against scanning electron microscopy (SEM) images of physically sectioned samples, confirming the accuracy of DL-based reconstructions and enabling comprehensive defect analysis. While challenges remain in generalizing DL models to diverse materials and imaging conditions, improvements in resolution, noise reduction, and defect detection highlight the transformative potential of these methods. This multiscale and correlative approach enables precise identification and correlation of microstructural features with the overall performance of AM components. By integrating advanced XCT, XRM, and DL techniques, this paper demonstrates a significant leap forward in AM characterization, offering valuable insights into the relationships between processing parameters, microstructure, and part performance, and driving innovations that enhance the quality and reliability of AM products for demanding industrial applications.

High-speed X-ray computed tomography (CT) of batteries in-line or at-line is a promising technique to obtain quality-relevant insights leading to an optimized production process and detection of defective batteries. By using a high-power micro-focus X-ray source and a photon-counting detector, CT scans can be obtained within seconds. Here we explore utilizing the simultaneous readout of multiple images at different energy-discriminating thresholds and recombining them to improve the quality of the reconstructed volumes to optimize different quality parameters relevant to battery inspection. Using a-priori knowledge, threshold optimization is performed. Evaluating the combined volumes shows that there is an ideal threshold, or combination of two thresholds, depending on what matric used to optimize contrast between specific feature of a specific sample. Further, the contrast of the jelly roll compared to the rest of the battery can also be improved by combining two different thresholds. The experiments highlight the importance of threshold optimization and the potential gain of combining two simultaneous acquisitions using different energy thresholds for fast CT scans with limited photon statistics.

In the industrial production of metals, surface defect detection is crucial for ensuring product quality and optimizing production line efficiency. Although deep learning algorithms are effective for detecting metal surface defects, their complexity can often slow down the detection process. To achieve a balance between detection accuracy and efficiency, this study proposes an enhanced and lightweight Real-Time Detection Transformer (RT-DETR) network and incorporates a multi-scale residual feature extraction (MSRFE) module, termed as MSRFE-RTDETR. The MSRFE module is specifically designed to manage varying defect shapes while reducing the parameter count. To further enhance detection accuracy, a context feature information fusion (CFIF) module is introduced, which integrates deep and shallow features to prevent information loss. Additionally, an efficient encoder based on additive attention (EEAA) is employed to overcome the limitations of matrix multiplication inherent in traditional multi-head attention mechanisms, thereby increasing the model's detection speed. Compared to the baseline model, the proposed algorithm improves the average precision on the public NEU-DET dataset by 2.4%, increases detection speed by 39.69 FPS, and enhances all lightweight metrics. Its generalization is validated on GC10-DET and ASSDD datasets, demonstrating superior performance.

In this paper, we propose the ultrasound imaging method, arbitrary virtual array sources aperture (AVASA), using signal sign coherence (SC) information to inspect thick, highly attenuating structural components and enhance image resolution. The AVASA-SC employs phased array (PA) parallel transmission to focus beamforming at multiple virtual sources, improve the signal-to-noise ratio (SNR) of received A-scan signals, and record the reflected signals with all the array elements. The high-resolution imaging is reconstructed on the reception by an AVASA beamformer that virtually focuses on each point in the inspection region through the coherence summing of the signal sign bit, reducing image processing time. AVASA effectively images thicker structures by focusing the ultrasound beam at virtual sources through exciting parallel transmission. However, in AVASA, the SNR of deeper reflectors can be reduced due to signal amplitude-based image reconstruction. Therefore, AVASA-SC uses the instantaneous signal sign bit of the AVASA beamforming aperture data to create imaging. To compare AVASA-SC’s defect SNR and imaging resolution for deeper-located defects, two test samples (one with known defects, one with unknown) were scanned using AVASA and full matrix capture-total focusing method (FMC-TFM) techniques. AVASA-SC significantly improves image resolutions, enabling enhanced defect characterization.