Journal of Nondestructive Evaluation最新文献

Ultrasonic testing (UT) is a commonly used method to detect internal damage in composite materials, and the test data are commonly analyzed by manual determination, relying on a priori knowledge to assess the status of the specimen. In this work, A method for the automatic detection of delamination defects based on improved EfficientDet was proposed. The Swin Transformer block was adopted in the Backbone part of the network to capture the global information of the feature map and improve the feature extraction capability of the whole model. Meanwhile, a custom block was added to prompt the model to extract object features from different receptive fields, which enriches the feature information. In the Neck part of the network, the adaptive weighting was used to keep the features that were more conductive to the prediction object, and desert or give smaller weights to those features that were not desirable for the prediction object. Two kinds of specimens were prepared with embedded artificial delamination defects and delamination damage caused by low-velocity impacts. Ultrasonic phased array technology was employed to investigate the specimens and the amount of data was increased by the sliding window approach. The object detection model proposed in this work was evaluated on the obtained dataset and delamination in the composites was effectively detected. The proposed model achieved 98.97% of mean average precision, which is more accurate compared to ultrasonic testing methods.

A multi-detector fast neutron radiography panel was built using the previous work on scalable neutron radiography using the IDEAS ROSSPAD readout module. A new aluminum housing was built to accommodate a large number of detectors tiled together. Additional changes to startup and processing code were made to operate the detector as one cohesive unit. Spatial resolution of the full panel using Cs-137 gammas was reported to be 0.42 line pairs per centimeter at 90% MTF and 2.09 line pairs per centimeter at 10% MTF. Three neutron radiographs generated using a Cf-252 fission neutron source were used to determine the spatial resolution of the panel for neutrons. The experiments had 90% MTF values of 0.24, 0.3, and 0.27 line pairs per centimeter and 10% MTF values of 1.30, 1.46, and 1.40 line pairs per centimeter. An example neutron radiograph was also used to prove that the radiography panel can perform true neutron radiography.

The electrochemical indicators including corrosion potential (E_corr), concrete resistivity (ρ), corrosion current density (i_corr), and polarization resistance (R_ρ) are pivotal in the evaluation of the degradation state of reinforcements embedded in concrete. Notwithstanding, extensive investigations traditionally hinge on a singular electrochemical metric for the appraisal of rebar corrosion. The current study transcends this conventional approach by integrating multiple electrochemical detections, significantly improving the accuracy in ascertaining the corrosion status of reinforcing bars within concrete. In this paper, a Bayesian network model is developed, synthesizing results from four electrochemical indictors obtained from published literatures. This model effectively addresses the challenge of integrating unmeasured electrochemical parameters in cases where only a limited set is tested in practical engineering, culminating in a more comprehensive assessment dataset. Further, this study progresses to quantitatively assess the reinforcement corrosion status by devising and fine-tuning an integrated model. The Bayesian network notably excels in extrapolating untested results and accurately determining the thresholds for rebar corrosion status, thus significantly improving the overall assessment capability. The Bayesian network, as employed in this study, computes median E_corr and i_corr values at -282mV and 0.168µA/cm², respectively. These computed values exhibit a deviation within 15% of experimental data, aligning with the uncertainty range stipulated by the ASTM C876-91 standards.

Some metal structures in the aerospace and nuclear industries are subjected to repeated impact loads that accumulate microcracks until fracture, called impact fatigue damage, which will compromise the metal structure’s overall strength and fatigue life. The microcracks generated by impact fatigue damage on metal materials are so small that, at present, only some microscopic characterization methods have been used to evaluate its damage level, such as scanning electron microscopy (SEM), electron backscatter diffraction (EBSD), energy X-ray dispersive spectroscopy (EDS), and X-ray Photoelectron Spectroscopy (XPS). There is a lack of more convenient and effective non-destructive testing methods. In this paper, the combination of nonlinear acoustic modulation and coda wave interferometry is used to detect impact fatigue damage on 40Cr steel specimens. The simulation discusses the observability of local elastic modulus reduction caused by impact fatigue damage in nonlinear coda wave interferometry (NCWI). Finally, NCWI experiments were carried out on six 40Cr steel specimens with different impact times. Results show that the proposed method can effectively detect and quantify the metal impact fatigue damage.

Recent advancements in non-destructive evaluation (NDE) techniques have demonstrated potential in assessing underwater structural integrity. However, evolving maritime structures demand more efficient, user-friendly, and technologically advanced underwater NDE methods. Building on successful applications in air as a medium, this paper explores the feasibility of utilizing coplanar capacitive sensors to gauge structural integrity in underwater environments, drawing on assertions made by pioneering scholars. The study employs simulations, complemented by experimental validation, to assess its viability. With artificial surface defects in both non-conducting and conducting specimens, this study conducts a comprehensive comparison of the performance between the bare-electrode and insulated-electrode coplanar capacitive sensor (CCS). The outcomes affirm the viability of utilizing the technique for underwater NDE. Notably, the study reveals that electrical conductivity is a significantly influential factor, and there are discernible differences in response between the two sensor configurations. The nature of the response in non-conducting materials is intricately tied to the dominant sensitivity value region. However, detecting defects in conducting materials poses a challenge in some instances. Overall, results show that defect detection, characterisation and imaging under water are feasible, thereby emphasizing the techniques potential for underwater NDE. This study broadens underwater NDE knowledge and offers a viable alternative for inspecting structures and equipment in underwater environments.

Acoustic emission (AE) is a non-destructive technique that relies on monitoring naturally occurring sources of high frequency ultrasound in components and structures. Ultrasonic waves propagate in the form of different wave modes—for instance Lamb waves in thin plates, or Rayleigh and P- and S- waves in bulk structures. Those wave modes have different properties, but also contain information regarding the source of the naturally occurring wave. Manually, the wave modes can be recognized by comparing a time–frequency representation of the signal to the dispersion curves expected in the tested object. For analyzing a large number of signals, this manual mode recognition becomes a tedious process. This paper proposes a method to automate the wave mode recognition based on some minimal knowledge of the occurring wave modes. As inputs, only the propagation speed of the possible wave modes and the source position need to be provided along with a limited set of reference wavelets for each wave mode. Cross-correlation of a signal with a reference wavelet of a mode reduces the signal to a limited number of peaks that may delineate the start of the mode. Using other signals from the same event but from different sensors, velocities are calculated for each peak in order to select the peak that corresponds to the arrival of the mode under investigation. To validate the method, a dataset was recorded based on four types of out-of-plane sources: Hsu-Nielsen sources of 0.3 and 0.5 mm, sensor pulse signals and AEs from melting ice. Since the presented dataset was recorded on a plate, the aim of the validation was to recognize the zero-order symmetrical and anti-symmetrical Lamb modes. The results of the proposed mode recognition method applied to this dataset are compared with results from manual mode recognition. For Hsu-Nielsen sources, the succes rate is found to be above 95%. For narrow-band pulsed signals or for AEs from melting ice with a low signal-to-noise ratio, succes rates between 75 and 80% relative to manual mode recognition are reported.

This paper proposes a method for obtaining adaptive and high-precision surface meshes directly from Industrial computed tomography (ICT) projection data. Firstly, an adaptive volume octree is recursively constructed from top to bottom using a two-stage geometric error metric function. The CT values and gradient values at the nodes are computed using the Feldkamp–Davis–Kress (FDK) reconstruction algorithm and its derivatives, achieving sub-voxel precision. Next, feature vertices are calculated based on Quadratic error functions (QEFs), and a dual mesh is constructed. Finally, Hermite interpolation is used to determine the iso-surface vertices, and the Convex Contouring lookup table is employed to accurately extract the iso-surface contours, resulting in high-precision and crack-free surface meshes. Experimental results show that the surface meshes generated by the proposed method exhibit superior dimensional accuracy, form and position accuracy, and surface model accuracy compared to traditional methods, and the dimensional accuracy has been enhanced by approximately 10–30%.

Metal additive manufacturing (MAM) technology provides a direct and efficient way for large-scale, integrated, and sophisticated engineering components in the aerospace field. Non-destructive testing (NDT) technique has been proven to be a significant method for quality evaluation of MAM components without destructing the integrity and performance of the components. However, it is still a challenging task that how to accurately and efficiently achieve the quality evaluation of large-scale and complex MAM engineering components using NDT technique. Nowadays, most studies mainly focus on the quality evaluation of small specimens or simple structure components, with comparatively less on the assessment of large-scale or complex engineering components. Thus, this review briefly introduced three urgent demands for quality evaluation of as-fabricated large or complex structure components and eight conventional NDT techniques possibly used for the quality detection of MAM. Four main challenges and future development trends in NDT technique are discussed in detail according to testing ability, data processing ability, and test standards. Among the future development trends, the application of machine learning and digital twins in NDT technique are the most promising method for intelligent detection and quality prediction of components. This work aims to provide a insight to enlarge the application of engineering components fabricated by MAM.

This paper introduces a novel approach for characterizing and estimating the size of incipient cracks, employing Jensen–Shannon divergence and Wasserstein distance for precise measurement. A novel signal correction method is proposed and coupled with Finite Element Modeling can extend the experimental data. The method is verified to accurately quantify incipient cracks with areas as small as 0.02 mm(^2), with a maximum relative error of 3.5% in surface estimation, and accurately discern variations in crack sizes. This allows for more accurate predictions of crack dimensions crucial for structural health monitoring.

Guided waves propagating in mechanical structures have proved to be an essential technique for applications, such as structural health monitoring. However, it is a well-known problem that when using non-stationary guided wave signals, dispersion, and high-order vibrational modes are excited, it becomes cumbersome to detect and identify relevant information. A typical method for the characterization of these non-stationary signals is based on time–frequency (TF) mapping techniques. This method produces 2D images, allowing the study of specific vibration modes and their evolution over time. However, this approach has low resolution, increases the size of the data, and introduces redundant information, making it difficult to extract relevant features for their accurate identification and classification. This paper presents a method for identifying discontinuities by analyzing the data in the TF maps of Lamb wave signals. Singular Value Decomposition (SVD) for low-rank optimization and then perform foreground feature extraction on the maps were proposed. These foreground features are then analyzed using Principal Component Analysis (PCA). Unlike traditional PCA, which operates on vectorized images, our approach focuses on the correlation between coordinates within the maps. This modification enhances feature detection and enables the classification of discontinuities within the maps. To evaluate unsupervised clustering of the dimensionally reduced data obtained from PCA, we experimentally tested our method using broadband Lamb waves with various vibrational modes interacting with different types of discontinuity patterns in a thin aluminum plate. A Support Vector Machine (SVM) classifier was then implemented for classification. The results of the experimental data yielded good classification effectiveness within reasonably low computational time despite the large matrixes of the TF maps used.