Journal of Nondestructive Evaluation最新文献

This paper presents an integrated non-destructive evaluation method for monitoring thermal aging in P91 steel by analyzing magneto-acoustic emission (MAE) signals through wavelet packet transform (WPT). Samples were thermally aged for 0–600 h at 780 °C and tested under controlled excitation conditions of 30 V and 30 Hz. The resulting MAE signals were processed using level-3 WPT decomposition to obtain energy distribution ratio (EDR%) features across multiple frequency bands. These frequency-domain features were compared with changes in hardness, tensile properties, and impact energy, as well as metallographic observations showing a transition from fine-lath martensitic to coarsened ferritic structures. Lower-frequency energy (Node 0, 0–125 kHz) increased during early aging and then declined due to precipitate coarsening and boundary pinning, while mid-frequency energy (Node 1) showed complementary trends associated with evolving domain-wall interactions. Although the dataset is limited (n = 4), Pearson correlation and linear regression further confirmed that Node-specific EDR% tracks progression of mechanical degradation. Overall, the findings demonstrate that WPT-based MAE analysis offers a sensitive and practical approach for non-destructive condition monitoring of thermally aged P91 steel components.

As critical sealing components in well control equipment, the preload uniformity of flange bolt connections significantly influences the reliability of metal seals under high-pressure dynamic service conditions. However, non-uniform stress distributions in bolt groups caused by complex external loads can compromise sealing contact stress, thereby affecting the sealing performance. Existing detection methods have difficulties in accurately characterizing bolt stress states under coupled complex loads such as eccentric loading. This paper develops a combined magnetic-acoustic bolt stress detection system based on magnetic stress measurement and acoustoelastic effects. Laboratory experiments were conducted to validate an integrated methodology for identifying complex bolt stress states. Field tests under eccentric loading conditions show that the relative error between magnetic and acoustic axial stress measurements is below 6%. Under non-uniform preload and bending loads, magnetic stress measurements were used to identify linear axial stress evolution during elastic-stage pressurization, stress variation disparities, and tensile-compressive stress asymmetry on individual bolts.

X-ray computed tomography (CT) is a non-invasive diagnostic technology that has been widely used for various clinical applications. However, CT image quality becomes severely degraded when the X-ray dose is reduced. To reconstruct high-quality low-dose CT image, we present a sub-pixel anisotropic diffusion (SAD) for statistical iterative reconstruction (SIR), based on the penalized weighted least-squares (PWLS) model, termed as PWLS-SAD. Specifically, the SAD uses sub-pixel difference as a generalized form of the first-order derivative, replacing the original first-order derivative in anisotropic diffusion. An alternative minimization algorithm is used to solve the associated objective function. XCAT phantom simulations, anthropomorphic torso phantom measurements, and clinical data were used for the experiment. Experimental results show that PWLS-SAD technique achieves superior performance compared to competing methods, particularly in terms of suppressing image noise, enhancing the visibility of low-contrast structures, and maintaining edge detail.

Lightweight expanded vermiculite (EV) mortars based on calcium sulfoaluminate (CSA) cement are promising for high temperature applications. However, predicting their residual strength after moderate thermal exposure (70–100℃) remains challenging. This study employs advanced acoustic emission (AE) monitoring and machine learning (ML) to address this. The key contributions are twofold: First, a novel Radial Basis Function (RBF) kernel-based approach has been introduced to dynamically classify failure modes in RA-AF analysis, overcoming the limitations of fixed threshold approaches. Second, a newly developed grouped Gaussian noise (GGN) technique has been used to augment the dataset, which has improved the performance of the LightGBM (LGBM) regression model. Experimental results indicate that while EV content reduces flexural strength, heating at 100℃ restores it by up to 48%, likely due to the formation of crack-filling hydration products. The RBF-refined AE analysis reveals a distinct transition from tensile to shear-dominated failure with accumulating damage. The optimized LGBM model, trained on GGN-augmented data, achieved high prediction accuracy (R² = 0.99, MAE = 0.18, MSE = 0.06), outperforming other mainstream models. This work proposes a combined diagnostic-predictive framework for assessing lightweight EV mortars under moderate thermal stress.

Terahertz (THz) imaging has emerged as a powerful modality for nondestructive testing (NDT), especially for inspecting non-conductive materials where traditional X-rays and ultrasound techniques fall short. Here, a compressive sensing-based THz single-pixel imaging system optimised for real-time nondestructive testing and evaluation of composite materials has been employed. Different structured and random sensing masks have been employed, namely, the discrete cosine transform (DCT), Hadamard, Gaussian, Bernoulli, and random. The impact of various mask reordering strategies, including Cake-Cutting, Total Gradient, and Total Variation, on the image quality has been systematically examined. Image quality has been quantitatively assessed using Mean Square Error, Peak Signal-to-Noise Ratio, and Structural Similarity Index Measure metrics across different sampling ratios and noise levels. A novel Deconvolved Energy (DE) reordering has been proposed and implemented, where a descending reordering has been carried out based on the energy of the mask pattern deconvolved with the Tikhonov regularised blur kernel. From the results, it is evident that DCT-based masks consistently outperform others in terms of THz image reconstruction fidelity and computational efficiency, especially when paired with DE reordering. The generalizability of the proposed methodology has been validated by different THz images acquired with a variety of defects across different composite materials. From the results, it is evident that the proposed methodology achieves robust THz image reconstruction even in under-sampled scenarios and in the presence of noise, with significantly reduced CPU time, establishing a high-performance and scalable framework ideally suited for THz-based nondestructive testing and real-time imaging applications.

Vibrothermography using vibration excitation at specific frequency to activate a resonance in a defective area (local defect resonance, LDR) is promising for magnifying vibration induced heating and facilitating the detection of defects. However, the technique is limited by the requirement for a knowledge of vibrational responses to determine the resonance frequency. In this paper, a method for the automated determination of an optimal probing frequency only from surface temperature is proposed. A 3D finite element model and an experimental setup are established. The rate of temperature rise is proposed to better indicate the occurrence of LDR of an artificial delamination in carbon fiber reinforced polymer (CFRP). The defect-to-background contrast (DBC) is defined to quantify the enhancement of thermal imaging. Results from long-pulse vibrothermographic experiments show that the highest signal-to-noise ratio of delamination detection is achieved when the probing frequency is selected at the peak of DBC calculated from the rate of temperature rise. The identified probing frequency is stable for various bandwidths of sweep. The proposed method can improve the signal-to-noise ratio of thermal imaging of delamination in CFRP.

Sparse-view computed tomography (CT) can reduce acquisition times, supporting inline industrial inspection in suitable settings. In practice, scan time may also be shortened by lowering exposure per view, using faster detectors or motion systems, or leveraging partial/parallel acquisition; here we focus on reducing the number of projections. Fewer projections, however, can introduce streak artifacts that cause measurement deviations during metrological evaluations. This paper presents two self-supervised deep learning approaches using implicit neural representations (INR) to mitigate sparse-view artifacts and enhance measurement accuracy. Both methods represent the 3D object volume using a multi-layer perceptron (MLP) optimized individually for each scan through an incremental forward-backward strategy. The first approach, Neural Representation with Sparse-View Volume-based Loss (NR-SVOL), employs volume-domain training using an initial filtered back-projection (FBP) volume, enabling rapid artifact reduction with limited computational overhead. The second, Neural Representation with Sparse-View Projection-based Loss (NR-SPRO), directly optimizes the INR to match measured sparse projections, analogous to Neural Radiance Fields (NeRF), yielding superior artifact compensation at the expense of increased computation. Comprehensive evaluations were conducted on three industrial objects, a gear, a cylinder head, and a connector, at varying sparse-view configurations (32–256 projections). Both NR-SVOL and NR-SPRO demonstrated substantial artifact reduction, decreasing surface deviations by up to an order of magnitude in standard deviation. NR-SVOL achieved results within approximately five minutes, suggesting compatibility with some inline cycle times for our tested parts, while NR-SPRO delivered even higher accuracy when allowed more computation. This study highlights a practical trade-off between speed and precision, showcasing the potential of these methods for sparse-view inline industrial CT for improved metrological quality.

Manual evaluation and interpretation of Impact echo (IE) data is often labor-intensive and time-consuming, motivating the growing interest in applying machine learning (ML) techniques to this non-destructive testing (NDT) method. However, the scarcity of labeled datasets limits the generalizability of ML models to new, unseen data. This study investigates strategies to integrate a-priori knowledge into Convolutional Neural Networks (CNNs) for improved prediction of the S1 Lamb wave frequency from IE signals. To this end, time–frequency representations of IE signals, derived using the Short-Time Fourier Transform (STFT), are used as model inputs. A-priori knowledge is introduced in the form of initial frequency estimates obtained through manual evaluation. Additionally, transfer learning is employed to enrich the limited measurement dataset with data from 2D numerical simulations. The results demonstrate that, although training loss curves remain similar across models, incorporating additional information significantly enhances performance on unseen datasets. Furthermore, pre-training with simulation data accelerates convergence during early fine-tuning stages. The highest predictive accuracy was achieved when the initial guess was directly embedded into the loss function.

This article examines the effectiveness of non-destructive testing (NDT) in assessing wood under impact loadings. Our research was to evaluate the feasibility of using the frequency resonance technique (FRT), to predict the behaviour under impact of thermally modified timber (TMT) compared with a control sample of untreated wood. Wooden planks from five different species were subjected to a thermal modification process (TMP) under two different regimes. Both the TMT and control samples were evaluated using NDT to measure their dynamic modulus of elasticity (MOED), logarithmic decrement of damping (LDD) and acoustic conversion efficiency (ACE). Subsequently, wood samples from the same species were tested using drop-weight impact tests to measure their inflicted maximum force and impact bending strength (IBS), while high-speed cameras recorded the impacts to measure the maximum deflection of the specimens. The results revealed that the only relatively efficient prediction of FRT was the relationship between MOED and IBS. The ACE and LDD results did not show any acceptable correlations with impact tests, indicating that NDT is not reliable for assessing maximum force and deflection in the wood species under impact. Our study also found that the efficiency of the results and predictions were influenced by the wood species and the TMP conditions, necessitating a large number of samples for each species and heat modification temperature to achieve accurate NDT results. Our study found that the efficiency of NDT predictions was significantly influenced by both wood species and the TMP conditions. Specifically, oak showed a relatively higher coefficient of determination, while ash had the lowest. The thermal treatment also had a varied effect on NDT's ability to determine IBS, increasing its efficiency for larch specimens while decreasing it for ash and beech, with no significant effect on oak and spruce. These findings imply that future NDT methodologies must be developed with a species-specific approach and calibrated for each unique modification condition. Consequently, achieving accurate NDT results will require comprehensive data sets with a large number of samples for each species and heat modification temperature.

Diffuse ultrasound is a promising technique for estimating the depth of surface-breaking cracks in concrete. However, practical use in the field has been limited by the necessity of establishing the crack depth-lag time relationship, only derived by time-consuming finite element simulations. Running these time-consuming simulations on-site is impractical, especially when rapid assessment of damage over large areas is crucial. This research addresses this limitation by recently proposed theoretical diffuse energy velocity concepts, to directly correlate with depth of surface-breaking cracks. Existing datasets for artificial notches in concrete specimens and actual surface-breaking cracks in reinforced concrete beams subjected to four-point bending are utilized to evaluate the performance of the proposed method. The results indicate that the diffuse ultrasonic method based on the diffuse energy velocity provides more accurate crack depth predictions compared with conventional approaches. More importantly, the simplicity of using the theoretical diffuse energy velocity approach eliminates the need for time-consuming finite element simulations, enabling rapid, on-site crack depth measurements. This enhancement significantly improves the utility of the diffuse ultrasonic method for field applications. Therefore, this research highlights the potential of the diffuse ultrasonic method, enhanced by the theoretical diffuse energy velocity approach, to serve as a reliable and efficient commercial tool for field inspections of concrete structures.