Journal of Nondestructive Evaluation最新文献

Corrosion is a critical factor in the degradation of metallic structures, especially in sectors such as maritime, aerospace, and civil infrastructure. Traditional corrosion assessment techniques, while widely used, are limited by their point-based measurements, labor intensity, and susceptibility to human error. This study proposes an advanced non-destructive evaluation (NDE) method combining guided wave propagation with probabilistic imaging to assess global corrosion damage and surface roughness in metallic plates. The approach utilizes Lamb waves, which are sensitive to thickness variations and capable of propagating over large areas with minimal sensor deployment. Key indicators such as wavefront asymmetry, residual signal energy, and correlation-based metrics (RAPID) were analyzed both numerically and experimentally. Corroded plates were modeled using Gaussian random fields and validated through controlled electrochemical degradation experiments. Results demonstrate that guided wave-based indices can effectively detect and monitor corrosion progression, with sensitivity to both the degree of material loss and surface irregularity. Additionally, a reference-free method based on wavefront asymmetry showed potential for practical, in-situ applications. The findings confirm the viability of guided waves as a powerful tool for structural health monitoring, offering enhanced spatial coverage, automation potential, and early-stage damage detection capabilities.

This study investigates the impact of subsurface defects on surface temperature distributions through Pulsed Infrared Thermography (PIRT), employing a connection between MATLABl software and COMSOL Multiphysics for the simulation of 300 distinct defect depths, culminating in the analysis of 450,000 thermal images. The Hough Circle Transform (HCT) was applied to a specific time frame from the infrared video sequence, where defect signatures were pronounced, to precisely localize defect regions. For defect quantification, a normalization method was implemented, calculating the ratio of the thermal norm within defect regions to that of non-defective areas. A Long Short-Term Memory (LSTM) network was then trained to map the temporal evolution of this norm ratio to a specific defect detection time. A Photopolymer Resin sample, fabricated with varying depths of defects via Stereolithography Apparatus (SLA) 3D printing, underwent Non-destructive testing (NDT) using Pulsed Infrared Thermography (PIRT). An integrated algorithm was employed for the identification of defect regions and the subsequent calculation of the times at which defects were identified. The depths of unknown defects were predicted employing a least square fitted curve, derived from four samples with known defect depths. This methodology achieved a notable degree of precision. This research delineates a relationship between the depth of a defect and its detection time, introducing an innovative approach for the accurate prediction of defect depths within the realm of Non-destructive testing (NDT).

This study presents a comparative evaluation of statistical and machine learning (ML) models for predicting moisture content (MC) in lightweight foamed concrete (LFC) using nondestructive microwave reflection parameters. Five models were examined: Multiple Linear Regression (MLR), Support Vector Machines (SVM), Random Forest (RF), Levenberg-Marquardt Neural Network (LMNN), and Radial Basis Function Network (RBF). Two dataset formats were used: a frequency-structured dataset (FSD) capturing full-spectrum information, and an instance-based dataset (IBD) designed for single-frequency applications. Model performance was assessed using R², RMSE, MAE, training time, and prediction speed, with five-fold cross-validation applied to evaluate generalization. Results showed that ML models outperformed the statistical model in capturing non-linear relationships. RF and LMNN achieved the highest accuracy and stability across both datasets, while RBF and MLR showed signs of overfitting, especially on FSD. Sensitivity analysis using permutation feature importance revealed that S11 magnitude was most influential in structured data, whereas input importance was more evenly distributed in IBD. The findings emphasize the importance of aligning model choice with dataset structure to improve accuracy and robustness. This study supports the development of real-time, nondestructive moisture monitoring systems in LFC for sustainable construction applications.

In order to mitigate the loss caused by hole damage during the service of carbon fiber-reinforced polymer (CFRP) axle tubes, this paper proposes a modal parameter-based approach to identify hole damage. The method focuses on single-hole, double-hole, and triple-hole damage as the objects of study, with fiber Bragg grating sensors for data collecting and strain mode shapes serving as the indicator for damage determination. The damage area of the axle tubes is localized based on the difference in strain mode shapes, and the degree of damage is identified using deep neural networks (DNN). The results indicate that the method of identifying the hole damage of CFRP axle tubes based on modal parameters is highly accurate, with all damage locations reliably identified, and the maximum relative error in damage degree identification is -12.95%. This study is highly significant for enhancing maintenance efficiency and prolonging the service life of CFRP axle tubes.

This work presents an investigation of acoustic emission (AE) behavior during compression-after-impact (CAI) tests on thermoplastic composites subjected to different impact energy levels. AE sensing is employed to detect and evaluate damage that may not be immediately visible. To the best of the authors’ knowledge, existing literature provides limited insight into CAI performance of thermoplastic composites, especially under relatively high impact conditions, an important gap given the rising use of thermoplastics in advanced air mobility applications. The primary objective is to analyze AE signals recorded during CAI tests and characterize their features across various impact energies, with the longer-term goal of enabling applications in advanced methods such as machine learning and artificial intelligence. For each test, results include time-dependent signal density, peak frequencies, and amplitudes, along with cumulative signal strength (CSS) to track the progression of damage at each impact level. The discussion further explores correlations between AE features and includes metrics like peak frequency density to highlight the relative influence of different features and link specific frequency ranges to distinct failure modes. Additionally, optical microscopy revealed four main failure mechanisms: matrix cracking, delamination, debonding between fiber and matrix, and fiber breakage.

In the nondestructive testing industry, probability of detection (POD) studies can be prohibitively expensive because of specimen and testing costs. In model-assisted probability of detection (MAPOD) analysis, physics-based model simulations are used to reduce the number of specimens needed to compute POD curves, but current MAPOD practices require precision simulations. These simulations need to have high enough accuracy to be treated as equivalent to real experimental data, perhaps after calibrating a “transfer function” that corrects their output. Not all simulators will have that level of accuracy, but when there are multiple inspection scenarios, the simulator accuracy can be assessed as part of the MAPOD process. This paper proposes Model-Quality-Adaptive POD (MoQuAPOD) which adapts to the demonstrated quality of a simulator across multiple inspection scenarios. The approach extends the traditional MAPOD concept of a simulation-to-experiment transfer function by acknowledging the uncertainty in its estimation. Stochastic transfer functions are evaluated and calibrated as a population over the range of inspection scenarios as part of the POD process. A hierarchical Bayesian model uses simulator predictions to draw strength across that population, reducing the number of specimens required to achieve a particular POD and confidence. We obtain POD estimates and population statistics from a Markov Chain Monte Carlo (MCMC) sampler. Population uniformity implies trustworthiness of the simulator, so that trustworthy simulators need less experimental data. An example illustrates model-quality-adaptive multi-scenario POD methods reducing the required number of specimens by 25%, compared to single-scenario POD methods, using synthetically generated data.

In the design of lightweight and extended-span structures, cable-based systems have been widely used and may be the only solution, particularly for superlong-span bridges and spatial structures. Prestressed anchor cables are the most crucial element in cable-based structures and directly influence their performance. However, prestressed anchor cables are prone to corrosion and fatigue due to environmental factors, complex loads, and chemical influences. Therefore, to ensure safety, developing a real-time monitoring method to evaluate the status of the prestressed cable is urgently needed. This paper introduces Acoustic Emission (AE) integrated with machine learning to enhance the diagnosis and prognosis of prestressed anchor cables. In the laboratory, twelve cables with varying defects were tested to failure to optimize the machine learning framework. Meanwhile, AE signatures due to damage progression were characterized and identified. Moreover, a machine learning framework for prestressed anchor cables was developed in terms of k-means and k-Nearest Neighbor (K-NN) clustering to distinguish AE signatures precisely due to different AE sources from large-scale data. A precursor signal for cable fracture was also extracted and recommended for early-stage warning. Furthermore, the in-situ recorded AE signal has validated the proposed framework. This study provides guidelines for using AE as a valuable tool for the Structural Health Monitoring (SHM) of prestressed anchor cables.

Detection of debonds in honeycomb sandwich-type structures has been a subject of interest for many researchers. However, detection and quantification of debonds in sandwich structures by methods adaptable for structural health monitoring is still being pursued. Low frequency guided waves are being used for the detection, localization and quantification of the debonds, with some limitations, like quantification of the damage being dependent on the distance of the sensor from the debond, need for a reference signal from the pristine structure etc. The present work investigates the potential of the low frequency guided waves in the detection, localization and quantification of debonds in Honeycomb Sandwich Structures (HSS) and addresses some of these limitations. A unique debond quantification curve for the given HSS is generated using a 3D finite element model and validated experimentally. Unlike in earlier works, these curves are independent of the distance of the sensor from the debond. The methodology is demonstrated in pulse-echo and pitch-catch configurations, and it does not need a reference signal from the pristine structure. The developed method is effective in detecting and quantifying the debond located on both the face skins of the sandwich, with the sensor mounted only on one face skin. An efficient methodology to assess the size of the debond is proposed, based on the results obtained from this study. The guided waves are actuated and sensed by Lead Zirconium Titrate (PZT) transducers which facilitate the implementation of structural health monitoring.

In intelligent nondestructive evaluation (NDE), overfitting on small datasets poses a significant limitation to the generalization of ultrasound classification models across different materials. Consequently, the development of effective regularization techniques is crucial for designing robust multi-material NDE systems. In this paper, we propose a versatile and lightweight dual-regularization module comprising two sub-modules: Acoustic Velocity-Guided Dropout (AVGD) and Squeeze-and-Excitation (SE) attention. The AVGD integrates physical domain knowledge with conventional regularization methods by dynamically adjusting the dropout rate of feature channels based on acoustic velocity information. Meanwhile, the SE attention mechanism enhances critical features in conjunction with dropout, thereby improving the model’s learning capacity. Both sub-modules are encapsulated into a dropout layer and an SE block, respectively, and seamlessly integrated into a classical neural network architecture. The proposed method is evaluated on a collected ultrasound signal dataset and compared against standard regularization mechanisms. Experimental results demonstrate that the dual-regularization mechanism significantly enhances the generalization capability of the baseline model.

This study systematically addresses critical challenges associated with blurred edges of wafer defects, including multi-dimensional feature aggregation, abrupt gradient decline, and hierarchical information loss. To tackle these issues, an innovative, precise segmentation method is proposed based on a dual cross-attention mechanism and a feature gradient histogram. Through in-depth analysis of edge-blurring characteristics in wafer defects, a multi-scale embedding matrix equation was formulated to optimize the contour extraction process. Additionally, a multi-level encoder architecture was implemented to enhance the efficiency of edge contour information extraction. To address boundary information loss during segmentation, a boundary gradient optimization model was constructed using multi-scale differential equations, enabling accurate fitting of boundary gradients through feature recombination vectors. Experimental results demonstrate the effectiveness of the proposed wafer defect segmentation approach. The method achieves an average accuracy of 97.51%, with mean intersection-over-union (mIoU) scores exceeding 89% across three distinct types of wafer defect detection tasks. By effectively mitigating the adverse effects of edge blur on segmentation precision, this approach offers a comprehensive solution for wafer defect detection. The contributions of this research not only enhance the accuracy and reliability of defect identification but also provide robust technical support for improving product quality and manufacturing efficiency in high-end semiconductor industries. These advancements hold significant practical value in promoting the high-quality development of the semiconductor sector.