Journal of Nondestructive Evaluation最新文献

Time reverse modeling (TRM) is successfully applied to acoustic signals from a circular microphone array, for mapping of sudden cracking sound events. Numerical feasibility using synthetic acoustic sources followed by an experimental study with steel pendulum impacts on a steel plate is carried out. The mapping results from the numerical and experimental data are compared and verified using a delay-and-sum beamforming technique. Based on the feasibility and experimental study, a mapping error is estimated. In the main experimental study, cracking sound events obtained during a tensile test on a textile-reinforced concrete specimen are mapped with the TRM. The enhanced capability of the TRM to map simultaneously occurring cracking sound events along crack paths is demonstrated.

Inspection of defects in pipelines can be materialized by measuring ultrasonic guided waves the properties of which are conventionally analyzed with three-dimensional finite-element methods (FEM). They require complicated geometric discretization and memory consumption in a single analysis, thus are clumsy and limited to be used for field fast analysis. This work developed a systematic analytical approach to perform rapid assessment of mode-to-mode reflection for guided waves in a pipe owing to notches and used low-cost microprocessors for calculation. The mechanism of wave reflection was interpreted with the reciprocity theorem and a novel dynamic rigid-ring approximation. The theory successfully estimated the coefficient dependence of notch depths with an accuracy comparable to that obtained from a FEM, with the maximum error being less than 0.044. The developed algorithm was further implemented on an embedded system for computational complexity estimation. It shows the complete analytical theory sufficiently reduces computational memory and time cost by orders of magnitude while retaining good accuracy in determining mode-to-mode guided reflection by notches, which is a useful tool for practical pipeline applications.

The inspection of thick-section sandwich structures with skins around core materials such as honeycomb, balsa, and foam relies on low-frequency vibration techniques to identify defects through changes in amplitude or phase response. However, current industrial methods are often limited to detecting specific types of defects, potentially overlooking others. Moreover, these methods do not gather detailed information about the defect type or depth, as they only analyse a small portion of the available data instead of the full relevant response spectrum. This paper explores the scientific basis of using low-frequency vibration in the pitch-catch variant for defect detection in homogeneous solids, through analysis of the full relevant frequency spectrum (5–50 kHz). Defects in structures lead to reduced local stiffness and mass in the affected area, causing resonance in the layer above, resulting in amplified vibrations known as local defect resonance (LDR). In this work, an aluminium plate with a 40 mm diameter circular flat-bottomed hole (FBH) at a depth of 1 mm (representing a skin defect) is excited with a chirp signal of 5–50 kHz, and the response is monitored 17 mm away from the excitation point. Finite-element analysis (FEA) is used for the numerical model, addressing challenges in creating an accurate model. The process to optimise the numerical model and the reduce model-experiment error is outlined, including challenges such as the lack of knowledge of material damping. The study emphasizes the importance of modelling the probe’s stiffness and damping effects for achieving agreement between the model and experiment. After incorporating these effects, the maximum LDR frequency error decreased from approximately 3 kHz to less than 1 kHz. In addition, this study presents a method with the potential for defect classification through comparison to modelled responses. The minimum difference error was used to quantify the resonance frequencies’ error between the model and the experiment. Since the resonant frequencies are a function of the defect’s shape, size, and depth, a relatively low root mean squared (RMS) error across the resonance frequency error spectrum indicates the defect’s characteristics. Finally, defect detection and sizing using the pitch-catch probe are explored with a wide-band excitation signal and a line scan through the mid-plane of the defect. A method for defect sizing using a pitch-catch probe is presented and experimentally validated. Accurate defect sizing is achieved with the pitch-catch probe when the defect width is at least (ge ) twice the 17 mm pin-spacing of the probe.

Repeated inspections have been reported to improve the reliability of nondestructive inspection and can be evaluated by multiplying the likelihood function. However, repeated inspections conducted by a single inspector may not be independent, because the subsequent inspections may be influenced by previous inspection results. The probability of detection (POD) quantifies the sensitivity and reliability of an inspection system. In this study, eddy-current inspection data were used to assess the effect of repeated inspections on POD improvement. Specifically, repeated measures correlation (RMC) analysis was performed, which did not violate the assumption of independence to analyze intra-individual association, considering the nonindependence of repeated measures. Nonindependent repeated inspections performed using a combination of two datasets reduced the uncertainty in POD. Moreover, RMC yielded further improvements in POD and reduced the uncertainty.

X-ray cone-beam computed tomography (CBCT) is a powerful tool for nondestructive testing and evaluation, yet the CT image quality can be compromised by artifact due to X-ray scattering within dense materials such as metals. This problem leads to the need for hardware- and software-based scatter artifact correction to enhance the image quality. Recently, deep learning techniques have merged as a promising approach to obtain scatter-free images efficiently. However, these deep learning techniques rely heavily on training data, often gathered through simulation. Simulated CT images, unfortunately, do not accurately reproduce the real properties of objects, and physically accurate X-ray simulation still requires significant computation time, hindering the collection of a large number of CT images. To address these problems, we propose a deep learning framework for scatter artifact correction using projections obtained solely by real CT scanning. To this end, we utilize a beam-hole array (BHA) to block the X-rays deviating from the primary beam path, thereby capturing scatter-free X-ray intensity at certain detector pixels. As the BHA shadows a large portion of detector pixels, we incorporate several regularization losses to enhance the training process. Furthermore, we introduce radiographic data augmentation to mitigate the need for long scanning time, which is a concern as CT devices equipped with BHA require two series of CT scans. Experimental validation showed that the proposed framework outperforms a baseline method that learns simulated projections where the entire image is visible and does not contain scattering artifacts.

Machine learning in non-destructive testing (NDT) offers significant potential for efficient daily data analysis and uncovering previously unknown relationships in persistent problems. However, its successful application heavily depends on the availability of a diverse and well-labeled training dataset, which is often lacking, raising questions about the transferability of trained algorithms to new datasets. To examine this issue closely, the authors applied classifiers trained with laboratory Ground Penetrating Radar (GPR) data to categorize on-site moisture damage in layered building floors. The investigations were conducted at five different locations in Germany. For reference, cores were taken at each measurement point and labeled as (i) dry, (ii) with insulation damage, or (iii) with screed damage. Compared to the accuracies of 84 % to 90 % within the laboratory training data (504 B-Scans), the classifiers achieved a lower overall accuracy of 53 % for on-site data (72 B-Scans). This discrepancy is mainly attributable to a significantly higher dynamic of all signal features extracted from on-site measurements compared to laboratory training data. Nevertheless, this study highlights the promising sensitivity of GPR for identifying individual damage cases. In particular the results showing insulation damage, which cannot be detected by any other non-destructive method, revealed characteristic patterns. The accurate interpretation of such results still depends on trained personnel, whereby fully automated approaches would require a larger and diverse on-site data set. Until then, the findings of this work contribute to a more reliable analysis of moisture damage in building floors using GPR and offer practical insights into applying machine learning to non-destructive testing for civil engineering (NDT-CE).

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.