Journal of Nondestructive Evaluation最新文献

For the construction and maintenance of bridges, dams, pipelines, etc., the damage detection method of underwater concrete components is increasingly important to ensure the safety and reliability of civil infrastructures. Current visual and sonar techniques are not robust and sensitive for inspecting underwater concrete components, especially in low illumination and muddy water. Therefore, this paper proposes an ultrasonic method to detect underwater concrete voids besides traditional methods. First, theoretical derivation reveals the wave modes propagating at the water-concrete interface as well as the wave velocities. Second, finite element simulation is used to investigate the wave transmission through a void. Rayleigh waves are more reliable and sensitive to characterize underwater voids than bulk and Scholte waves. Third, a damage index is formulated considering the energy attenuation of the transmission of Rayleigh waves. The interaction of abnormal wave paths indicates the void in a damage image. Finally, a distributed transducer network is developed in experiments to image the concrete slabs with single and double voids underwater. The proposed method can enrich the nondestructive testing of underwater structures and ensure the safety of civil infrastructures.

A systematic feature-engineering approach to generate informative 2D representations of 3D data is introduced. In this method, the sequences of voxels along one axis of the 3D image are treated as spatial variation sequences. These sequences are projected into a 783-dimensional feature space using algorithms from statistics, signal processing, complexity theory as well as time-series forecasting and financial time-series analysis. The resulting two-dimensional image has 783 layers from which the most relevant three layers are chosen using a combination of univariate and multivariate feature selection. This process effectively converts the volumetric data into a two-dimensional three-layer image which can then be used as input to established object detection models. The validation of the method is conducted on an object detection application, involving the identification of biomatter threats in 3D X-ray scans of international travellers’ baggage. The 3D scans were recorded at the Airport in Auckland, New Zealand, and comprised 1525 biomatter threats distributed over 690 different bags. Various object detection models from the YOLO series are tested on this dataset. The YOLOv5l model achieved the highest mAP@0.5 of 0.878 on the validation dataset. Our results demonstrate that the methodologies of time-series classification and pattern recognition can be combined to implement efficient pattern recognition on 3D data sets with small sample sizes.

Coated glass has been used for many applications, such as electromagnetic shielding/absorbing, green architecture, antennas and solar cells. Sheet resistance is one of the important parameters of coated glass. This paper, based on the end-wall replacement cavity technique, used four resonators in the TE₀₁₁ mode to observe the relationship between the measurement performance of sheet resistance and the design of the resonator. An air gap was introduced to achieve true non-contact non-destructive testing, and the effects of the air gap between the cavity sidewall and the sample (0–15.5 mm), the sample’s tilt (0–10 degrees), and the substrate thickness were studied. The results show: (1) The air-gapped microwave cylindrical resonator is suitable for the R_□ evaluation of coated glass, and its measurement range and accuracy are closely related to the design of the resonator. When the air gap is between 0 and 15.5 mm, the sapphire filled resonator can extend the measurable sheet resistance to approximately 1000 Ω/sq; (2) For samples with R_□ <  ~ 49.33 Ω/sq, the air filled resonator has higher precision than the sapphire filled resonator; (3) The sample tilt has a small effect on the resonant frequency but a significant effect on the Q-factor; Potential advantages of the air-gapped method include: (1) it realize real non-contact test, which may protect the sample from damage/contamination, or be convenient for multi-physics test such as high temperature, photo excitation; (2) the air gap size can be used as an additional parameter for measurement performance optimization.

In this paper, the finite element boundary integral (FEBI) method, for the first time, is applied to solve the 3-D arbitrary shaped eddy current nondestructive testing (ECNDT) problems. FEBI alleviates the extra computational costs of truncation region in finite element method (FEM) and the difficulty in derivation of the Green’s function in boundary element method (BEM). The boundary integral equation (BIE) selected is the combined field integral equation (CFIE) in the TENH (tangential testing of electric field and normal testing of magnetic field) form, which shows better convergence compared with other forms. In BEM, the equivalent electric and magnetic surface currents are expanded by Rao-Wilton-Glisson (RWG) vector basis functions. While in FEM, the electric field and electric surface current are expanded by tetrahedron-based edge elements and RWG vector basis functions, respectively. The discretized matrix achieved by BEM and FEM is coupled by the field continuity conditions. For ECNDT problems, inhomogeneous meshes are required due to the small size of cracks or slots than the whole solution domain. It makes the convergence for solving the coupled matrix formed by the sparse matrix generated by FEM and the dense matrix produced by BEM worse, thus, precondition is required for FEBI solution in iterative method, which complicates the solving procedure. To alleviate the cumbersome solving process, the inward-looking formulation method is studied to work as precondition by solving the inverse of FEM matrix directly, and then the coupled discretized matrix is solved iteratively. By evaluating several ECNDT benchmark cases involving the cylindrical flaws and surface slots, the predicted impedance changes achieved by FEBI method are compared with those by semi-analytical method, FEM, and experiment which demonstrates that the proposed FEBI method based forward solver can simulate the ECNDT problems both accurately and efficiently.

In the present work, the feasibility of using Magnetic Barkhausen Noise (MBN) to identify variations in the microstructure of a commercial rail steel has been studied. To achieve this purpose a brand new rail, reference R260 was sectioned to obtain two samples that were subjected to quenching and normalizing heat treatments. The hardness and microstructure of the specimens were evaluated by conventional destructive and nondestructive evaluation. The MBN technique's sensibility to characterize different microstructures was studied, and the results were contrasted with hardness and residual stress measurements. The envelope of MBN signals proved to be useful to detect the presence of martensite at the surface of rail sections, mainly because of the high density of dislocations that is typical of this micro constituent in comparison with pearlitic or ferritic microstructures. The MBN signals showed strong correlation with the changes in hardness and microstructure of the samples, being the normalized sample the one with the highest amplitude signal of MBN. In contrast, the quenched sample with martensite microstructure had a lower MBN intensity. The results of this work show the potential of MBN for nondestructive evaluation (NDE) of rails in the field, which could improve the capacity of early detection of defects in railway systems.

Stainless steel pipes are one of the indispensable raw materials in industrial production, and they are widely used in fields such as aerospace, energy, and chemical industries. The service environment involves harsh conditions such as high temperature, high pressure, corrosion, and fatigue, placing high demands on structural safety. Due to the complex surface defects of steel pipes and the similarity of defect types, the assessment of the surface quality of steel pipes still relies on manual visual inspection. Based on these issues, this paper proposes a semi-bilateral efficient self-attention network (SBSANet) for quantifying the severity of internal surface defects in steel pipes. Firstly, a semi-bilateral and jump connection structure was designed to effectively compensate for information loss during the encoding process. Then, a multi-head attention encoding (MHAE) module was proposed to enable long-distance pixel interactions. Finally, a multi-scale residual context extraction (MSRCE) module was designed at the end of the encoder, effectively extracting multi-scale contextual information and enhancing the network’s segmentation capability. Experimental results show that compared to other networks, the proposed method exhibits superior performance. SBSANet achieves a mean intersection over union (mIoU) of 78.48% on a self-made dataset and a running speed of 91.62 FPS, with mIoU of 81.28% and a running speed of 92.70 FPS on the NEU-Seg strip steel dataset.

Electrical impedance tomography (EIT) has attracted attention for its potential application to nondestructive evaluation of materials due to a lack of ionizing radiation, low cost, and the potential for integration with the material system. However, EIT is an ill-posed inverse problem and requires regularization to achieve a physically meaningful solution. Many materials-based practitioners of EIT make use of relatively simple regularization methods. This is important because the choice of the regularizer significantly impacts final image quality. Choosing poorly can misrepresent the damage state of the material to the inspector, ultimately undermining the potential of this modality. This manuscript thus serves as a primer for researchers using EIT for damage detection and localization by applying several common regularization techniques and one new technique to six experimental data sets. Notably, experimental data is taken from components of representative complexity and/or subject to non-trivial loading or realistic damage in an effort to transition the research away from the overly-simplified shapes and conditions that permeate the current state-of-the-art. It is shown that there is no one-size-fits-all regularization method; materials-based EIT practitioners must guide selection of the appropriate regularization method with knowledge of what they intend to image.

Metering of various parameters is a very imperative task in the gas and oil industries. Therefore, many studies can be found that focus on measuring the volume fractions of multiphase flows without any interruption or separation in the process. One of the key factors highly impacting on the accuracy of the measurements is the scale layer formed in the pipelines. When there is a scale in the transmission lines, it significantly affects measurement accuracy, sensor performance, and fluid dynamics. In this paper, a new approach, including two distinct sensors, photon-attenuation-based and capacitance-based, in conjunction with an Artificial Neural Network (ANN), is presented to measure scale thickness in multiphase oil-gas-water homogeneous fluids. The intelligent model has 2 inputs. While the first input is generated by simulating a capacitive sensor, the concave type, in the COMSOL Multiphysics software, the second input comes from counting rays traveling from a Cobalt-60 source to a detector. This counting is calculated using the Beer-Lambert equations. By considering an interval equal to 10% of material in each ratio, in total, 726 data are accumulated resulting in collecting enough data to measure the scale thickness with a high level of precision. The investigated range for the thickness of the metering scale inside a pipe with a gas-oil-water homogeneous fluid is from 0 cm to 1 cm. Moreover, to reach the lowest amount of Mean Absolute Error (MAE), a number of networks with various hyperparameters were run in MATLAB software, and the best model had MAE equal to 0.46 illustrating the accuracy of the proposed metering system in predicting scale thickness.

In recent years, significant discussions and efforts have been made regarding the deformations’ detection and paintings’ restoration. There are various non-destructive testing methods, such as spectroscopy or photogrammetry, but in this manuscript, a new industrial geodetic methodology is presented, in detail. This method uses both a laser tracker and a coordinate measuring arm in order to create detailed three-dimensional models of the paintings’ surface in real time with an accuracy of ± 0.050 mm. Additionally, using surfaces’ models from different period of time, deformations of ± 0.10 mm are calculated using the Cloud-to-Cloud Distance (C2C) or the Multiscale Model to Model Cloud Comparison (M3C2) algorithm. By testing this new methodology in two different paintings, important results, concerning the paintings’ restoration process, are obtained. This methodology can help restorers, before, during or after restoration, to recognize additions of colors and materials or deformations due to humidity or other causes.

X-ray computed tomography with laboratory imaging chains often struggles with high-speed processes, as recording a single tomographic dataset quickly enough is often a challenging task. This paper presents a method for extracting the eigenmode of a harmonically excited oscillating object based on a probabilistic analysis of its tomographic reconstruction. In the standard reconstruction of an oscillating object, where the recording of tomography data is realised over a relatively long period of time, the highest probability of the object occurrence is in its amplitudes. Based on this fact, it is possible to identify the eigenshape of the oscillating object by searching for the envelope of its motion. The identified modal shapes show good agreement with the laser Doppler vibrometer measurements. Consequently, the effectiveness of the method was demonstrated for objects that are unsuitable for traditional laser vibrometry due to their shape or surface limitations.