Journal of Nondestructive Evaluation最新文献

Images of titanium alloy welds detected by time-of-flight diffraction (TOFD) have problems, including large noise signals and many interference streaks around the defects, all of which seriously limit the accuracy and effectiveness of defect recognition. Existing image denoising methods lack the knowledge of the noise characteristics of TOFD images of titanium alloy weld and the preprocessing experience of technicians in the field. In addition, it is difficult to select the parameters of the preprocessing methods, and they are easily influenced by the level of technical personnel, resulting in low efficiency and poor consistency in preprocessing. To address these problems, we proposed a denoising method based on the combination of wavelet band features and deep-learning theory for TOFD images of titanium alloy weld. First, based on the wavelet preprocessing method and the experience of nondestructive testing (NDT) technicians, we constructed an image pair dataset consisting of the original TOFD images of titanium alloy weld and the desired target images to realize the accumulation of engineers’ preprocessing knowledge. Second, we constructed a multiband wavelet feature fusion U-net image denoising model (WU-net) and designed a loss function under three constraints of image consistency, image texture information consistency, and structural similarity. This model was able to learn to achieve end-to-end adaptive denoising for TOFD images of titanium alloy weld. Third, we illustrated and validated the effectiveness of TOFD image preprocessing for titanium alloy weld. The results showed that the proposed method effectively eliminated TOFD image noise and improved the accuracy of defect recognition.

Flat objects like electronic boards are challenging samples for high-resolution X-ray computed tomography scanning because their largest dimension significantly limits the magnification using circular trajectory scans. One way to improve spatial resolution for such samples is to utilize variable zoom trajectory. During variable zoom trajectory scanning, the source-to-object distance changes during the 360° rotation to maximize the magnification in the projections. Here, we propose an automatic variable zoom trajectory generation algorithm for arbitrary object and region of interest (ROI). We analyze how such a trajectory can enhance resolution in different cases and how isotropic is the resolution in the reconstructed volume. We demonstrate that the resolution can be improved without destroying the sample. However, the improvement is manifested mainly in directions in which we achieved the highest magnification in the projection.

The wire to terminal joints are prepared using ultrasonic welding and find extensive application in various fields, such as new energy vehicles and aerospace. Traditionally, tensile strength tests have been employed for welding quality inspection. However, this study proposes an automatic nondestructive evaluation scheme to overcome the inefficiency and destructiveness associated with tensile testing. To achieve this, a 5 MHz/32-element array ultrasound probe is utilized for ultrasound detection and signal acquisition from two groups of joints categorized as OK (good quality) and NG (poor quality) based on their welding quality. Signal processing techniques including short-time Fourier transform, wavelet transform, and Gramian angular field are applied to convert one-dimensional time series into two-dimensional signal feature maps. Convolutional neural networks such as VGGNet, ResNet, DenseNet, and MobileNet are utilized for the classification of two-dimensional signal feature maps. The comprehensive evaluation of different feature maps and combinations of neural networks is conducted from various perspectives including network complexity, recognition accuracy, memory consumption, and inference time. The study findings indicate that wavelet transform feature maps achieve the highest accuracy across diverse neural networks, reaching up to 95% accuracy in VGGnet13 despite higher associated costs. In MobileNet-Small and ShuffleNet-V2 networks, the accuracy stands at approximately 85%, accompanied by faster inference times and lower costs. Considering all factors holistically, the combination of wavelet transforms feature maps with MobileNet and ShuffleNet demonstrates superior cost-effectiveness and suitability for ultimate deployment and application on mobile devices facilitating automated non-destructive assessment of wire to terminal joints quality.

As the availability and importance of high energy X-ray sources grows, accurate source characterizations provide critical information for field flatness corrections, beam hardening corrections, detector response corrections, and radiation shielding assessments. This study uses MCNP6.2 to create accurate high definition angular and energy dependent X-ray source definitions for the most common high energy industrial X-ray sources at 450 kVp, 3 MVp, 6 MVp, 9 MVp, and 15 MVp.

Experimental results are presented for the contact acoustic nonlinearity of interfaces between two solid surfaces in dry contact which experience a moderate level of plastic deformation. The current research aims at investigating the effects of the elastoplastic hysteresis, surface roughness, and possible adhesive force on the acoustic nonlinearity. The ultrasonic results are compared with results from the elastoplastic contact model of Kim and Lee (2007) and the clapping model of Blanloeuil et al. (2020), which reveals that the nonlinearities are dominated by clapping of lightly contacting cracks at the interface at low pressures and by the elastoplastic nonlinear spring at high pressures. On top of this major trends, there are consistent minor trends which are attributed to the effects of adhesive force. There is a critical pressure level at which the adhesive clapping of cracks starts being more pronounced. As predicted by the model, the nonlinearity increases with the surface roughness and thus is always lower during unloading in the high pressure regime. The effects of the adhesion are investigated by measuring the nonlinearity at two different relative humidity levels. Some systematic, physically reasonable trends in the experimental results illustrates possible effects of the adhesive force on the acoustic nonlinearity.

Thermochemical storage using salt hydrates presents a promising energy storage method. Ensuring the long-term effectiveness of the system is critical, demanding both chemical and mechanical stability of material for repetitive cycling. Challenges arise from agglomeration and volume variations during discharging and charging, impacting the cyclability of thermochemical materials (TCM). For practical usage, the material is often used in a packed bed containing millimetre-sized grains. A micro-level analysis of changes in a packed bed system, along with a deeper understanding involving quantifying bed characteristics, is crucial. In this study, micro X-ray computed tomography (XCT) is used to compare changes in the packed bed before and after cycling the material. Findings indicate a significant decrease in pore size distribution in the bed after 10 cycles and a decrease in porosity from 41.34 to 19.91% accompanied by an increase in grain size, reducing void space. A comparison of effective thermal conductivity between the uncycled and cycled reactor indicates an increase after cycling. Additionally, the effective thermal conductivity is lower in the axial direction compared to the radial. XCT data from uncycled and cycled experiments are further used to observe percolation paths inside the bed. Furthermore, at a system scale fluid flow profile comparison is presented for uncycled and cycled packed beds. It has been observed that the permeability decreased and the pressure drop increased from 0.31 to 4.88 Pa after cycling.

The performance of machine learning algorithms is conditioned by the availability of training datasets, which is especially true for the field of nondestructive evaluation. Here we propose one reconfigurable specimen instead of numerous reference specimens with known, unchangeable defect properties, which are usually complicated to fabricate. It consist of a shape memory polymer foil with temperature-dependent Young’s modulus and ultrasound attenuation. This open a possibility to generate a reconfigurable defect by projecting a heating laser in the form of a short line on the specimen surface. Ultrasound is generated by a laser pulse at one fixed position and detected by a laser vibrometer at another fixed position for 64 different defect positions and 3 different configurations of the specimen. The obtained diversified datasets are used to optimize the neural network architecture for the interpretation of ultrasound signals. We study the performance of the model in cases of reduced and dissimilar training datasets. In our first study, we classify the specimen configurations with the defect position being the disturbing parameter. The model shows high performance on a dataset of signals obtained at all the defect positions, even if trained on a completely different dataset containing signals obtained at only few defect positions. In our second study, we perform precise defect localization. The model becomes robust to the changes in the specimen configuration when a reduced dataset, containing signals obtained at two different specimen configurations, is used for the training process. This work highlights the potential of the demonstrated machine learning algorithm for industrial quality control. High-volume products (simulated by a reconfigurable specimen in our work) can be rapidly tested on the production line using this single-point and contact-free laser ultrasonic method.

Thermoplastic composite pipe is gaining popularity in the oil and gas and renewable energy industries as an alternative to traditional metal pipe mainly due to its capability of being spooled onto a reel and exceptional corrosion resistance properties. Despite its corrosion-proof nature, this material remains susceptible to various defects, such as delamination, fiber breakage, matrix degradation and deformation. This study employed the phased array ultrasonic testing technique with the implementation of the classical time-corrected gain method to compensate for the significant spatial signal attenuation beyond the first interface layer in the thick multi-layered thermoplastic composite pipe. Initially, the ultrasonic signals from the interface layers and back wall were detected with good signal-to-noise ratios. Subsequently, flat-bottom holes of varying depths, simulating one-sided delamination, were bored and the proposed method effectively identified ultrasonic signals from these holes, clearly distinguishing them from the background noise and interface layer signals. Finally, a defect deliberately fabricated within the composite laminate layers during the pipe manufacturing process was successfully identified. Subsequently, this fabricated defect was visualized in a three-dimensional representation using the X-ray computed tomography for a qualitative and quantitative comparison with the proposed ultrasonic method, showing a high level of agreement.

X-ray imaging can be efficiently used for high-throughput in-line inspection of industrial products. However, designing a system that satisfies industrial requirements and achieves high accuracy is a challenging problem. The effect of many system settings is application-specific and difficult to predict in advance. Consequently, the system is often configured using empirical rules and visual observations. The performance of the resulting system is characterized by extensive experimental testing. We propose to use computational methods to substitute real measurements with generated images corresponding to the same experimental settings. With this approach, it is possible to observe the influence of experimental settings on a large amount of data and to make a prediction of the system performance faster than with conventional methods. We argue that a high accuracy of the image generator may be unnecessary for an accurate performance prediction. We propose a quantitative methodology to characterize the quality of the generation model using Probability of Detection curves. The proposed approach can be adapted to various applications and we demonstrate it on the poultry inspection problem. We show how a calibrated image generation model can be used to quantitatively evaluate the effect of the X-ray exposure time on the performance of the inspection system.