Journal of Nondestructive Evaluation最新文献

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.

Functionally gradient material (FGM) in service often experience temperature variations that can affect the propagation characteristics of guided waves. This investigation aims to study the propagation of thermoelastic guided waves in the FGM plate. A computational method for the state vector and Legendre polynomials hybrid approach, which is proposed based on the Green–Nagdhi theory of thermoelasticity. The heat conduction equation is introduced into the governing equations, and optimized using univariate nonlinear regression for arbitrary gradient distributions of the material components. To study their dispersion characteristics, a non-hierarchical calculation for the dispersion curves of FGM plates versus temperature is realized. In addition, a frequency domain simulation model is developed and compared with theoretical data to evaluate the accuracy and feasibility of the proposed theory. Then, the influence of Legendre orthogonal polynomial cut-off order on dispersion curve convergence is investigated. Subsequently, the shift of the gradient index and temperature variation on the fundamental mode in dispersion curve is analyzed. The results indicate that changes in both gradient index and temperature lead to a systematic shift in the phase velocity of fundamental modes in the low frequency range. Meanwhile, anti-symmetric modes exhibit higher sensitivity. On this basis, the study can provide theoretical support for the acoustic non-destructive characterization of FGM plates versus temperature.

This work develops a polarization-dependent analytical model using terahertz time-domain spectroscopy (THz-TDS) for computing strain in materials. The model establishes a correlation between volumetric strain and the change in time of arrival for a THz pulse by using the dielectrostrictive properties, variations in doping particle density, and changes in the thickness of the sample resulting from Poisson’s effects. The analytical model is validated through strain mapping of polydimethylsiloxane (PDMS) doped with passive highly dielectrostrictive strontium titanate (STO). Two experiments, using an open-hole tensile and a circular edge-notch specimen are conducted to show the efficacy of the proposed. The stress relaxation behavior of the composite is measured and accounted for to prevent changes in strain during the measurement window. The THz strain mapping results are compared with the finite element model (FEM) and surface strain measurements using the digital image correlation (DIC) method. The experimental findings exhibit sensitivity to material features such as particle clumping and edge effects. The THz strain map shows a strong agreement with FEM and DIC results, thus demonstrating the applicability of this technique for surface and sub-surface strain mapping in polymeric composites.

The detection of steel surface defects is of great significance to steel production. In order to better meet the requirements of accuracy, real-time, and lightweight model, this paper proposes a highly efficient and lightweight steel surface defect detection method based on YOLOv5n. Firstly, ODMobileNetV2 composed of MobileNetV2 and ODConv is used as the backbone to improve the defect feature extraction capability. Secondly, GSConv is utilized in the neck to achieve deep information fusion through channel concatenation and shuffling, enhancing the ability of feature fusion. Finally, this paper proposes a spatial-channel reconstruction block (SCRB) designed to suppress redundant features and improve the representation ability of defect features through feature separation and reconstruction. Experimental results show that this method achieves 84.1% mAP and 109 FPS on the NEU-DET dataset, and 72.9% mAP and 110.1 FPS on the GC10-DET dataset, enabling accurate and efficient detection. Furthermore, the number of parameters is only 5.04M, which has a significant lightweight advantage.

Carbon fiber reinforced polymer (CFRP) is an important composite material widely used in aerospace and other industries. However, long-term service in harsh environments can lead to various defects such as debonding, delamination, water, cracks, etc. Therefore, it becomes imperative to conduct non-destructive testing (NDT) on CFRP to ensure its structural integrity and safety. Infrared thermography was employed for defect classification in CFRP laminate and CFRP honeycomb sandwich composites (HSC) by applied a convolutional neural networks (CNN). The proposed automatic defect classification method based on CNN is one of the goals of NDE 4.0 to apply advanced technologies (such as deep learning and AI) to improve NDT efficiency and accuracy. The infrared detection dataset consisted of five classes: debonding, water, delamination, crack, and health. To effectively expand the dataset, offline data augmentation technique were employed. A deep learning technique of Dense convolutional neural network (DCNN) were proposed to defect classification. AlexNet, VGG-16, ResNet-50 and DenseNet-121 based on transfer learning fine-tuning model was applied to classify debonding, water, delamination, crack and health. The classification results were analyzed by using a confusion matrix. The results shown that the accuracy of AlexNet, VGG-16, ResNet-50 and DenseNet-121 were 92.34%, 82.86%, 88.30%, 98.48%, respectively. DenseNet-121 demonstrates good performance in defect detection and recognition with an accuracy of 98.48%, and DenseNet-121 has high application potential in accurately classify and recognize defects in deep learning technique.