Journal of Nondestructive Evaluation最新文献

Estimation of volume fraction percentage of the multiple phases flowing in pipes with limited access is a challenge in oil, gas, chemical processes, and petrochemical industries. In this research, the gamma backscattered spectra together with the machine learning model were used to predict precise volume fraction percentages in water-gasoil-air three-phase flows and solve the aforementioned challenge. The detection system includes a single energy ¹³⁷Cs source and a NaI(Tl) detector to measure the backscattered rays. The MCNPX code was used to simulate the setup and produce the required data for the artificial neural network. The volume fraction was calculated with mean relative error percentage 13.60% and the root mean square error 2.68, respectively. Then, the results were compared with the acquired results of transmitted gamma-ray spectra. The proposed design is a suitable, safe, and low-cost choice for industries.

The falling weight deflectometer (FWD) test is a prevalent non-destructive testing (NDT) technique in engineering that is essential for evaluating pavement conditions. In this work, the transfer function (TF) theory in frequency domain analysis was applied to address the technical challenges present in FWD research. A pavement system transfer function (PSTF) was proposed as a novel approach for evaluating pavement conditions. The spectral method with fixed-end boundary conditions (B-SEM) was employed to compute the theoretical deflection data for different pavement structures with bedrock during FWD testing. The fast Fourier transform (FFT) technique was used to convert the data into the frequency domain, enabling the construction and calculation of the PSTF. The validity of the PSTF theory was confirmed, and the pavement information contained in the PSTF spectrum was discussed. An analysis and summary are conducted on the impact of variations in pavement attributes on the spectrum. The results indicate that the proposed PSTF contains information regarding pavement system, including the structural layer modulus, structural layer thickness, and bedrock depth. The pavement conditions can be evaluated by directly analyzing the PSTF without considering external factors. The PSTF spectrum is most significantly influenced by bedrock depths between 200 and 500 cm. For every 50 cm variation in bedrock depth, the coefficient of increase and decrease (CIE) of peak frequency ranges from 8.1% to 23.1%. The PSTF spectrum is highly sensitive to variations in the subgrade modulus between 40 and 70 MPa. In this range, the CIE of peak amplitude is greater than 11% for every 10MPa variation in subgrade modulus. The impact of the modulus and thickness of both the surface layer and base layer on the spectrum is noteworthy and should not be disregarded. Spectral analysis is used to summarize the variation in pavement attributes within the PSTF spectrum, serving as a theoretical foundation for further investigations.

A new Forward Ray Tracing Instrument (FRTI) for simulating X-ray CT scanners is presented. The FRTI enables the modelling of various detector geometries to optimise instrument designs. The FRTI is demonstrated by comparing experimentally measured sphere centre-to-centre distances from two material measures with digital clones. The measured length deviations were smaller than the reconstructed grid spacing for both the experimental and simulated acquisitions. As expected the experimentally measured length deviations were larger than the simulated measurements. The results demonstrate the FRII’s capability of simulating an X-ray CT scanner and performing length measurements.

The surface of superalloy precision castings might exhibit defects after forming, posing a significant risk to their service life, necessitating inspection during post-process. Radiographic inspection, with its extensive research in automation, can achieve efficient and accurate detection of defects. However, it is limited in surface defects detection due to limited sensitivity to non-volumetric defects and high cost. In contrast, fluorescent penetrant inspection (FPI) is highly efficient for surface defect inspection due to its low cost, high sensitivity, and speed. However, manual examination introduces variability in the results, impacting the consistency and reliability of the inspection process. Automation is needed to ensure consistency and reliability of inspection. The implementation of an automated defect identification system based on FPI using convolutional neural networks (CNNs) was systematically investigated. Among the CNN models tested, MobileNetV2 exhibited exceptional performance, achieving a remarkable recall rate of 0.992 and an accuracy of 0.992. Additionally, the effect of class imbalance on model performance was carefully examined. Furthermore, the features extracted by the model were visualized using Grad-CAM to reveal the attention of the CNN model to the fluorescent display features of defects. This study underscores the strong capability of deep learning architectures in identifying defects of precision casting components, paving the way for the automation of the entire FPI process.

This paper presents a review of the possible methods for testing the fire performance properties of reused timber through non-destructive techniques, focusing on structural elements. Evaluating the fire performance of old wooden specimen is necessary to facilitate reuse, in the support of the transition to a circular economy. The use of non-destructive methods minimizes damages to the pieces during the evaluation process. Three angles are reviewed: (1) The properties of wood influencing fire performance, (2) the change of wood properties over time, and (3) the known non-destructive tests. Some properties of wood are known to influence the fire performance, e.g., the density. Of these, there is no evidence of irreversible changes due to the passage of time only. The many different non- and semi- destructive techniques that can be applied to wood seldom relate to these properties, but rather to mechanical properties or geometry. Additionally, accurate measurements are often difficult, while some are only done in laboratories. This review concludes that currently there is no known non-destructive method that permits to estimate the fire performance of a reused timber element compared to a new one. There is a gap of knowledge on the evolution of the fire properties of timber during the use phase of the building, and there are no established methods to test for these properties without destroying a significant portion of the element. Development of non-destructive test methodologies to assess fire properties of timber will expand the market for reused timber to include load carrying timber.

Concrete is a versatile construction material, which is often chemically attacked by various environmental agents. Concrete, being porous, allows movement of chemicals within its interior. The transport property of various chemicals depends on hydraulic diffusivity, which in turn depends on the degree of moisture saturation (DoS). Therefore, DoS is an important parameter and its estimation is highly significant with regards to material characterization. In this paper, cement concrete samples of size 75 mm × 75 mm × 300 mm are fabricated with water to cement ratio (w/c) of 0.45, 0.55 and 0.65. These samples are conditioned to various DoS in two methods described as drying and wetting cycles. A set-up for electrical measurements along the length of the sample is proposed, in which a pulse-based electrical input is imposed, which enables simultaneous acquisition of the material response at multiple frequencies, ranging between 100 and 500 kHz. Using a simple circuit model, the real and imaginary parts of impedivity are calculated along the length of the samples and the bulk conductivities and bulk relative permittivities at various DoS are estimated. The conductivity values are found to follow a regular pattern for various DoS and at different excitation frequencies, which facilitates the establishment of an empirical quantitative relationship between conductivity and the DoS of concrete. Further, on evaluation of permittivity it is found that the value of this parameter is much higher than that of its constituents which was seen in the literature.

The lack of real defect data samples has become a challenging problem for the effective application of deep learning networks in ultrasound target detection. This paper proposes a data augmented generative adversarial network (DCSGAN) aimed at overcoming the scarcity of welding ultrasonic defect data in training target detection networks. This network utilizes bilinear interpolation to expand the real data sample space, facilitating the extraction of high-dimensional defect spatial features through deeper networks. By obtaining a mixed dataset of generative data and real data, training and testing experiments are conducted on the object detection network. The experimental results demonstrate that the data augmentation method proposed in this paper effectively enhances the detection rate of ultrasonic welding defects in the target detection network, which has reference significance for similar application scenarios of ultrasonic defect detection.

The purpose of this paper is to conduct a thorough investigation of a stochastic eddy-current testing problem, when the geometric parameters of the system under study are characterized by uncertainty. Focusing on the case of subsurface defect detection, we devise reliable surrogates for the quantities of interest (QoI) based on the principles of the generalized polynomial chaos (PC) and using the orthogonal matching pursuit (OMP) solver to promote sparsity in the approximate models. In addition, a variance-based approach is implemented for the sequential construction of the necessary sample set, enabling more accurate estimation of the statistical metrics without imposing additional computational overhead. Apart from quantifying the inherent uncertainty, a sensitivity analysis is performed that assesses the impact of each geometric variable on the QoI, via the computation of Sobol indices. The efficiency of the OMP-PC algorithm is demonstrated in two variants of the subsurface-discontinuity problem, yielding at the same time useful conclusions regarding the properties of the stochastic outputs.

Titanium plate has a vital position in many industrial fields due to its outstanding characteristics, and the eddy current detection technology can quickly and non-destructively detect the defects of titanium plate, which is one of the crucial methods of titanium plate defect non-destructive testing. However, in the actual detection process, eddy current detection imaging is inevitably affected by noise interference to varying degrees, concerning the accuracy of defect classification recognition. Therefore, this study has proposed a titanium plate eddy current detection image classification method based on parallel sparse filtering and deep forest, which realizes the detection image's sparse feature extraction and defect classification. Firstly, the parallel sparse filtering network is constructed by adding another direction's feature extraction operation to the traditional sparse filtering. The parallel sparse filtering network extracts more comprehensive sparse features from the detection image. Secondly, a deep forest network is built, and the Bayesian optimization algorithm is used to optimize the network's hyperparameters. Finally, the deep forest network with optimized hyperparameters is used to classify and recognize the titanium plate defect eddy current detection images. The experimental results show that the proposed method has better feature representation and feature relevance learning ability, has higher classification accuracy under different levels of noise interference, with a classification accuracy increase of 3.09–40.65% compared to other conventional methods, and has better robustness and anti-noise ability.

Welding defects have a significant influence on welding quality and structural strength, and the rapid and accurate detection of welding defects is required. In order to achieve this goal, it is imperative to create corresponding high-quality datasets. However, capturing image information through a single sensor presents certain limitations. In this study, a magneto-optical imaging device and an infrared thermal imaging device were combined to collect images of resistance spot welding samples. The imaging principles of magneto-optical imaging device and the infrared thermal imaging device are discussed, and the possible factors affecting the imaging modes are analyzed. By synthesizing the 3D gray image, the gray histogram, and inherent image features, the imaging rules of magneto-optical image and the infrared image of resistance spot welding samples have been summarized. Under the guidance of these two image types and imaging modes, image enhancement technology has been utilized to optimize the quality of sample images. The Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Universal Image Quality Index (UIQI) indicators were used to evaluate the optimization quality of the enhanced images. Compared with Histogram Equalization (HE), the Gamma transform, Brightness Preserving Bi-Histogram Equalization (BPBHE), and the Digital Detail Enhancement (DDE) method, the scores of the enhanced infrared images showed improvement across all indicators. The magneto-optical image yielded the best results in the PSNR index, while the other two indices showed only moderate performance. The image dataset, enhanced with appropriate image enhancement techniques, can be utilized for further research into magneto-optical and infrared image information fusion and welding defect identification.