Ultrasonic Flaw Research Articles

Raman Deconvolution, Photoluminescence, Thermoluminescence, Radiation Shielding and Dielectric Investigations of Cu2+: Li2Al2SiO6 Glasses for Opto-electronic use

The purpose of this research is to work toward the development of thermoluminescent (TL) dielectric glass, which will combine the benefits of robust radiation protection with mechanical durability. Copper oxide (CuO) and lithium oxide are the two components that are being investigated in relation to aluminium silicate glasses. This work investigates the genuine potential of CuO-doped glasses to defend against radiation, to have TL, and to have dielectric capabilities. CuO-doped glasses are well acknowledged for their electrical properties. Raman spectroscopy, differential thermal analysis (DTA), and X-ray diffraction (XRD) were employed in order to explore the glassy structure of these glasses. Additionally, energy-dispersive X-beam spectroscopy was utilized for the purpose of analysing the substances. According to the findings of the DTA, the amount of CuO present has an effect on the warm security. In conjunction with its deconvolution, Raman spectroscopy was able to identify several vibrational modes, with variation in wave numbers being associated with the fixation of CuO. Using an ultrasonic defect finder, the mechanical characteristics of the glasses were evaluated, and the assessment revealed that the covalent structure of the glasses is responsible for their flexible capabilities. The purpose of this research is to develop thermoluminescent (TL) dielectric glass that combines the benefits of radiation protection with the ability to withstand mechanical conditions. Aluminium silicate glasses that include copper oxide (CuO) and lithium oxide are the primary focus of the investigation. TL, dielectric characteristics, and the real ability of CuO-doped glasses to guard against radiation are the subjects of this study. CuO-doped glasses are well regarded for their dielectric qualities. In order to explore the shapeless construction of these glasses, Raman spectroscopy, differential thermal analysis (DTA), and X-ray diffraction (XRD) were used. Additionally, energy-dispersive X-beam spectroscopy was utilized for the evaluation of compounds. According to the findings of the DTA, the amount of CuO present has an effect on the warm security. Different vibrational modes were identified using FT-IR and Raman spectroscopy, in addition to its deconvolution, with variations in wave numbers being associated with CuO fixation. We used an ultrasonic defect finder to evaluate the mechanical characteristics of the glasses, and our findings revealed that the covalently reinforced inner structure of the glasses is responsible for their flexible capabilities. In addition, microhardness studies demonstrated that the effect of CuO concentration on this attribute is significant. In addition, the concentration evaluated the radiation protection capabilities by determining certain limits, such as Macintosh, HVL, MFP, and RPE, in comparison to the CuO ratio. At radiation doses of 5, 10, and 25 kGy, TL characteristics were tested by introducing them to gamma illumination. The results demonstrated that the TL force increases with radiation dose and is mostly influenced by CuO. Using optical absorption, bandgap assessments were carried out. The emission cross section range may be determined using photoluminescence investigations. "CIE" The Chromacity technique will be used in order to expose the luminescence colour spectrum.

Optimization of Weak Ultrasonic Defect Signal Detection of Carbon Fiber Composites Based on Double-Sided Pulse Reflection Scanning

Optimization of Weak Ultrasonic Defect Signal Detection of Carbon Fiber Composites Based on Double-Sided Pulse Reflection Scanning

Modeling stochastic elastic wave diffraction by the tips of randomly rough defects

Elastic wave scattering from a randomly rough surface of a finite length includes surface reflections and diffractions from the tips. Previous research has focused upon reflection waves with applications in ultrasonic defect detection, seismic wave exploration and phonon boundary transport. However, waves diffracted from the tips/edges have been largely neglected so far for rough defects, despite their importance in engineering applications including ultrasonic defect sizing and imaging for assessment of structural integrity. Currently understanding the statistical nature of elastic wave tip diffraction and the role of roughness is limited due to the lack of theoretical studies. In this article, we develop a statistical geometrical tip diffraction (SGTD) theory to rapidly predict the stochastic properties of tip diffraction amplitude as a function of surface roughness and incident angle. By applying a small slope perturbation to the model, a simplified analytical solution of tip diffraction is obtained. It is found that for defects with small to medium roughness, the diffraction amplitude explicitly follows a Gamma distribution, and its mean and the standard deviation are both proportional to the square of the rms slope. High-fidelity Monte Carlo finite element simulations are then run to evaluate the accuracy of the theoretical model. The range of validity of the analytical solution with respect to the level of roughness and the incident angle is obtained. The SGTD method is accurate when the correlation length is approximately equivalent or larger than one wavelength, for a wide range of angles. It is also applicable for a correlation length as short as half wavelength, but only for small rms values and when the beam angle is larger than 45∘. In addition, at large angles, the tip diffraction is almost not affected by roughness, being very similar to that from a smooth crack. This is explained by the significant dependence on the beam angle factor explicitly shown in the theoretical solution.

Systematic Evaluation of Ultrasonic In-Line Inspection Techniques for Oil and Gas Pipeline Defects Based on Bibliometric Analysis.

The global reliance on oil and gas pipelines for energy transportation is increasing. As the pioneering review in the field of ultrasonic defect detection for oil and gas pipelines based on bibliometric methods, this study employs visual analysis to identify the most influential countries, academic institutions, and journals in this domain. Through cluster analysis, it determines the primary trends, research hotspots, and future directions in this critical field. Starting from the current global industrial ultrasonic in-line inspection (ILI) detection level, this paper provides a flowchart for selecting detection methods and a table for defect comparison, detailing the comparative performance limits of different detection devices. It offers a comprehensive perspective on the latest ultrasonic pipeline detection technology from laboratory experiments to industrial practice.

Laser ultrasonic frequency-domain imaging and phase weighted optimization based on full matrix capture

Far-field laser technology has greatly promoted the progress of nondestructive ultrasonic imaging of bulk structures. However, under thermoelastic excitation, the body waves exhibit a relatively low signal-to-noise ratio, resulting in images with low resolution and contrast. Based on the motivation, this paper developed a frequency-domain phase weighted imaging method to improve the quality of laser ultrasonic defect imaging. Firstly, laser ultrasonic scanning was performed on the sample with artificial transverse hole defects. The cylindrical lens focused line source was used to improve the intensity of the body wave signals, and ensure that there was no damage on the material surface under high laser energies. Then, the frequency-domain phase shift migration (PSM) algorithm was used to perform multimode imaging of defects, achieving frequency-domain synthetic aperture focusing technique (F-SAFT) and total focused method (F-TFM) imaging based on full matrix capture. Furthermore, the phase circular statistical vector (PCSV) was proposed for weighted optimization, which improved the image quality, suppressed the background noise and multimode artifacts. Finally, the imaging quality of several algorithms were discussed. The results indicate that frequency-domain images were superior to time-domain results. After phase weighting, the imaging quality can be further improved, and the detection blind zone was significantly reduced. This work will contribute to the rapid and high-quality defect imaging of laser ultrasonic.

A New Denoising Method VSCE for High-Temperature Shear Wave Electromagnetic Ultrasonic Defect Detection at a Low Excitation Voltage

A New Denoising Method VSCE for High-Temperature Shear Wave Electromagnetic Ultrasonic Defect Detection at a Low Excitation Voltage

Rubber mandrel design suitable for manufacture co-cured hat-stiffened composite panel with large curvature by auto fiber placement technique

Producing advanced composite part with complex contour and configuration by automated manufacture process is a challenging research field ever since composite materials have been widely used in aerospace industry. In this study, three potential rubber mandrel designs suitable for producing integral hat-stiffened co-curing panel with large curvature by automatic fiber placement (AFP) technique were discussed. Finite element analysis (FEA) was utilized to study the mechanical behavior of these mandrels during the AFP process. Based on the FEA result, a mandrel design with an outer rubber bladder and a corset inner rubber mandrel enhanced with a flexible steel supporter was selected out. An actual experimental part was manufactured to study the feasibility of such design. Magnetic thickness measurement, ultrasonic defect test, and cross-section examination by metallographic microscope were conducted to study the quality of the experimental part. The results revel that producing hat-stiffened co-curing large curvature panel with desirable quality could be achieved by applying the refined rubber mandrel designed in this research.

Advancing Ultrasonic Defect Detection in High-Speed Wheels via UT-YOLO.

In the context of defect detection in high-speed railway train wheels, particularly in ultrasonic-testing B-scan images characterized by their small size and complexity, the need for a robust solution is paramount. The proposed algorithm, UT-YOLO, was meticulously designed to address the specific challenges presented by these images. UT-YOLO enhances its learning capacity, accuracy in detecting small targets, and overall processing speed by adopting optimized convolutional layers, a special layer design, and an attention mechanism. This algorithm exhibits superior performance on high-speed railway wheel UT datasets, indicating its potential. Crucially, UT-YOLO meets real-time processing requirements, positioning it as a practical solution for the dynamic and high-speed environment of railway inspections. In experimental evaluations, UT-YOLO exhibited good performance in best recall, mAP@0.5 and mAP@0.5:0.95 increased by 37%, 36%, and 43%, respectively; and its speed also met the needs of real-time performance. Moreover, an ultrasonic defect detection data set based on real wheels was created, and this research has been applied in actual scenarios and has helped to greatly improve manual detection efficiency.

Inception meets Swin Transformer: A Novel Approach for Metal Defect Recognition

The detection of metal defects with high precision and efficiency is a significant challenge in modern industry. Existing machine learning methods for recognizing common metal surface defects heavily rely on expert knowledge for manual feature extraction. Conventional deep learning methods face challenges in capturing global feature information from defect images or defect detection signals.To address this issue, we proposed a metal defect recognition method based on an Inception-fused Swin Transformer model. The method combines the adaptive local feature extraction capability of the Inception structure with the advantage of the Swin Transformer in capturing global feature information from defect signals. Additionally, it utilizes the Channel-Coordinate Attention module (CoordAttention) to highlight important feature channels. Experimental results demonstrate the effectiveness of the proposed method on the Ultrasonic Defect Grayscale Image dataset (ULFSL-DET) and the publicly available Image-based Metal Defect dataset (NEU-CLS), achieving recognition accuracies of 98.1% and 99.8%, respectively. The method exhibits high effectiveness in recognizing metal defect signals in grayscale images, and it demonstrates strong generality for image-based metal defect recognition.

Advancing Carbon Fiber Composite Inspection: Deep Learning-Enabled Defect Localization and Sizing via 3-D U-Net Segmentation of Ultrasonic Data.

In nondestructive evaluation (NDE), accurately characterizing defects within components relies on accurate sizing and localization to evaluate the severity or criticality of defects. This study presents for the first time a deep learning (DL) methodology using 3-D U-Net to localize and size defects in carbon fiber reinforced polymer (CFRP) composites through volumetric segmentation of ultrasonic testing (UT) data. Using a previously developed approach, synthetic training data, closely representative of experimental data, was used for the automatic generation of ground truth segmentation masks. The model's performance was compared to the conventional amplitude 6 dB drop analysis method used in the industry against ultrasonic defect responses from 40 defects fabricated in CFRP components. The results showed good agreement with the 6 dB drop method for in-plane localization and excellent through-thickness localization, with mean absolute errors (MAEs) of 0.57 and 0.08 mm, respectively. Initial sizing results consistently oversized defects with a 55% higher mean average error than the 6 dB drop method. However, when a correction factor was applied to account for variation between the experimental and synthetic domains, the final sizing accuracy resulted in a 35% reduction in MAE compared to the 6 dB drop technique. By working with volumetric ultrasonic data (as opposed to 2-D images), this approach reduces preprocessing (such as signal gating) and allows for the generation of 3-D defect masks which can be used for the generation of computer-aided design files; greatly reducing the qualification reporting burden of NDE operators.

Research on ultrasonic defect imaging based on a neural network with Gaussian weight function fusion model

Ultrasonic testing is widely used in the industrial field because of its high sensitivity, safety and low-cost, which plays an increasingly important role in detecting defects on industrial. However, at present, defect detection only stays in scanning surface defects and classifying the internal defects of the specimen. The accuracy of quantitative imaging of the morphology of the internal defects of the specimen is still an issue to be improved. In this paper, the simple defects in several specimens are detected by ultrasound based on finite element simulation software and the accuracy of the signal can be verified by ultrasonic experiment. Then, the features can be extracted by signal processing method. The neural network imagines the inversion results with the Gaussian weight function fusion model. Finally, this paper uses the signals of simple shape defects as the training set to detect the shapes of complex defects and obtain the corresponding imaging results.

Frequency shifting of transmitted ultrasound in thick composites containing fiber wrinkles and its application in non-destructive evaluation

Thick composite structures are prone to defects such as fiber wrinkles and voids during manufacturing. It is challenging to accurately evaluate the forming quality of the structures using conventional ultrasonic inspection, due to the coupling effects between time-frequency features of ultrasound and mixed-type defects. Propagation behaviors of through-thickness transmitted ultrasound in thick composites with fiber wrinkles, voids and no defects were comparatively investigated by both simulation and experiment in this study. Influence of out-of-plane fiber wrinkles on the ultrasonic frequency shifting was studied by comparing the time-frequency characteristics of simulated ultrasonic signals after propagating different distances in the intact and wavy composites. Changes of transmission coefficients of converted transverse waves caused by fiber wrinkles were concluded be the reason for the appearance of high-frequency components above the excitation frequency in the transmitted ultrasound signal. Thereby ultrasonic defect inspection indices based on the phenomenon of ultrasonic frequency shifting were defined. Effects of interaction angles between ultrasonic propagation direction and fibers on the defined inspection indices was further investigated quantitatively. The thick composites with diverse defects were successfully diagnosed using the ultrasound frequency shifting and energy dissipation behaviors.

Ultrasonic defect detection of high-density polyethylene pipe materials using FIR filtering and block-wise singular value decomposition

Condition monitoring of high-density polyethylene (HDPE) pipes used for fluid and gas transfer is important for the safety of energy conservation and the environment. Ultrasonic phased array imaging methods provide a solution to detect and assess defects in HDPE pipes. However, ultrasonic bulk waves propagating in these viscoelastic media are strongly attenuated, resulting in reduced signal amplitude. In this study, a linear-phase Finite Impulse Response (FIR) filter is used to remove unwanted frequency components from the measured ultrasonic signals to improve the signal-to-noise ratio before applying the imaging algorithm of the total focusing method (TFM). Building upon this, a block-wise singular value decomposition (SVD) technique, which can adaptively determine the singular value cutoff threshold based on each block in the whole TFM image, is used to enhance the obtained TFM image quality. The performance of the combination of FIR filtering and block-wise SVD technique is validated by experimental data of HDPE pipe materials. Results demonstrate that the proposed approach generates good images to provide the detection and characterization of side-drilled holes in HDPE pipe materials.

Ultrasonic defect detection in noisy signals by a nonconvex sparse regularization approach

Ultrasonic testing has been an important tool for the detection of hidden defects in materials yet its effectiveness is usually compromised by the noise originated from both the testing system and the material being evaluated. Motivated by the prior knowledge that only a small number of defects or reflectors exist within the tested material thus the measured signal can be viewed as a linear combination of a few echoes, a signal processing method is proposed in this study where a nonconvex sparse regularization method based on lp-norm (0 < p < 1) penalty is employed to suppress noises in ultrasonic NDT signals thus enhancing the effectiveness and accuracy of flaw detection. Based on a specially designed over-complete Gabor dictionary, the nonconvex sparse regularization method is introduced to sparsely represent the noisy signal. After signal representation, noise is suppressed by a pruning operator, facilitating the identification and reconstruction of flaw-reflected echoes. The performance of the proposed method is quantitatively evaluated and compared with competing algorithms using simulated noisy ultrasonic signals in a statistical manner. Experimental results are also presented to demonstrate the effectiveness of our method.

Automatic classification of weld defects from ultrasonic signals using WPEE-KPCA feature extraction and an ABC-SVM approach

The classification of weld defects is very important for the safety assessment of welded structures and feature extraction of ultrasonic defect signals is vital for defect classification. A novel approach based on wavelet packet energy entropy (WPEE) and kernel principal component analysis (KPCA) feature extraction and an artificial bee colony optimisation support vector machine (ABC-SVM) classifier is proposed in this paper. Firstly, the WPEE method is adopted to extract ultrasonic signal features of weld defects and KPCA is used for feature selection. Secondly, an ABC-SVM classifier is employed to perform defect classification. Finally, experiments involving defect feature extraction, selection and classification are carried out using four types of weld defect. The results demonstrate that the performance of the proposed feature extraction method based on WPEE is superior to that of wavelet packet energy (WPE). In addition, the WPEE-KPCA method achieved a higher accuracy rate of defect classification than WPEE.

Interpretable & Explainable Machine Learning for Ultrasonic Defect Sizing.

Despite its popularity in literature, there are few examples of machine learning (ML) being used for industrial nondestructive evaluation (NDE) applications. A significant barrier is the 'black box' nature of most ML algorithms. This paper aims to improve the interpretability and explainability of ML for ultrasonic NDE by presenting a novel dimensionality reduction method: Gaussian feature approximation (GFA). GFA involves fitting a 2D elliptical Gaussian function an ultrasonic image and storing the seven parameters that describe each Gaussian. These seven parameters can then be used as inputs to data analysis methods such as the defect sizing neural network presented in this paper. GFA is applied to ultrasonic defect sizing for inline pipe inspection as an example application. This approach is compared to sizing with the same neural network, and two other dimensionality reduction methods (the parameters of 6 dB drop boxes and principal component analysis), as well as a convolutional neural network applied to raw ultrasonic images. Of the dimensionality reduction methods tested, GFA features produce the closest sizing accuracy to sizing from the raw images, with only a 23% increase in RMSE, despite a 96.5% reduction in the dimensionality of the input data. Implementing ML with GFA is implicitly more interpretable than doing so with principal component analysis or raw images as inputs, and gives significantly more sizing accuracy than 6 dB drop boxes. Shapley additive explanations (SHAP) are used to calculate how each feature contributes to the prediction of an individual defect's length. Analysis of SHAP values demonstrates that the GFA-based neural network proposed displays many of the same relationships between defect indications and their predicted size as occur in traditional NDE sizing methods.

Frequency shifting of transmitted ultrasound in thick composites containing fiber wrinkles and its application in non-destructive evaluation

Thick composite structures are prone to defects such as fiber wrinkles and voids during manufacturing. It is challenging to accurately evaluate the forming quality of the structures using conventional ultrasonic inspection, due to the coupling effects between time-frequency features of ultrasound and mixed-type defects. Propagation behaviors of through-thickness transmitted ultrasound in thick composites with fiber wrinkles, voids and no defects were comparatively investigated by both simulation and experiment in this study. Influence of out-of-plane fiber wrinkles on the ultrasonic frequency shifting was studied by comparing the time-frequency characteristics of simulated ultrasonic signals after propagating different distances in the intact and wavy composites. Changes of transmission coefficients of converted transverse waves caused by fiber wrinkles were concluded be the reason for the appearance of high-frequency components above the excitation frequency in the transmitted ultrasound signal. Thereby ultrasonic defect inspection indices based on the phenomenon of ultrasonic frequency shifting were defined. Effects of interaction angles between ultrasonic propagation direction and fibers on the defined inspection indices was further investigated quantitatively. The thick composites with diverse defects were successfully diagnosed using the ultrasound frequency shifting and energy dissipation behaviors.

Ultrasonic defect characterization using Bayesian inversion and scattering matrix denoising neural networks

The use of scattering matrix and deep neural networks for ultrasonic characterization of inclined crack-like defects in noisy environments was explored. A distortion model was utilized to simulate coherent noises that could contaminate the experimental measurements in practice. Given a test scattering matrix, we first developed an approach for estimating the parameters of the distortion model based on the structural similarity index. Subsequently, a Bayesian inversion approach was adopted to determine the proportion of positive-angled cracks that should be included in the scattering matrix database. Based on this result, a deep neural network model was constructed and used in the denoising procedure, which can effectively reduce the characterization error induced by measurement noise. The simulation showed that the proposed approach can be reliably used for characterization of crack-like defects with large orientation angles relative to the array direction (e.g., 75∘) and small sizes (e.g., 0.8λ). In experiments, six crack-like defects with orientation angles of 60∘ and 75∘ were characterized with errors within 0.1λ (i.e., 0.25 mm) and 5∘ in size and angle, respectively. In addition, the characterization uncertainty measured by the root-mean-squared error was reduced by 44.7% in size compared with the conventional Bayesian approach.

Error-compensated one-dimensional convolutional neural network-based ultrasonic defect signal recognition method for flat ceramic membranes

For the flowing hole affecting the one-dimensional convolutional neural network to identify the ultrasonic defect signal inside the flat ceramic membrane, this study proposed a 1D-CNN based on error compensation for the ultrasonic defect signal identification method of flat ceramic membrane. First, the ultrasonic flaw detector was used to scan the flat ceramic film and obtain the ultrasonic signal of the flat ceramic film. Second, through the analysis of the pulse reflection method, the inherent position of the flowing hole that causes the flowing hole ultrasonic signal was generated with the movement of the probe and change. A "rectangular box" was used for the ultrasonic signal error compensation of the generated flowing hole. Finally, the error-compensated ultrasonic signals were learned and classified employing a 1D-CNN model involving a fused attention mechanism. The experimental results demonstrated that the accuracy of the proposed 1D-CNN based on error compensation for ultrasonic defect identification of flat ceramic films was 95.63%, which was 17.06% higher than that of the 1D-CNN model without error compensation. Thus, the proposed detection method indicates promising potential and value in industrial applications.

Micro solder bump detection of flip chip using improved decision tree model based on SAM image

The increasing packaging density of flip chips brings great challenges for micro defect detection. It is necessary to develop an efficient and automatic inspection system for electronic industrial application. In this paper, the scanning acoustic microscopy (SAM) technology and the optimized classifier were investigated for the automatic detection of solder joint defects of flip chips. Ultrasonic images of 1902 solder bumps from 6 chip samples were obtained using a SAM equipment. The decision tree model was improved by introducing the granularity decision entropy. The feature vectors were extracted from the images of solder bumps, and then used for classification. The results show that the decision tree model correctly detected 1812 solder bumps with the accuracy of 95.3%. It is verified that the ultrasonic defect detection system based on the improve decision tree model has high accuracy for micro defect inspection, which has potential and promising application in the electronic packaging industry.