Quantitative Analysis Of Defects Research Articles

Differential phase contrast STEM image calculation software – Magnifier

An innovative software with a user-friendly interface for calculation of differential phase contrast (DPC) scanning transmission electron microscopy images (integrated iDPC- and differentiated dDPC-STEM) is presented. The underlying algorithm is described and the program functionalities are demonstrated on the examples of Li5OsO6, α-Ga2O3, and LiCoO2. The software supports interpretation of DPC-STEM images, which is crucial for qualitative and quantitative analysis of crystal structures and defects.

Damage kinetics in high-temperature irradiated Ni crystals

High-temperature (∼800 K) ion irradiation with 380 keV Ar ions was used to modify the surface of Ni single crystals in order to generate radiation defects and mimic the effect of neutrons avoiding sample activation. The modified 800 nm thick layer was analyzed regarding structural and mechanical properties utilizing a combination of experimental and simulation techniques. The ion implantation fluence ranged from 1 × 1014 cm−2 up to 1 × 1016 cm−2, which corresponds to values of displacements per atom from 0.25 to 25, respectively. The structural characterization was performed by Rutherford Spectroscopy in Channeling mode (RBS/C) associated with scanning (SEM) and transmission electron microscopy (TEM). The experimental results were supported by Monte Carlo (McChasy) and Molecular Dynamics (MD-LAMMPS) simulations. The simulations performed using the second generation of the McChasy code show that the influence of bubbles formed inside the material is not negligible and affects the quantitative analysis of defects in the simulated spectra. A second step in damage kinetics was revealed for the highest dose (i.e., 25 dpa) as a consequence of the entanglement of dislocations and agglomeration of noble gas bubbles, which was confirmed by TEM analysis. Moreover, nanomechanical results confirm that Ar bubbles deteriorate mechanical properties such as hardness.

A Magnetic Flux Leakage Detector for Ferromagnetic Pipeline Welds with a Magnetization Direction Perpendicular to the Direction of Travel.

For the sake of realizing the safety detection of natural gas and petroleum pipeline welds, this paper designs a ferromagnetic pipeline weld magnetic flux leakage detector based on the calculation of the magnetic circuit of the detection probe, with the magnetization direction perpendicular to the traveling direction. The traditional pipeline magnetic flux leakage detection device uses a detection system mode in which the magnetization direction is parallel to the direction of travel. However, due to the structural characteristics of the weld, the traditional detection system mode is not applicable. Since the weld magnetic flux leakage detector needs to travel along the direction of the weld, the detector designed in this paper rotates the magnetizer 90 degrees along the direction of the weld seam so that the magnetization direction is perpendicular to the direction of travel, breaking through the technical barrier that make traditional magnetic flux leakage detection devices unsuitable for weld detection. The detection device includes a magnetizing structure, a data sampling device, and a driving and traveling device. The magnetic flux leakage signal collected by the detector is converted into a digital image in the form of a grayscale matrix. Using mathematical morphology and chain code algorithms in image processing technology, a pipeline weld defect inversion software system is developed, and a preliminary quantitative analysis of pipeline weld defects is achieved. The application of this technology enables the inspection and protection of oil and gas pipeline welds throughout their life cycle, broadens the scope of existing inspection objects, and is of great safety significance for ensuring national public security.

Application of Weak-Beam Dark-Field STEM for Dislocation Loop Analysis†.

Nanoscale dislocation loops formed by irradiation can significantly contribute to both irradiation hardening and embrittlement of materials when subjected to extreme nuclear reactor environments. This study explores the application of weak-beam dark-field (WBDF) scanning transmission electron microscopy (STEM) methods for quantitative irradiation-induced defect analysis in crystalline materials, with a specific focus on dislocation loop imaging and analysis. A high-purity Fe-5 wt% Cr model alloy was irradiated with 8 MeV Fe2+ ions at 450°C to a fluence of 8.8 × 1019 m-2, inducing dislocation loops for analysis. While transmission electron microscopy (TEM) has traditionally been the primary tool for dislocation imaging, recent advancements in STEM technology have reignited interest in using STEM for defect imaging. This study introduces and compares three WBDF STEM methods, demonstrating their effectiveness in suppressing background contrasts, isolating defect information for dislocation loop type classification, providing finer dislocation line images for small loop analysis, and presenting inside-outside contrast for identifying loop nature. Experimental findings indicate that WBDF STEM methods surpass traditional TEM approaches, yielding clearer and more detailed images of dislocation loops. The study concludes by discussing the potential applications of WBDF STEM techniques in defect analysis, emphasizing their adaptability across various material systems beyond nuclear materials.

Application of residual network based on multispectral attention mechanism in stepped eddy current thermography defect detection

The stepped eddy current thermography nondestructive testing technique has a low and uneven temperature rise under weak excitation conditions, which brings great challenges to the quantitative analysis of defects in the inner wall of pressure vessels. To solve the above problems, this paper proposes a multi-spectral channel attention residual network model based on a Gramian angular difference field (GADF) for automatic detection and quantification. Firstly, the noise reduction and compression of temperature time series data are accomplished by singular value decomposition and piecewise aggregate approximation. Secondly, three infrared feature strategies commonly used for quantitative analysis were transformed by GADF and Gramian angular sum field (GASF) techniques to filter out the infrared feature data sets that contain the most defective feature information. Finally, the frequency domainization of the feature map is realized by embedding the multispectral channel attention in the residual network. The global dependencies and relevant essential features of infrared data are effectively captured, and the redundant feature information generated by deep learning feature maps is suppressed. The effectiveness of the model is verified by comparing several time-series models with advanced deep-learning models. The results show that the proposed model has excellent performance in terms of accuracy, stability, and generalization ability. Meanwhile, the model can effectively extract the defects in the region of uneven temperature rise.

Quantitative Detection of Pipeline Cracks Based on Ultrasonic Guided Waves and Convolutional Neural Network.

In this study, a quantitative detection method of pipeline cracks based on a one-dimensional convolutional neural network (1D-CNN) was developed using the time-domain signal of ultrasonic guided waves and the crack size of the pipeline as the input and output, respectively. Pipeline ultrasonic guided wave detection signals under different crack defect conditions were obtained via numerical simulations and experiments, and these signals were input as features into a multi-layer perceptron and one-dimensional convolutional neural network (1D-CNN) for training. The results revealed that the 1D-CNN performed better in the quantitative analysis of pipeline crack defects, with an error of less than 2% in the simulated and experimental data, and it could effectively evaluate the size of crack defects from the echo signals under different frequency excitations. Thus, by combining the ultrasonic guided wave detection technology and CNN, a quantitative analysis of pipeline crack defects can be effectively realized.

Quantitative Detection of Defects in Multi-Layer Lightweight Composite Structures Using THz-TDS Based on a U-Net-BiLSTM Network.

Multi-layer lightweight composite structures are widely used in the field of aviation and aerospace during the processes of manufacturing and use, and, as such, they inevitably produce defects, damage, and other quality problems, creating the need for timely non-destructive testing procedures and the convenient repair or replacement of quality problems related to the material. When using terahertz non-destructive testing technology to detect defects in multi-layer lightweight composite materials, due to the complexity of their structure and defect types, there are many signal characteristics of terahertz waves propagating in the structures, and there is no obvious rule behind them, resulting in a large gap between the recognition results and the actual ones. In this study, we introduced a U-Net-BiLSTM network that combines the strengths of the U-Net and BiLSTM networks. The U-Net network extracts the spatial features of THz signals, while the BiLSTM network captures their temporal features. By optimizing the network structure and various parameters, we obtained a model tailored to THz spectroscopy data. This model was subsequently employed for the identification and quantitative analysis of defects in multi-layer lightweight composite structures using THz non-destructive testing. The proposed U-Net-BiLSTM network achieved an accuracy of 99.45% in typical defect identification, with a comprehensive F1 score of 99.43%, outperforming the CNN, ResNet, U-Net, and BiLSTM networks. By leveraging defect classification and thickness recognition, this study successfully reconstructed three-dimensional THz defect images, thereby realizing quantitative defect detection.

Reossification of Bone Defects After Surgical Correction of Nonsyndromic Craniosynostosis: A Review and An Original Study.

Surgical correction of nonsyndromic craniosynostosis (NSC) aims to restore cranial shape. Reossification of bone defects is paramount for the best aesthetic prognosis. However, the literature on the quantitative evaluation of bone defects after NSC surgery is scarce. This study aimed to quantify and analyze the surface area of bone defects after NSC surgery and establish a threshold value for predicting persistent defects. We conducted a systematic review and a prospective study of 28 children undergoing surgical treatment for NSC. We analyzed 426 defects on the first computed tomography scan (1 year postoperative) and 132 defects on the second computed tomography scan (4.6 years postoperative). Statistical analysis was performed using Spearman's rank correlation coefficient, Mann-Whitney-Wilcoxon rank-sum test, and Youden's J statistic. Our systematic review identified three studies reporting on bone defects' surface area and reossification rate. In our study, we found no statistically significant differences in the number or size of defects between sex or type of NSC. The threshold value for the surface area of bone defects above which there was a higher probability of persistent defects was 0.19 cm2 (Youden point), with an 89.47 % probability of persistence. Defects with a surface area below 0.19 cm2 had a considerably lower probability, only 15.07%, of persistence over time. Our study provides valuable quantitative data for managing bone defects after NSC surgery. Defects with a surface area above 0.19 cm2 should be monitored with radiological imaging because of the risk of persistence. Our findings highlight the importance of developing robust and reproducible methods for the quantitative analysis of bone defects after NSC surgery.

Double Gated a-InGaZnO TFT Properties Based on Quantitative Defect Analysis and Computational Modeling

Double Gated a-InGaZnO TFT Properties Based on Quantitative Defect Analysis and Computational Modeling

An automated method to analyze root filling voids and gaps using confocal microscopy images.

This study evaluated the feasibility of an automated method to delimit the required area to quantitatively analyze root filling voids and gaps from cross-sectional confocal laser scanning microscopy (CLSM) images. Root canals of maxillary canines were prepared with rotary instruments and filled by lateral compaction technique using gutta-percha and AH Plus sealer. The roots were stored (100% humidity, 37°C) for a period of 24h and then transversally sectioned to obtain 2-mm-thick slices from the apical and middle thirds. The areas corresponding to filling materials, gaps, and voids were manually delimited or automatically demarked by ImageJ software after converting the images to the RGB color system. Based on manual and automatic delimitations, the percentages of voids and gaps were calculated. Data of voids and gaps between middle and apical thirds were individually compared by paired t-test. Pearson`s correlation test was used to assess the correlation of data between the methods. Irrespective of the method of area delimitation, no difference was observed between the root thirds for both voids and gaps, while the p-values calculated for each method were similar. Almost perfect correlations between the methods were observed for both outcomes. The proposed method to automatically delimit the areas corresponding to filling material, voids, and gaps appears to be a valid method to facilitate the quantitative analysis of defects in root canal fillings using topographic CSLM images.

Ultrasonic visualization and quantitative analysis of internal defects in RTV coatings

Room temperature vulcanised (RTV) silicone rubber coatings effectively enhance the insulation properties of electrical equipment. However, RTV coatings are prone to internal defects caused by the coating process and the effects of aging during service, which can lead to debonding of the coatings. Internal debonding defects are challenging to detect and can ultimately lead to accidents due to a reduction in the insulation capacity of the equipment. To visualize the internal defect morphology of RTV coatings and quantify the defect size, an ultrasonic pulse-echo-based method for detecting and imaging debonding defects is proposed. The method involves the development of a finite element model to investigate how ultrasonic waves propagate in RTV coatings and the influence of ultrasonic probes and inspection conditions on defect echoes. Furthermore, an ultrasonic detection system specifically designed for RTV coating debonding defects is constructed. This system utilizes wavelet packets in the time-frequency domain to analyze the echo signals in both normal and defective regions. The three-dimensional reconstruction of the debonding defect morphology is accomplished by integrating ultrasonic echo amplitude and position information. Finally, the size of the debonding defects is quantified using an adaptive threshold segmentation method. The findings indicate that ultrasound waves reflected in RTV materials propagate as spherical waves, with the acoustic energy primarily concentrated near the acoustic axis. As the propagation distance increases, the sound beam disperses along the axis and extends beyond the transducer, resulting in a decrease in the sound field's directionality. The developed visual reconstruction method in this study offers the capability of three-dimensional visualization for defects present within RTV coatings, including their length, width, and depth. The accurate determination of defect size is achieved through the utilization of the adaptive threshold segmentation method, yielding an average error rate of 5.7 % across different defect types. In comparison, the maximal interclass variance method (OTSU) and the fuzzy C-means (FCM) method produced results with error rates of 9.8 % and 7.9 %, respectively. The research presented in this paper enables precise assessment of debonding defect severity and establishes a reliable foundation for on-site inspection, operation, and maintenance of RTV coatings.

Defect-rich conversion-based manganese oxide nanofibers: An ultra-high rate capable anode for next-generation binder-free rechargeable batteries

Recently, nanostructured materials with oxygen defects and their utilization as electrode materials for lithium-ion batteries (LIBs) are gaining considerable popularity. However, it is still unclear how surface morphology, nanostructuring, and oxygen defects contribute to Li-storage characteristics. In view of this, the present research work is focused on the fabrication of one-dimensional, porous, and oxygen-deficient nanofibers of MgMn2O4 (MMO) through electrospinning technique, followed by their subsequent heat-treatment on various temperatures such as 450 °C, 600 °C, and 750 °C. A quantitative analysis of oxygen defects indicates that MMO-600 contains a high number of oxygen vacancies. Further, the binder-free electrodes of all the samples are fabricated via electrophoretic deposition technology and their Li-storage performance is investigated. As a binder-free LIB anode, MMO-600 exhibits excellent reversible specific capacity, rate capability, and cyclic stability (817 mAh g−1 after 500 cycles at 2 C) with ∼ 99% coulombic efficiency. Further, MMO-600 displays the capacity of 228 mAh g−1 at 10 C is found to be better than the MMO-450 and MMO-750. Additionally, the feasibility of MMO-600 is also investigated in full cell configuration with a binder-free LiNi1/3Mn1/3Co1/3O2 cathode, which exhibited an energy density of 166 ( ± 3) Wh kg−1.

A deep learning model for automatic analysis of cavities in irradiated materials

Transmission electron microscopy (TEM) is a commonly used technique in materials science for defect investigation. Quantitative analysis of defects is important for understanding the properties of a material, but manual analysis of TEM micrographs can be time-consuming and prone to error, especially when the defects have irregular shapes rather than spherical shapes. Many existing methods or deep learning models do not handle a wide range of sizes for the same object type within a single image. In this work, we present a framework that enables users to train an instance segmentation model called Mask R- CNN on any microstructure dataset, perform multi-detection on the same image at different scales, and obtain properties (e.g., size, area) of the objects based on the desired shape (e.g., circle, ellipse, rectangle). Additionally, we have developed a parallel detection module that uses multiple GPUs to increase the efficiency of the object detection process. We demonstrate the capabilities of our framework using a set of TEM images of cavities with different shapes, size distributions, and background contrasts. Finally, we show that the performance of our model in terms of density, size, and swelling of the cavities is comparable to the human average and that our model achieves the highest recall value compared to existing methods due to the use of image multi-rescaling.

Research on the force-magnetic coupling of steel wire and defect evaluation based on self-magnetic flux leakage effect

As an important load-bearing component, wire rope is widely used in engineering structures. It is significant to conduct nondestructive testing on the wire rope and ensure its safe operation. The magnetic signal inspection based on the self-magnetic flux leakage (SMFL) effect can effectively identify the location of defects. However, the current research on the magnetic signal of defects with the influence of various factors is insufficient. Based on the SMFL inspection of defective steel wire under tensile load, it puts forward an approach to evaluate the defect through the peak-valley value of the normal component Hp(y) of the magnetic signal. Then the numerical simulation of force-magnetic coupling with COMSOL Multiphysics is conducted. Finally, the additive model is applied to analyze the factors affecting the magnetic characteristic parameters. The results show that the peak-valley values of Hp(y) with different sizes of defects increase first and then decrease with the increase of load, which can be used to evaluate the stress state of the defective steel wire; According to the nonlinear force-magnetic coupling model based on the thermodynamic relation under the constant weak magnetic field, the SMFL field of the defective steel wire can be calculated through the magnetization constitutive relationship using the representative stress of the refined domains at the defect; By comparing and verifying the results simulated and experimental, the additive model is further introduced to quantitatively analyze the magnetic signal of the defect considering the combined action of the defect depth h, the defect width w and the external load F. The defect depth h has a significant influence on the magnetic signal characteristics, which can be estimated by the additive model with determined w and F. This study is expected to provide some references for the quantitative analysis of defects under loading.

Quantification method of tubing defects based on machine learning algorithm and magnetic flux leakage signals.

Tubing is the pipeline that transports crude oil and natural gas from the oil and gas layer to the surface of the earth. Due to the harsh operating environment, the tubing will suffer from etch pits, scratches, cracks, perforations, and even direct fractures of different degrees of defective conditions. If tubing defects are not detected and quantified in a timely manner, the continued use of tubing will result in tubing leakage and failure. Magnetic flux leakage (MFL) testing as a nondestructive testing method enables the identification and quantitative analysis of defects in metal tubing. To improve the quantification accuracy of defects in the wellhead MFL testing of tubing defects during workover operations, this paper proposes a multi-output least-squares support vector regression machine (MLSSVR) model optimized based on the simulated annealing algorithm. The size of tubing defects can be quantified by establishing the mapping between the characteristic quantity of MFL signals and the defect size. The experimental results of MFL testing of tubing defects show that the root mean square error (RMSE) of the diameter of tubing defects of the simulated annealing algorithm optimized multi-output least-squares support vector regression (SA-MLSSVR) machine model proposed in this paper is 0.4562mm, and the RMSE of the depth of tubing defects is 0.1504mm. Compared with the non-optimized MLSSVR model, the overall RMSE of tubing defects is reduced by 36.48%. The SA-MLSSVR model only needs one-ninth of the time to achieve the same quantification accuracy as the particle swarm optimized multi-output least-squares support vector regression machine model.

Research on defect scanning and spatial locating based on CCIT sensor of single-pair electrode

Coplanar Capacitance Imaging Technology (CCIT) is a kind of nondestructive testing technology based on a capacitance sensing mechanism. However, the edge information of the image is often blurred, and making an accurate quantitative analysis of defects is impossible. This paper was concerned with scanning detection and locating defects based on the CCIT sensor of single-pair electrode. Firstly, the sensitivity distribution characteristics of the CCIT sensor of single-pair electrode were analyzed, and then the scanning detection and imaging scheme of the CCIT sensor of single-pair electrode were designed. Finally, the spatial location of defects was analyzed by using the capacitance values obtained by scanning detection and the visual images obtained by reconstructing. The feasibility of scanning detection and imaging of the coplanar single-pair electrode capacitance sensor was verified through experiments.

Quantitative study of the defects of ferromagnetic materials using the magnetic charge model and particle swarm optimization algorithm

Local defects in components are an important factor responsible for causing damage to steel structures. Hence, metal magnetic memory (MMM) has been used to investigate this issue in recent years. MMM can detect defects based on variations in the self-magnetic leakage of ferromagnetic materials. However, there have been problems of difficulty in quantifying the theoretical parameters of the magnetic charge model and the single characterization of magnetic feature parameters, when using MMM for quantitative analysis of defects. Therefore, the particle swarm optimization (PSO) algorithm was introduced to quicky and accurately quantify the parameters of the magnetic charge model. Theoretical values were compared with experimental data to verify the accuracy of the proposed method. Then, the defect information (defect width and defect depth) was changed and the variation patterns of the magnetic signal and characteristic parameters were analyzed. Finally, the weight of each characteristic parameter was calculated using the entropy value method. A theoretical formula to comprehensively describe defects using multi-feature parameters was proposed. The results show that the theoretical values calculated based on the parameter identification of the PSO algorithm well agreed with the experimental data. Moreover, the proposed formula well described the relationship between the defect width and characteristic parameters. This study is expected to provide a basis for improving the quantitative analysis of MMM.

Defect Pair Formation in FAPbI3 Perovskite Solar Cell Absorbers.

Formamidinium lead iodide (FAPbI3) based hybrid perovskite light absorbers have shown remarkable performance in recent years. Since they have unique set of optoelectronic characteristics, they are considered as a good candidate absorber material for future solar cell applications. Until recently, much research had focused on the quantitative analysis of point defects on halide-based perovskite solar cells. Studies show that understanding defect mechanisms in perovskites has a huge impact on efficiency and stability improvements; however, such mechanisms have not been fully understood yet. Here, using first-principles calculations, we investigate the possible defect pair formations in FAPbI3, characterized by their formation energies and charge transitions. We found that while some donor and acceptor point defects are unstable and shallow when they are isolated, they form stable and deep-trap defect pairs and potentially limit the optoelectronic performance. We anticipate that our results will influence future discussions on the impact of defect formation on the performance and stability of perovskite solar cells.

DefectTrack: a deep learning-based multi-object tracking algorithm for quantitative defect analysis of in-situ TEM videos in real-time

In-situ irradiation transmission electron microscopy (TEM) offers unique insights into the millisecond-timescale post-cascade process, such as the lifetime and thermal stability of defect clusters, vital to the mechanistic understanding of irradiation damage in nuclear materials. Converting in-situ irradiation TEM video data into meaningful information on defect cluster dynamic properties (e.g., lifetime) has become the major technical bottleneck. Here, we present a solution called the DefectTrack, the first dedicated deep learning-based one-shot multi-object tracking (MOT) model capable of tracking cascade-induced defect clusters in in-situ TEM videos in real-time. DefectTrack has achieved a Multi-Object Tracking Accuracy (MOTA) of 66.43% and a Mostly Tracked (MT) of 67.81% on the test set, which are comparable to state-of-the-art MOT algorithms. We discuss the MOT framework, model selection, training, and evaluation strategies for in-situ TEM applications. Further, we compare the DefectTrack with four human experts in quantifying defect cluster lifetime distributions using statistical tests and discuss the relationship between the material science domain metrics and MOT metrics. Our statistical evaluations on the defect lifetime distribution suggest that the DefectTrack outperforms human experts in accuracy and speed.

Ultrasonic Testing of Carbon Fiber-Reinforced Polymer Composites

Composite materials have been extensively used in different fields due to their excellent properties, among which carbon fiber-reinforced polymer (CFRP) is the representative. Especially in high-precision fields, such as aerospace, CFRP has become the main structural material for some core components instead of metal. The particularity of such materials and their components in terms of structure, material properties, and required detection conditions puts forward more stringent and targeted detection requirements for detection technology. Ultrasonic testing technology as one of the important means of composite defect detection, which is derived from advanced nondestructive testing (NDT), has also been a rapid development. The propagation behavior and variation of ultrasonic waves in CFRP composites can reveal defects and damages in CFRP composites. Moreover, by constructing reasonable defect identification technology and detection technology, not only the qualitative and quantitative positioning analysis of defects and damages in CFRP composites can be realized but also the automatic, visual and intelligent NDT, and evaluation of CFRP composites can be realized. This paper mainly reviews the innovative nondestructive ultrasonic testing technology for CFRP composites and briefly introduces the research progress and application of this technology in CFRP defect detection. Finally, advanced nondestructive ultrasonic testing technology is summarized, and the problems and development direction of this kind of testing technology are put forward.