Analysis Of Defects Research Articles

Three-dimensional hard X-ray ptychographic reflectometry imaging on extended mesoscopic surface structures

Many nanodevices and quantum devices, with their sizes often spanning from millimeters down to sub-nanometer, have intricate low-dimensional, non-uniform, or hierarchical structures on surfaces and interfaces. Since their functionalities are dependent on these structures, high-resolution surface-sensitive characterization becomes imperative to gain a comprehensive understanding of the function–structure relationship. We thus developed hard x-ray ptychographic reflectometry imaging, a new technique that merges the high-resolution two-dimensional imaging capabilities of hard x-ray ptychography for extended objects, with the high-resolution depth profiling capabilities of x-ray reflectivity for layered structures. The synergy of these two methods fully leverages both amplitude and phase information from ptychography reconstruction to not only reveal surface topography and localized structures, such as shapes and electron densities, but also yields statistical details, such as interfacial roughness that is not readily accessible through coherent imaging solely. The hard x-ray ptychographic reflectometry imaging is well-suited for three-dimensional imaging of mesoscopic samples, particularly those comprising planar or layered nanostructures on opaque supports, and could also offer a high-resolution surface metrology and defect analysis on semiconductor devices, such as integrated nanocircuits and lithographic photomasks for microchip fabrications.

Research on the causes analysis and control methods of line defects in the head of hundred-meter rails

In response to the rail head line defect in the production process of hundred-meter rails, through inspection and analysis of rail tracks with line defects, theoretical calculations of rolling deformation, and on-site industrial trials, it is clarified that the rail head line defect is caused by defects such as cracks in the ingot corners, slab furnace marks, and scratches during rolling. Correspondingly, effective measures to reduce the occurrence of rail head line defects are proposed for each of the three defect causes.

Chirp-coded ultrasound for localization of linear and nonlinear scatterers in isotropic media

Chirp-coded ultrasound has been under active research for reciprocal Time Reversal with Nonlinear Elastic Wave Spectroscopy for ultrasonic wave focusing and additionally detection and analysis of nonlinear defects. Localization and imaging of defects under these principles, without scanning or use of other complicated methods, is an open problem. Frequency Modulated Constant Wave technology is widely known in radar applications where the distance to multiple reflecting targets, their velocities and angles of return are found from the chirp signal. These principles are not yet widely used in ultrasound technology. Chirp-coded ultrasound is common between the two methods and enable to maximize total power on target due to longer transmitted signals (as opposed to pulsed operation). This work proposes to unite the Frequency-Modulated Constant-Wave scatterer localization with reciprocal Time Reversal – Nonlinear Elastic Wave Spectroscopy principles to enable the localization of scatterers distant from the transducers and the analysis of their nonclassical nonlinearity which often can be caused by microdamage or cracking. In this paper only the first part (localization of scatterers using frequency-modulated constant wave ultrasound) of the problem is investigated. The main goal is to verify the feasibility of the method for ultrasound in solids and identify the main problems to be solved. Physical experiments and simulations are used to ascertain the detectability of linear and nonlinear scatterers in an isotropic solid medium (acrylic) using ultrasound. Potential problems with this approach are identified, to be solved in future work. In the future, this approach could enable to spatially filter the return signals, enabling to use reciprocal time reversal for focusing the wave energy in space directly onto selected scatterers, using the simplest of test setups: one transmitting and two receiving transducers.

Quantitative correlation between temperature difference and sintering neck length in material extrusion-based additive manufacturing

The performance of polymeric parts fabricated via material extrusion-based additive manufacturing (MEAM) is significantly influenced by the sintering neck length between filaments. Despite its importance, elucidating the underlying mechanism governing the formation of inter-filament sintering necks is challenging due to the difficulties in monitoring temperature fields during MEAM. In this work, the thermal condition in MEAM was simplified using a printer with a tubular hotbed, coupled with a high temporal-spatial resolution infrared (IR) camera to monitor temperature field variations during printing. An advanced IR image processing method, integrating multi-directional retrieval and whole-loop average filtering, was developed to accurately measure temperature differences between filaments. By correlating these measurements with micro-CT characterizations, a quantitative relationship between temperature difference and sintering neck length was established. Through single- or multi-loop defect experiments, microscopic defect detection and analysis were conducted, achieving the prediction of sintering neck length variations at a 10 μm level with an 85 % accuracy. This work is believed to provide potential value for further accurate prediction of the mechanical properties of MEAM parts.

Defect analysis of 3D printed object using transfer learning approaches

Additive manufacturing (AM) is rapidly gaining traction across diverse industries, including healthcare, aerospace, and automotive, mainly due to its ability to enhance production efficiency. However, a critical challenge in AM is the early detection of defects, which can significantly reduce production costs and improve productivity. Addressing this need, our study explored the effectiveness of machine learning (ML) approaches, mainly focusing on transfer learning (TL) models, for defect detection in 3D-printed objects. We utilized a range of TL models such as visualgeometrygroup (VGG)-16, inception-residualnetwork (InceptionResNet)-V2, residual network (ResNet)-50, mobilenetwork (MobileNet)-V2, VGG19, ResNet101, extreme inception (Xception), efficientnetwork (EfficientNet)-B0, EfficientNetB7, efficientnetworkV2medium (EfficientNetV2M), and neural architecture search network large (NASNetLarge), analyzing images of 3D-printed objects across three datasets using various statistical measures. An ablation study was also conducted using adaptive moment estimation (Adam), stochastic gradient descent (Sgd), and root mean square propagation (Rmsprop) optimizers. We identified that TL models such as VGG16, MobileNetV2, InceptionResNetV2, and NASNetLarge significantly outperform others, especially when optimized with Adam and Rmsprop. Conversely, we found that models like EfficientNetB0, EfficientNetB7, and EfficientNetV2M exhibited lower performance when paired with the Sgd optimizer. Additionally, Local Interpretable Model-agnostic (LIME)-based approaches are used to provide explanations of the models’ predictions. These findings offer substantial insights into model optimization and integration, aiming to enhance AM product quality through advanced image-based monitoring and inspection.

Advances in Three-dimensional Finite Element Analysis of Non-carious Cervical Defects

Advances in Three-dimensional Finite Element Analysis of Non-carious Cervical Defects

Analysis of heating defects in extension rods of extra-high voltage direct current overhead transmission lines

This paper studies the overheating defect of an extension rod in a ± 800 kV extra-high voltage direct current overhead transmission line, and proposes various factors that affect the overheating of the extension rod. Establish an equivalent model of an extra high voltage DC transmission line based on equipotential point and line parameter analysis, and calculate the model parameters. The proposed model can also be applied to ultra-high voltage DC overhead transmission lines such as ± 500 kV, and has strong universality. Simulation experiments show that if the equivalent resistance value of the split wire side is reduced by 0.01Ω, the power loss on the extension tie rod can be increased to 380.951 W; if the equivalent resistance value of the jumper side is increased by 0.01Ω, the power loss on the extension tie rod can be increased to 273.41 W. Combined with theoretical analysis and simulation verification, it is concluded that the main factor affecting the overheating of the extension rod is the change in the resistance value on the jumper side caused by the increase in contact resistance. A systematic troubleshooting plan for related defect problems is proposed, which has important reference and guidance significance for such line fault problems.

Combining unsupervised and supervised learning in microscopy enables defect analysis of a full 4H-SiC wafer

Detecting and analyzing various defect types in semiconductor materials is an important prerequisite for understanding the underlying mechanisms and tailoring the production processes. Analysis of microscopy images that reveal defects typically requires image analysis tasks such as segmentation and object detection. With the permanently increasing amount of data from experiments, handling these tasks manually becomes more and more impossible. In this work, we combine various image analysis and data mining techniques to create a robust and accurate, automated image analysis pipeline for extracting the type and position of all defects in a microscopy image of a KOH-etched 4H-SiC wafer.Graphical abstract

Impact of string connection and contact defects on electrical current distribution in solar cells and modules: A model validated by magnetic field imaging

AbstractModeling of solar modules and their components is essential to quantify geometrical, optical, and electrical losses and to improve the designs and technologies in terms of performance. In most loss analysis models, the current share among the busbars of the solar cell is assumed to be equal since a symmetrical distribution of the metallization is given. The impact of string terminal connection on the current distribution among the ribbons and the resulting changes in ohmic losses has not been studied yet. In this study, a MATLAB model is developed to consider the impact of the string connector terminal position on the current distribution and the ohmic losses in the ribbons and in string connector. The model allows for the analysis of the impact of contact defects scenarios in ribbons and string connectors on the current distribution. Results show that the highest current flows at the closest busbar to the string connector terminal while the current decreases at the busbars farther away from the terminal due to higher ohmic resistance of the current path. The higher the ohmic resistance of the string connector, the more inhomogeneous the current share at busbars. Simulating a 9 busbar M6 half‐cell with 1 × 0.08 mm2 string connector, positioning the string connector terminal at the leftmost or rightmost ribbon results in 0.4 W less power compared to center connection configuration, where the string connector terminal is positioned at the center ribbon. Furthermore, simulation results show that inhomogeneity of current causes about 2.1% reduction in module power compared to the case of evenly distributed cell current, considering a 120‐haf‐cell module with the same string connector. Regarding contact defect analysis, exemplary simulations show the impact of the position of detached ribbons on the power or efficiency loss. Considering left or right connection configuration, detaching the leftmost or rightmost ribbon results in higher power loss compared to other ribbons. Detaching one cell ribbon completely from the string connector results in about 0.2%abs decrease in cell efficiency, while detaching the outer ribbon along all strings of a 120‐half‐cell module results in power loss of about 0.8%. The developed model is validated by performing magnetic field imaging (MFI) measurements, in which the magnetic flux density induced by the current carried by the ribbons is measured.

Effects ofAs8structure formation on the surface morphology and internal microstructure of GaAs thin films.

Gallium arsenide (GaAs) materials have the advantages of high electron mobility, electron saturation drift rate, and other irreplaceable semiconducting properties. They play an important role in the electronics, solar and other fields. However, during GaAs film sedimentary growth, As atoms can undergo segregation to formAs8clusters because of the influence of external factors, which affect the surface morphology and internal structure of these films. In this study, a series of investigations on the deposition and growth of GaAs crystal films were performed. Additionally, the deposition and growth of GaAs thin films were simulated using molecular dynamics. The influence of As8clusters on the surface morphology and internal structure of GaAs films at different incidence angles, velocities and substrate temperatures was studied by using 'defect analysis technology' and 'diamond structure identification' in open source software, along with surface roughness and radial distribution function. Results show that with increasing incident angle, the number ofAs8clusters decreases and film density increases. Increasing incident velocity increases the irregular movement ofAs8clusters in air, and their deposition on the film surface affects the morphology of the film, and the surface roughness increases first and then decreases. Additionally, we investigated the effect of different substrate temperatures on the film surface. Results show that at a substrate temperature of 1173 K, the number ofAs8clusters in the film decreases or the As8clusters disappear, heterogeneous nucleation occurs in the film, and the crystallization rate increases. Although the dislocation line associated with nucleation may affect the mechanical and optical properties of the film, it considerably reduces the annealing effort after the deposition and growth.

Cranial trauma in handgun executions: Circumferential delamination defect and its relationship with contact/close-range shooting.

Skeletal evidence usually constitutes the only source of information to interpret lesion patterns that help to clarify the circumstances surrounding death. The examination and interpretation of bone trauma are essential to the application and utility of anthropology as a forensic science. When discussing the effect of gunshot wounds in bone, it becomes imperative to differentiate between short and long-distance injuries based on clear, distinct, and observable signs. To contribute to the debate, our focus is directed toward the external analysis of the so-called circumferential delamination defect (CDD) as an observable proxy for close-range shooting (≤30 cm) and contact gunshot wounds in the skull. In the context of known extrajudicial killings, in which the perpetrators used short 9 × 19 FMJ ammunition in a close-range shooting, instances of CDD have been documented. Empirical evidence reinforcing the causal relationship between CDD and close-range shootings is presented. Elements' characteristics of firearm residues were also found in remains buried for up to 30 years. Primarily, this work shows that the concentrations of gunshot residues (Pb, Ba, and Sb) resemble those observed in fresh corpses with the same gunshot wound (GSW). Moreover, the correlation observed between CDD and gunshot residues, where the likelihood of CDD increases the closer to the head and the more perpendicular the shot angle is, reinforces CDD as a pivotal discriminatory factor in the skeletal evidence of short-range or contact shot. This research contributes to the field of forensic anthropology by providing fundamental insights into the etiology of CDD and its practical application.

A mass defect-based approach for the automatic construction of peak lists for databases of mass spectra with limited resolution: Application to time-of-flight secondary ion mass spectrometry data.

This study has developed a data processing protocol based on mass defect analysis for the automatic construction of unique peak lists addressing the need for the fast and efficient treatment of databases of mass spectra with limited mass resolution. The data processing protocol, implemented in MATLAB, is tested on a database of 126 mass spectra obtained from time-of-flight secondary ion mass spectrometry analysis of the exhaust of a laboratory diesel miniCAST burner deposited on Ti substrates. The data processing protocol converts the mass spectra into a data matrix suitable for chemometrics (peak list) by combining mass defect analysis and multivariate analysis. In particular, the role of the mass defect analysis is expanded to improve mass calibration and automate the construction of the peak list. In this context, mass defect analysis becomes an invaluable technique for the efficient processing of databases of mass spectra with limited mass resolution by allowing the fast and automated construction of a peak list common to all mass spectra, by improving the mass calibration, and finally by reducing the number of molecular formulae consistent with a given accurate mass, thus facilitating the identification of unknown ions.

Deep learning methods for roping defect analysis in aluminum alloy sheets: prediction and grading

Deep learning methods for roping defect analysis in aluminum alloy sheets: prediction and grading

Analysis of Various Defects and Reconstructive Methods after Invasive Thyroid Carcinoma Resection

Analysis of Various Defects and Reconstructive Methods after Invasive Thyroid Carcinoma Resection

A Multi-Information Fusion Method for Repetitive Tunnel Disease Detection

Existing tunnel defect detection methods often lack repeated inspections, limiting longitudinal analysis of defects. To address this, we propose a multi-information fusion approach for continuous defect monitoring. Initially, we utilized the You Only Look Once version 7 (Yolov7) network to identify defects in tunnel lining videos. Subsequently, defect localization is achieved with Super Visual Odometer (SuperVO) algorithm. Lastly, the SuperPoint–SuperGlue Matching Network (SpSg Network) is employed to analyze similarities among defect images. Combining the above information, the repeatability detection of the disease is realized. SuperVO was tested in tunnels of 159 m and 260 m, showcasing enhanced localization accuracy compared to traditional visual odometry methods, with errors measuring below 0.3 m on average and 0.8 m at maximum. The SpSg Network surpassed the depth-feature-based Siamese Network in image matching, achieving a precision of 96.61%, recall of 93.44%, and F1 score of 95%. These findings validate the effectiveness of this approach in the repetitive detection and monitoring of tunnel defects.

Transfer from defective bridge information model to defect analysis model based on industry foundation classes

Bridges, as an important part in modern infrastructure networks, suffer from increasingly severe aging issues in recent years. Road agencies and authorities conduct regular inspections for their bridges and identify defects and deteriorations for condition assessment. Such defect information cannot be efficiently used for analysis in the structural domain. This study proposes an Industry Foundation Classes-based method to allow for the transfer from a defective bridge information model to a defect analysis model. The architectural model of bridge is first converted into analytical model and then the impacts of defects on structural properties are quantified with a stiffness reduction coefficient. Case studies are conducted to validate the applicability of the proposed method for defect analysis and a range of coefficients are tested on an illustrative case bridge. The results prove that the proposed method can generate instructive and informative knowledge on the safety and reliability of the entire structure. This study narrows the gap between the empirical condition assessment scheme and the numerical structural analysis scheme for bridge management and manages to make full use of inspection-related information within Building Information Modelling (BIM) environment.

Measurement of asphalt concrete base thickness using ultrasonic pulse echo

This paper deals with the description and use of one of the up-to-date NDT method, the ultrasonic pulse echo, to determine the thickness of an asphalt concrete model. In the article, the authors explain the principle of the method and the limits of its use. Furthermore, attention is also paid to the influence of the shape and design of the back wall of the tested elements and the speed of propagation of ultrasonic waves. The research is part of a larger work, defect analysis and implementation of Whitetopping technology in the Czech Republic.

Analysis of enamel defects in a cave bear maxillary molar, with remarks on incremental markings in bear enamel

AbstractThe paper discusses the formation of an enamel defect in the crown of a cave bear (Ursus spelaeus sensu lato) left maxillary second molar (M2), based on macroscopic and microscopic analysis. The tooth belongs to a cranium recovered from the Cerovac caves in Croatia that exhibits a partially healed, depressed lesion in the left squama frontalis and a further lesion in the left maxilla associated with loss of the M1. Microscopic inspection demonstrated an accentuated incremental line in both enamel and dentin of the left M2. It is suggested that in the defect area the outer enamel had been posteruptively lost along the accentuated line in the enamel that constituted a zone of reduced mechanical resistance. Presence of enamel hypoplasia in both M2 indicated that these developmental lesions reflect a systemic stress event during crown formation of the teeth. The underlying cause of this stress is assumed to have been a trauma to the skull that caused the lesion in the left squama frontalis. It is further suggested that a later trauma to the left maxilla had led to the loss of the left M1 and the flaking‐off of enamel along the accentuated incremental line in the left M2. The defect in the left M2 is thus diagnosed as the result of a developmental lesion during crown formation, related to systemic stress due to a skull trauma, followed by posteruptive damage from a second traumatic impact. In addition to reconstructing the formation of the defect in the crown of the left M2, the paper, for the first time, describes daily and subdaily incremental markings in ursid enamel and provides preliminary information on enamel secretion rate in a cave bear molar.

Recent progress in defect-engineered metal oxides for photocatalytic environmental remediation.

Rapid industrial advancement over the last few decades has led to an alarming increase in pollution levels in the ecosystem. Among the primary pollutants, harmful organic dyes and pharmaceutical drugs are directly released by industries into the water bodies which serves as a major cause of environmental deterioration. This warns of a severe need to find some sustainable strategies to overcome these increasing levels of water pollution and eliminate the pollutants before being exposed to the environment. Photocatalysis is a well-established strategy in the field of pollutant degradation and various metal oxides have been proven to exhibit excellent physicochemical properties which makes them a potential candidate for environmental remediation. Further, with the aim of rapid industrialization of photocatalytic pollutant degradation technology, constant efforts have been made to increase the photocatalytic activity of various metal oxides. One such strategy is the introduction of defects into the lattice of the parent catalyst through doping or vacancy which plays a major role in enhancing the catalytic activity and achieving excellent degradation rates. This review provides a comprehensive analysis of defects and their role in altering the photocatalytic activity of the material. Various defect-rich metal oxides like binary oxides, perovskite oxides, and spinel oxides have been summarized for their application in pollutant degradation. Finally, a summary of existing research, followed by the existing challenges along with the potential countermeasures has been provided to pave a path for the future studies and industrialization of this promising field.

Enhancing photovoltaic module sustainability: Defect analysis on partially repaired modules from Spanish PV plants

Between 2010 and 2022, there has been a significant increase in photovoltaic (PV) solar energy installations, driven by factors such as cost reductions, technological advances, policy support and innovative financing approaches. However, this rapid growth has created a significant challenge in the form of increased PV waste generation, coinciding with global commitments to achieve carbon neutrality by 2050. The potential for reuse of decommissioned PV modules represents an environmentally sustainable way forward that requires a comprehensive assessment of technical, economic, environmental and regulatory considerations. This study focuses on evaluating the impact of defects in partially repaired PV modules from Spanish installations on their power output and efficiency, with the aim of developing strategies to increase reuse and reduce environmental impact. The research focuses on 23 crystalline silicon modules and uses established techniques to evaluate their operational condition, detect defects and assess potential safety risks. Among the defects observed, moisture-induced degradation (MID) emerges as the most prevalent, followed by cracked cells and disconnected areas in cells across different technologies. Notably, despite the presence of defects, approximately 87 % of these modules have a power loss of less than 20 %.