Various Types Of Defects Research Articles

Miniaturized multi-modality field-ready sensing system for defect detection of CFRP materials

As infrastructure decays rapidly due to structural and material aging and failures, the development of new improved, and efficient nondestructive evaluation (NDE) techniques is vital for system health monitoring and preventive maintenance. Composite material, such as carbon fiber reinforced polymer (CFRP) plays a significant role in energy and transportation infrastructure due to the advantages of corrosion resistance, durability, and lightweight, which contributes to minimal maintenance and long service life. However, due to their unique anisotropic dielectric and mechanical characteristics, detecting composite material defects by NDE techniques is still challenging. This paper presents a novel, low-cost miniaturized multi-modality imaging system combining low-frequency capacitive shortwave and high-frequency microwave NDE technologies to detect various types of defects in CFRP materials. Based on the governing electromagnetic theory behind multi-modality electromagnetic NDE methods, the merit of combining low-frequency shortwave and near-field microwave imaging for dielectric characterization of composite materials is thoroughly investigated and demonstrated. Then, a miniaturized imaging system is developed to operate at ultra-wide bands: 10 kHz to 200 MHz for the capacitive shortwave probes and 1 GHz to 9 GHz for the microwave probes. Customized multi-material joining samples of CFRP and steel with different defect types, locations, depths, and sizes are tested by the developed imaging system, and the experimental results of the miniaturized system are compared with the existing table-top systems, which demonstrates comparatively accurate results for the developed multi-modality imaging system. The compact and practical nature of the presented imaging system makes it an optimal tool that can be utilized in field conditions with constrained operational spaces and NDE uncertainties.

Defect-Induced Transport Enhancement in Carbon-Boron Nitride-Carbon Heteronanotube Junctions.

New heteromaterials, particularly those involving nanoscale elements such as nanotubes, have opened a wide window for the next generation of materials and devices. Here, we perform density functional theory (DFT) simulations combined with a Green's function (GF) scattering approach to investigate the electronic transport properties of defective heteronanotube junctions (hNTJs) made of (6,6) carbon nanotubes (CNT) with a boron nitride nanotube (BNNT) as scatterer. We used the sculpturene method to form different heteronanotube junctions with various types of defects in the boron nitride part. Our results show that the defects and the curvature induced by them have a nontrivial impact on the transport properties and, interestingly, lead to an increase of the conductance of the heteronanotube junctions compared to the free-defect junction. We also show that narrowing the BNNTs region leads to a large decrease of the conductance, an effect that is opposite to that of the defects.

Synergic effect of defects on carbon nanoparticles under interaction with calcium silicate hydrate composites

One- and two- dimensional carbon nanoparticles, i.e. graphene and CNT, are amongst the most potent additives in cementitious matrices. Their potential lies within the higher hydration degree, nano-filler effect and cracking-bridging effect that are only realized with a strong connection between the cementitious matrix and graphene. Here, Density functional theory (DFT) and molecular dynamics (MD) used to determine the nature of the interactions between the Tubermorite 14 Å and graphene. We observed that the carbon atoms on the periphery of various types of defects on the surface of a graphene nanosheet and carbon nanotube can form electrophile regions that adsorb silicate chains within the cementitious matrix, forming SiOC covalent bonds between the two species. Single vacancies are highly likely to bond with the silicate chains, with a bond energy of −90 kcal/mol at 1.5 Å length, which is also the case for the silicate – carbon nanotube (CNT) interactions. This is accompanied by the adsorption of Ca ions in Calcium Silicate Hydrates (CSH) on O atoms of functional groups in graphene interactions at the intralayer space, increasing the bond energy between the two nanostructures to 120 kcal/mol. Molecular dynamics (MD) simulations confirm the consolidation of the CSH – graphene around the two interactions, which causes a significant reduction in the mobility of the nanoparticle within the cementitious matrix. These findings provide essential information from the interactions within the interlayer and intralayer regions of CSH with carbon nanoparticles at the molecular level.

Development of a linear array electromagnetic acoustic transducer for shear horizontal guided wave inspection

Ultrasonic guided wave screenings have proven to be fast and reliable in detecting various types of defects in plate-like structures. Today, low frequency ultrasonic guided waves are routinely used to screen long sections of pipelines. For many years, ultrasonic guided waves at frequencies beyond the cutoff of the first high-order mode attracted interest in the research community due to the plurality of modes that can propagate. During operation at a frequency beyond the cutoff of the first high-order mode, the transduction mechanism becomes crucial for selectively exciting and detecting a single high-order mode or a group of high-order modes. Ultrasonic comb transducers allow to selectively excite and detect high-order ultrasonic guided waves at a desired wavelength. Linear array transducers are even more flexible, and allow virtually full control in the frequency wavenumber space. High-order shear horizontal (SH) modes have multiple potential applications including, for example, remote thickness gauging and crack monitoring. However, these modes are notoriously difficult to excite and detect using conventional piezoelectric transducers. Electromagnetic acoustic transducers (EMAT) make the excitation and detection of SH modes relatively simple. Periodic permanent magnet (PPM) EMAT can be used to selectively excite and detect high-order SH modes based on a desired wavelength. However, in some applications, it may be necessary to excite and detect high-order SH modes with more control in the frequency wavenumber space. In this paper, a novel EMAT linear array is proposed using a configuration similar to a PPM EMAT but with individual coils wrapped around the magnets. The performance of the EMAT linear array in transmission and reception is demonstrated in simulations and experimentally on a 9.53 mm steel plate. The results show a successful transmission and detection of SH0 to SH4 for a 300 to 800 kHz frequency range.

Defect‐engineered two‐dimensional transition metal dichalcogenides towards electrocatalytic hydrogen evolution reaction

AbstractRecently, two‐dimensional transition metal dichalcogenides (TMDs) demonstrated their great potential as cost‐effective catalysts in hydrogen evolution reaction. Herein, we systematically summarize the existing defect engineering strategies, including intrinsic defects (atomic vacancy and active edges) and extrinsic defects (metal doping, nonmetal doping, and hybrid doping), which have been utilized to obtain advanced TMD‐based electrocatalysts. Based on theoretical simulations and experimental results, the electronic structure, intermediate adsorption/desorption energies and possible catalytic mechanisms are thoroughly discussed. Particular emphasis is given to the intrinsic relationship between various types of defects and electrocatalytic properties. Furthermore, current opportunities and challenges for mechanical investigations and applications of defective TMD‐based catalysts are presented. The aim herein is to reveal the respective properties of various defective TMD catalysts and provide valuable insights for fabricating high‐efficiency TMD‐based electrocatalysts.

A photovoltaic degradation evaluation method applied to bifacial modules

The need to replace fossil energy sources with renewable energy sources to combat global warming has prompted the growth and development of various technologies, including photovoltaics. At the same time the PV technology is evolving to enhance efficiency and durability of PV modules. Bifacial PV modules is gaining larger share of market due to the higher energy and power density at almost the same price of conventional PV modules.In recent years, as PV systems have been operating for a long time, understanding the aging phenomenon of PV modules has become increasingly important. Various types of defects are known that occur during the life of the modules, however, understanding the trend of degradation through a simple quantification of the decrease in performance over time can be a useful tool. The Performance Loss Ratio can give indications about the state of health of the system and can be used for a rough comparison on the performance reduction guaranteed by the module manufacturer and check if the warranty terms are actually respected.In the present study a simple method is used to evaluate Performance Loss Ratio (PLR) on a real PV plant equipped with bifacial PERC modules.Criticalities were highlighted within each phase of the application of the method. The numerical results of the PLR estimate of the photovoltaic modules of the system are reported, calculated on a two-year dataset starting from the date of installation. Finally, the PLR values, that have been estimated using three different metrics and linear fitting, have been compared with the measured average Power decay values associated exclusively with PV modules.The simplicity of the method allows it to be replicated to other case studies.

Atomic Structure of the Pt Surface after Bombardment with Ar<sup>+</sup> Ions (E ~ 30 keV)

The results of the modification of the atomic structure of the Pt surface after bombardment by accelerated beams of Ar+ ions are presented. Field ion microscopy was used to study the irradiated surface on an atomic scale. From the analysis of the ion contrast of the atomically clean surface of platinum after bombardment with Ar+, a large number of radiation defects of various types were found. Individual vacancies, interstitial atoms, Frenkel pairs, depleted zones, nanoclusters of displaced atoms, etc. have been registered. It has been established that the depth of the near-surface volume of the irradiated metal with defects (except for zero-dimensional defects) is 1 - 1.5 nm for the bombardment regime: F = 1016 ion/cm2 and E = 30 keV.

Laser-induced breakdown spectroscopy and stoichiometry to identify various types of defects in metal-additive manufacturing parts

The purpose of this study is to use LIBS and SVM combined with the stoichiometry to quickly distinguish the defect categories of AM components.

Analysis of the dynamics of changes in gas content in oil-filled equipment with defects of various types

The article presents the results of analysing the dynamics of changes in the values of the ratios of characteristic gases, the percentage of gases, and defect nomograms in the process of developing electrical discharges, local overheating, and combined defects in high-voltage oil-filled equipment. During the analysis, the value of gas concentrations was evaluated for compliance with different levels regulated in the current Ukrainian standard at different stages of defect development. To determine the type of defect based on the values of the ratios of characteristic gases at different points in time, the norms regulated by the IEC 60599 standard and the ETRA square were used. The dynamics of changes in the percentage of gas was analysed using the Duval triangle. The results of the analysis showed that the primary defects during the development of electric discharges can be not only electric discharges with lower intensity, but also thermal (in particular, in the temperature range of 150-300 ℃) and combined defects. At the same time, in the process of discharge development, the nature of defects may change from measurement to measurement. Thus, when assessing the possibility of further operation of transformers, in addition to the degree of danger of the defect and the rate of its development, it is necessary to take into account the possibility of transforming the defect from a less to a more “dangerous” one that develops rapidly. During the development of thermal defects, the primary defect is usually a thermal defect with a lower hot spot temperature. It was found that in equipment with overheating above 700 ℃, the change in gas content during the development of the defect has a similar character. In particular, a mirror image of the change in the percentage of methane in relation to ethylene is observed. This effect can be used to detect high-temperature overheating at an early stage of its development, even before the gas concentrations exceed the limit values, which will help to avoid the destruction of equipment insulation and extend its service life. During the development of combined defects (overheating accompanied by discharges or discharges accompanied by heating), the main defect (e.g., discharge) first appears, and in the process of development, an additional defect (e.g., heating) develops. The results obtained indicate the possibility of detecting and recognizing defects in oil-filled equipment at different stages of their development.

Intelligent Detection and Classification of Surface Defects on Cold-Rolled Galvanized Steel Strips Using a Data-Driven Faulty Model With Attention Mechanism

Abstract In the production of cold-rolled galvanized steel strips used for stamping car body parts, the in-situ and real-time defect detection is crucial for quality control, in which various types of defects inevitably occur. It is challenging to improve the accuracy of defect detection and classification by appropriate means to assist the manual screening process better. Defects under actual production conditions are often not prominent enough in defect characteristics, and there may be a significant similarity between different defect categories. To eliminate this weakness, we propose a data-driven deep learning approach named steel surface faulty detection attention net (SSFDANet) that uses images of the galvanized steel surfaces as input to identify whether the product is qualified and automatic classification of defect types instantaneously. This method can shorten product inspection time and improve the production line automation efficiency. In addition, the attention mechanism is utilized to enhance the performance of SSFDANet. Compared with the baseline ResNet, SSFDANet achieves a noticeable improvement in classification accuracy on test data. The well-trained model can successfully show an improved performance than the baseline models on the multiple types of faulty. Enhanced by SSFDANet with high classification accuracy, the defect rate of products is significantly reduced, and the production speed of the production line is significantly improved. Future prospective studies that are inspired by this article are also discussed.

Description of the development of an experimental stand for the detection of defects in pipelines

The article discusses a bench that allows you to monitor the change in the dynamic characteristics of the medium when various types of defects appear in the connection lines. The setting makes it possible to select the optimal method for recording the measured values. The experimental stand was created as close as possible to the real conditions of the pipeline operation. To detect damage to the air circuit, the integration of technical developments with a software algorithm based on the use of the multiple signal classification method (MUSIC) was applied. The developed stand opens up opportunities for conducting practical experiments and obtaining experimental data directly when detecting pipeline defects.

A Survey on Non-Destructive Smart Inspection of Wind Turbine Blades Based on Industry 4.0 Strategy

Wind turbines are known to be the most efficient method of green energy production, and wind turbine blades (WTBs) are known as a key component of the wind turbine system, with a major influence on the efficiency of the entire system. Wind turbine blades have a quite manual production process of composite materials, which induces various types of defects in the blade. Blades are susceptible to the damage developed by complex and irregular loading or even catastrophic collapse and are expensive to maintain. Failure or damage to wind turbine blades not only decreases the lifespan, efficiency, and fault diagnosis capability but also increases safety hazards and maintenance costs. Hence, non-destructive testing (NDT) methods providing surface and subsurface information for the blade are indispensable in the maintenance of wind turbines. Damage detection is a critical part of the inspection methods for failure prevention, maintenance planning, and the sustainability of wind turbine operation. Industry 4.0 technologies provide a framework for deploying smart inspection, one of the key requirements for sustainable wind energy production. The wind energy industry is about to undergo a significant revolution due to the integration of the physical and virtual worlds driven by Industry 4.0. This paper aims to highlight the potential of Industry 4.0 to help exploit smart inspections for sustainable wind energy production. This study is also elaborated by damage categorization and a thorough review of the state-of-the-art non-destructive techniques for surface and sub-surface inspection of wind turbine blades.

Strength-hardness relationship for AlSi10Mg alloy produced by laser powder bed fusion: An experimental study

In this work, significant strength and ductility variations are reported for AlSi10Mg parts fabricated at different orientations using laser powder bed fusion (LPBF). Hardness and surface roughness of the specimens at different orientations were measured. Tensile testing together with digital image correlation technique were conducted on the specimens. XY specimens showed the highest yield stress and ultimate tensile strength while XZ specimens showed the highest ductility. Hardness measurements for different specimens were in accordance with the tensile test results, following the same order as the UTS values, XY specimens being the highest and XY-45° (out-of-plane) specimens being the lowest. Fractography of the broken surfaces of the specimens under tensile testing revealed the microstructural features and various defects in the tensile fracture. The anisotropy in mechanical properties is attributed to the microstructural anisotropy as well as presence of various types of defects induced by the AM process, which affects the deformation and failure mechanism of the parts. Linear relationships between experimental Vickers hardness versus yield stress and UTS measurements were developed. In case of material selection for different applications, these relationships can be used as a simple tool for converting hardness and yield stress (or UTS) values to each other. An equivalent strain-hardness relationship was also proposed which can be used for health monitoring of parts subject to tensile loading.

Defect chemistry of electrocatalysts for CO2 reduction.

Electrocatalytic CO2 reduction is a promising strategy for converting the greenhouse gas CO2 into high value-added products and achieving carbon neutrality. The rational design of electrocatalysts for CO2 reduction is of great significance. Defect chemistry is an important category for enhancing the intrinsic catalytic performance of electrocatalysts. Defect engineering breaks the catalytic inertia inherent in perfect structures by imparting unique electronic structures and physicochemical properties to electrocatalysts, thereby improving catalytic activity. Recently, various defective nanomaterials have been studied and show great potential in electrocatalytic CO2 reduction. There is an urgent need to gain insight into the effect of defects on catalytic performance. Here, we summarized the recent research advances on the design of various types of defects, including carbon-based materials (intrinsic defects, heteroatom doping and single-metal-atom sites) and metal compounds (vacancies, grain boundaries, and lattice defects). The major challenges and prospects of defect chemistry in electrocatalytic CO2 reduction are also proposed. This review is expected to be instructive in the development of defect engineering for CO2 reduction catalysts.

Defect Engineering in A2 BO4 Thin Films via Surface-Reconstructed LaSrAlO4 Substrates.

Ruddlesden-Popper oxides (A2 BO4 ) have attracted significant attention regarding their potential application in novel electronic and energy devices. However, practical uses of A2 BO4 thin films have been limited by extended defects such as out-of-phase boundaries (OPBs). OPBs disrupt the layered structure of A2 BO4 , which restricts functionality. OPBs are ubiquitous in A2 BO4 thin films but inhomogeneous interfaces make them difficult to suppress. Here, OPBs in A2 BO4 thin films are suppressed using a novel method to control the substrate surface termination. To demonstrate the technique, epitaxial thin films of cuprate superconductor La2- x Srx CuO4 (x= 0.15) are grown on surface-reconstructed LaSrAlO4 substrates, which are terminated with self-limited perovskite double layers. To date, La2- x Srx CuO4 thin films are grown on LaSrAlO4 substrates with mixed-termination and exhibit multiple interfacial structures resulting in many OPBs. In contrast, La2- x Srx CuO4 thin films grown on surface-reconstructed LaSrAlO4 substrates energetically favor only one interfacial structure, thus inhibiting OPB formation. OPB-suppressed La2- x Srx CuO4 thin films exhibit significantly enhanced superconducting properties compared with OPB-containing La2- x Srx CuO4 thin films. Defect engineering in A2 BO4 thin films will allow for the elimination of various types of defects in other complex oxides and facilitate next-generation quantum device applications.

3D positioning of defects for gas turbine blades based on digital radiographic projective imaging

During the manufacturing and service process of gas turbine blades, various types of defects may be formed and bring huge threat to the safe operation of gas turbine. To confirm whether the gas turbine blades with defects could be in safe service for some period of time, the residual strength and fatigue life of gas turbine blade should be analyzed and predicted based on the 3D spatial position of defect, which significantly determines the degree of effect on the strength of gas turbine blade. Considering the characteristics of shape (free-from surface) and material (high density superalloy), the digital radiographic imaging techniques could be used in the non-destructive testing of gas turbine blades. However, the projective nature of digital radiography (DR) technique results in the unavailability of position in the transmitting direction of X-ray beam and brings difficulty to the 3D characterization of defect. Aiming at this critical issue, based on the exploration of the changing rule for the projection of a point feature relative to the irradiating angle, a new method is presented to obtain the 3D spatial position of defect. Finally, the accuracy of the proposed method is validated using a metrological computed tomography (CT) system. It can be learned that the proposed DR based method could obtain the 3D spatial position of defect with a relative error less than 2%. The 3D spatial position of defect could be used in the following residual strength analysis and fatigue life prediction of gas turbine blade.

Subspace analysis based machine learning method for automated defect detection from fringe patterns

We propose a reliable and robust defect detection method from the noisy fringe patterns obtained in optical interferometry. The proposed method relies on a naive Bayes classifier based machine learning model. The model utilizes the phase derivatives computed using fringe signal subspace analysis as feature vectors. This allows for automated defect identification without the requirement of selection of manual threshold parameters. The simulation analysis of various types of defects corroborates the utility of the proposed method. Further, the method is tested on experimental fringes obtained in diffraction phase microscopy to validate its practical applicability.

A novel nanoprobing analysis flow by using multi-probe configuration to localize silicide defect in MOSFET

It is demonstrated that nanoprobing technique is superior in localizing silicide defect rapidly in metal-oxide-semiconductor field-effect transistor (MOSFET). Cobalt silicide (CoSi2) as an interfacial material below tungsten contact plug, various types of defects about CoSi2 often cause leakage current in MOSFET, so close studies of problematic silicide is significant regarding MOSFET reliability enhancement. When one MOSFET in a combinational logic circuit is suspected of anomaly, because this kind of circuit is composed of several devices which are sometimes arranged in an active area (AA) of unique geometric shape, it is impossible to collect MOSFET characteristic correctly based on regular nanoprobing flow since conventional method hits limit for combined circuits measurement. Such circumstance almost impedes failure analysis (FA) in device level. Nevertheless, relying on our alternative strategy of multi-probe configuration, a novel nanoprobing analysis flow is adopted for device examination. It is shown that exact I-V characteristic of failed device can be captured by proposed workflow. Following I-V curve analysis further reveals an evident electrical signature of abnormal abrupt junction. It corresponds to an active area shorting with its surrounding nwell region. In addition, sophisticated leakage current behaviors are exhaustively interpreted by simplified models. Eventually, we confirmed CoSi2 encroachment from the active area into nwell by the means of transmission electron microscopy (TEM) analysis. This paper presents an effective analysis to identify leaky junction of MOSFET in logic circuit that is rarely reported before. Furthermore, our exploration exhibits impressive flexibility of nanoprobing analysis in terms of localizing unusual silicide defect, so nanoprobe analysis is clearly an attractive option to optimize MOSFET manufacturing.

Structure-activity relationship of defective electrocatalysts for nitrogen fixation

Ammonia (NH3), as an important chemical substance and clean energy carrier, plays an indispensable role in industrial and agricultural production. The electrocatalytic synthesis of NH3 under mild conditions has attracted worldwide attention in the energy field due to its environmental friendliness and cost efficiency, but unsatisfactory NH3 yields and Faradaic efficiencies are restricting its development. The introduction of defect has been demonstrated as a feasible way to overcome the disadvantages of electrochemistry, as it can regulate the electronic structure and modulate coordination environment of electrocatalysts, which further create active sites and enhance nitrogen adsorption. In this regard, it is necessary to understand the effects of various types of defects on electrocatalysts based on the latest progress in the defect engineering for nitrogen reduction reaction (NRR). In this review, the concept, classifications, and characterization of defects as well as the approaches to create them in electrocatalysts are firstly discussed. Then, certain types of defects (vacancy, dopant, amorphism, edge/corner, and porousness) affecting the performances of various electrocatalysts are further described. Finally, the summary and challenges of electrocatalytic ammonia synthesis are proposed to design advanced electrocatalysts with high efficiency.

Simultaneous Detection of Sculpture Defects Based on Vision and Positioning Method

The crack detection and effective maintenance of sculpture cultural relics have attracted more and more attention. However, for surface defects. Autonomous detection is mostly based on vision. The detection range of this type of method is limited to a common defect with a large crack width and easy identification. The conditions are too harsh. In reality, various types of defects usually appear, and they only occupy a small part of the inspection image. In addition, the difference between the parameters and the surrounding image parameters is small, which can easily lead to missed detection and false detection. In addition, most of the current researches only focus on defect detection. Little attention is paid to defect positioning, and this is the indispensable information for repairing and protecting sculptures. Part of the research proposed GPS positioning, but GPS signals are easily lost in a relatively complex geographic environment, and its infrastructure is not reliable and will increase Positioning costs. In this regard, this paper proposes a vision-based defect detection and positioning network method, which can be used in harsh conditions Detect, and locate defects, Which also set A supervised Deep Convolutional Neural Network is calculated. This paper also creates a training method to optimize its performance on the neural network. Experiments show the detection accuracy of this method is 80.7%, and the positioning accuracy of each image is 86% at 0.41 s (in the field. In the test experiment, it is 1200 pixels).