Position Of Defect Research Articles

Automatic Detection of Concrete Surface Defects Using PRE-TRAINED CNN and Laser Ultrasonic Visualization Testing

In recent years, nondestructive testing for civil engineering structures has become increasingly important. Ultrasonic testing is one of nondestructive inspection methods for civil structures. However, the inspection of civil engineering structures takes much time because of the extensive scope of the inspection. Moreover, in the field of nondestructive testing, there are also concerns about a future shortage of inspectors, so that an innovative effective nondestructive method needs to be developed. This study proposes an automatic defect detection approach using pre-trained convolutional neural network for laser ultrasonic visualization testing. The effectiveness of the proposed method is confirmed by applying it to a concrete structure with a surface defect. Grad-CAM demonstrates that the created CNN model in this study accurately predicts the position of a surface defect of concrete specimens.

The strength of an adhesive contact in the presence of interfacial defects

Adhesive contacts which possess a dominant stress concentration, such as at the contact edge in spherical junctions or at the detachment front in a peeling film, are well studied. More complex adhesive junction geometries, such as mushroom-shaped fibrils in bioinspired micropatterned dry adhesives, have exhibited a complex dependence of adhesive strength on the presence of interfacial defects within the contact. This has led to the emergence of statistical variation of the local behavior among micropatterned sub-contacts. In order to examine the interplay between geometry and interfacial defect character in control of the adhesive strength, the model system of a stiff cylindrical probe on an elastic layer is examined. Both experiments (glass on PDMS) and cohesive zone finite element simulations are performed, with analytical asymptotic limits also considered. The thickness of the elastic layer is varied to alter the interfacial stress distribution, with thinner layers having a reduced edge stress concentration at the expense of increased stress at the contact center. The size and position of manufactured interfacial defects is varied. It is observed that for the thickest substrates the edge stress concentration is dominant, with detachment propagating from this region regardless of the presence of an interfacial defect within the contact. Only very large center defects, with radius greater than half of that of the contact influence the adhesive strength. This transition is in agreement with analytical asymptotic limits. As the substrate is made thinner and the stress distribution changes, a strong decay in adhesive strength with increasing center defect radius emerges. For the thinnest substrate the flaw-insensitive upper bound is approached, suggesting that this decay is dominated by a reduction in the contact area. For penny-shaped defects at increasing radial positions, the adhesive strength for the thinnest substrates becomes non-monotonic. This confirms an intricate interplay between the geometry-controlled interfacial stress distribution and the size and position of interfacial defects in adhesive contacts, which will lead to statistical variation in strength when defects form due to surface roughness, fabrication imperfections, or contaminant particles.

Extended Trochanteric Osteotomy Does Not Compromise Functional and Radiographic Outcomes of Femoral Stem Revisions with the Use of an Uncemented Modular Conical Stem.

Background: Stem revisions in revision total hip arthroplasty (THA) with proximal bone stock loss may be dealt with utilizing modular, uncemented conical stems. During stem extraction, surgeons may resort to extended trochanteric osteotomy (ETO). However, ETO is associated with extensive blood loss and infections. This study compared the clinical outcomes, radiographic results and complications in THA revisions utilizing conical modular stem with and without ETO. Methods: Patients who underwent revision THA with or without ETO were assessed retrospectively. The minimal follow-up was 3 years. The functional evaluation included Harris Hip Score (HHS) and Short Form 36 (SF-36) as well as Western Ontario and McMaster Universities Arthritis Index (WOMAC) and Numerical Rating Scale for pain assessment. The radiographic evaluation comprised bone defect assessment, osteotomy healing, stem migration and position, presence of radiolucent lines and stress shielding. Results: In total, 73 patients (80 hips) were included in the final analysis. The ETO group comprised 48 hips, and the no-ETO group comprised 32 hips. In the ETO group, pre-operative WOMAC scores were lower than in the no-ETO group (p = 0.012). No significant differences were found in terms of post-operative HHS, WOMAC, and NRS scores between groups, except worse results were found in the case of claw plate implantation. Patients in the no-ETO group exhibited better results in SF-36 than in the ETO-group. Osteotomy non-union was observed in four hips (9.5%). Stam varus/valgus position was within ±1.5 degrees (85.9%). Conclusions: ETO does not adversely impact outcomes in patients undergoing femoral stem revisions with modular conical stems. The invasive nature of these procedures prompts careful consideration in each case individually.

Insight into insulation degradation mechanism of Al2O3 involved with positive and negative defects

Al2O3 with the desired electrical insulating properties and thermal shock resistivity has been extensively applied in the field of solid oxide fuel cells and oxygen sensors. However, degradation of the insulating Al2O3 layer is an intractable issue in practical applications. In this study, different point defect structures of Al2O3 were realized with the substitutional doping effect of ZrO2 and MgO. The MgO dopant provides positively charged oxygen vacancies, whereas the ZrO2 dopant tends to trigger negatively charged vacancy formation at Al3+ sites. The oxygen vacancy concentration of Al2O3 exhibits the following trend: MgO-doped Al2O3 > Al2O3 > ZrO2-doped Al2O3. Furthermore, the densification morphology, insulating properties, and oxygen vacancy migration of Al2O3 have been confirmed to be largely affected by the extrinsic factors. This study indicates that oxygen vacancy migration depends on the applied electric field at high temperatures. As the voltage and temperature increase, oxygen vacancy migration shows obvious electric-field-dependent characteristics, and its aggregation macroscopically shows hole defects. The defect position of Al2O3 is nonstoichiometric Al2O3-x with poor crystallinity. Therefore, it is believed that the oxygen vacancy migration triggered by the second phase directly determines the insulation performance and causes the degradation of Al2O3 materials.

Field strength prediction of 220 kV cable oil terminal defects based on multivariate nonlinear regression model

During the installation of a cable oil terminal, it is easy to leave scratches on the main insulation owing to uneven forces when removing the semi-conductive layer. Scratch defects cause field intensity distortion, which leads to partial discharge and insulation failure. This study attempts to establish a simulation model of a 220 kV cable terminal to determine the effect of the length, depth, and position of the scratch on the maximum field strength at the defect. The simulation and experiment demonstrate that the maximum field strength at the defect increases with greater length and decreases as the depth increases. Therefore, a prediction method for the terminal defect field strength based on a multivariate nonlinear regression model was proposed in this study. When the defect is located at 20 mm from the root of the stress cone, the maximum field strength is 14.5 MV/m when the length and depth are 2 mm and 1 mm, respectively. The maximum field strength at the defect was predicted based on the length, depth, and position of the scratch defect to evaluate the severity of the defect.

A composite testing method of homologous magnetic flux leakage and motion-induced eddy current for inner and outer defects

Magnetic flux leakage (MFL) testing, widely utilized for pipeline integrity assessments due to its efficiency and accuracy, is often challenged by motion-induced eddy currents (MIEC) that can distort MFL signals. Contrary to traditional perspectives that consider MIEC as detrimental, this paper introduces a novel MFL-MIEC composite testing method. This method leverages MIEC as a complementary technique while sharing the same excitation source. By exploring the generation and influence of MIEC on MFL signals, we simulate optimal probe positions and analyze the effects of defect position, angle, and detection speed on signal quality. Adjustments in the detection coils orientation are shown to enhance signal strength, demonstrating the methods effectiveness in identifying pipeline defects through practical experiments. This approach offers significant potential for widespread application in pipeline assessments.

Longitudinal Wave Defect Detection Technology Based on Ablation Mechanism

For laser ultrasound in the thermoelastic mechanism, excitation of ultrasonic body wave signal is weak, it is not easy to realize the detection of deep defects inside the workpiece. While the ablation mechanism produces a high and practical ultrasonic signal-to-noise ratio, this paper is based on the generation mechanism of laser ablation excitation of ultrasonic waves, the establishment of laser ultrasound in the ablation mechanism in the aluminum plate excitation and propagation of ultrasonic numerical model, through the solution, obtained the ultrasonic acoustic field map, discussed the ablation mechanism of the laser ultrasonic body wave acoustic field directionality. Additionally, the preliminary verification of the validity of the model is presented. Then, in order to explore the application potential of high signal-to-noise ratio longitudinal waves in defect detection, defects of different depths are preset in the model for simulation calculations, and waveform and acoustic field analyses are performed on the simulation results to study the ultrasonic propagation paths inside the member and the interaction with the defects, and the transverse position and depth of the internal defects are judged by using B-scan imaging. Finally, experimental validation is carried out. The experimental results are highly consistent with the simulation model, and the defect experiments can qualitatively determine the location of internal defects and verify the practicality and accuracy of the model.

Emission of Trapped Electrons from the 4H-SiC/SiO<sub>2</sub>-Interface via Photon-Irradiance at Cryogenic Temperatures

For a reliable MOSFET performance it is crucial to identify and reduce device performance limiting 4H-SiC/SiO2 interface defects. Previous studies have focused on the quantification of the interface states density, however, the atomic defect structure is still to be investigated. Here, we introduce a new approach based on opto-electrical measurements, which allow to determine the energetic position of interface defects. The measurement routine is performed at cryogenic temperatures to suppress the thermal emission of electrons, which were trapped at interface states during the cooldown processed (from 300 K to 15 K) while a positive gate voltage of 50 V was applied. At 15 K the photon-assisted emission of trapped electrons is measured in dependence on the photon’s energy (1.8 eV-3 eV). The reduction of the threshold voltage is taken as an indicator for the amount of released charges after each photon irradiation. We found an enhanced emission efficiency at certain photon energies, especially at 2.8 eV. Near-interface-traps (NITs), reported by Afanasev et al. are located close to the measuredtrap level with ENIT = EC, SiO2−2.77(5) eV. Recently, El-Sayed et al. suggested wide-angle O-Si-Obonds as defect configuration, that act as electron traps and have a similar energy as the measure traplevel.

Mechanical Behavior of Double-shell Structure with Different Positions of Inner Lining Defects

Mechanical Behavior of Double-shell Structure with Different Positions of Inner Lining Defects

A Multi-Strategy Hybrid Sparse Reconstruction Method Based on Spatial-Temporal Sparse Wave Number Analysis for Enhancing Pipe Ultrasonic-Guided Wave Anomaly Imaging.

Ultrasonic-guided waves (UGWs) in defective pipes are subject to severe coherent noise caused by imperfect detection conditions, mode conversion, and intrinsic characteristics (dispersion and multiple modes), inducing the limited performance of anomaly imaging. To achieve the high resolution and accuracy of anomaly imaging, a multi-strategy hybrid sparse reconstruction (MHSR) method based on spatial-temporal sparse wavenumber analysis (ST-SWA) is proposed. MHSR leverages the capability of ST-SWA to extract the wavenumber dispersion curves, thereby providing a more refined and precise search space for MHSR. Furthermore, it mitigates the impact of coherent noise by conducting dispersion compensation on the reconstructed signal. The sparse compensated signals through MHSR are employed for sparse reconstruction imaging. To validate the efficacy of the proposed method, UGW testing is performed on the defective steel pipe, and the results demonstrate the significant enhancement of anomaly imaging in defect resolution and positioning accuracy. The lowest estimated errors for axial and circumferential defect positions are 10 mm and 4 mm, respectively.

Two-dimensional migration characteristics of contaminant through composite liner with arbitrarily distributed defects

Previous studies indicate that liner defects can significantly accelerate contaminant migration in landfill, shortening the migration time by orders of magnitude. Although the effect of defects upon contaminant migration and barrier capacity of liner system has been investigated based on several theoretical models, they are always limited to one-dimensional scopes. Here a two-dimensional (2-D) model was established for contaminant transport through composite liner to the soil layer, with geomembrane (GM) defects arbitrarily distributed along the horizontal direction, and then Laplace transform, Fourier transform, and boundary transform method were utilized to derive its semi-analytical solution, which was verified by experimental data and numerical results. The obtained solution can take nonuniformly distributed defects into account and demonstrates the 2-D contaminant concentration and flux profiles in the spatiotemporal domain. Computational results of the solution are employed to illustrate the roles of material coefficients and modeling sizes upon the variations of contaminant concentration and flux. Results support that contaminant migration is expedited by the GM defect in landfill, and geosynthetics clay liner can reduce the pollution level caused by GM failure. Moreover, the contaminant flux will not be significantly influenced by the defect positions, which, however, leads to non-negligible impact on concentration profiles.

Phonon transport in vacancy induced defective stanene/hBN van der Waals heterostructure

In this study, Non-Equilibrium Molecular Dynamics (NEMD) simulation is employed to investigate the phonon thermal conductivity (PTC) of Sn/hBN van der Waals heterostructures with different vacancy-induced defects. We deliberately introduce three types of vacancies in Sn/hBN bilayer point vacancies, bivacancies, and edge vacancies at various concentrations ranging from 0.25% to 2%, to examine their effects on PTC across temperatures from 100 K to 600 K. The key findings of our work are (i) PTC declines monotonically with increasing vacancy concentration for all types of vacancies, with a maximum reduction of ∼62% observed at room temperature compared to its pristine form. (ii) The position of defects has an impact on PTC, with a larger decrease observed when defects are present in the hBN layer and a smaller decrease when defects are in the Sn layer. (iii) The type of vacancy also influences PTC, with point vacancies causing the most substantial reduction, followed by bivacancies, and edge vacancies having the least effect. A 2% defect concentration results in a ∼62% decrease in PTC for point vacancies, ∼51% for bivacancies, and ∼32% for edge vacancies. (iv) Finally, our results indicate that for a given defect concentration, PTC decreases as temperature increases. The impact of temperature on thermal conductivity is less pronounced compared to the effect of vacancies for the defective Sn/hBN bilayer. The presence of vacancies and elevated temperatures enhance phonon-defect and phonon–phonon scattering, leading to changes in the phonon density of states (PDOS) profile and the distribution of phonons across different frequencies of Sn/hBN bilayer, thus affecting its thermal conductivity. This work offers new insights into the thermal behavior of vacancy-filled Sn/hBN heterostructures, suggesting potential pathways for modulating thermal conductivity in bilayer van der Waals heterostructures for applications in thermoelectric, optoelectronics, and nanoelectronics in future.

Smart inspection of rail wheels with matrix arrays

Rail wheels are one of the most safety critical safety components of rolling stock. Any failure can lead to catastrophic events resulting in high material damage and personal injury or even fatalities. Therefore, international railway standards require non-destructive inspection by means of ultrasound testing (UT) on a regular basis. Due to increasing distances, loads and speeds there are increasing demands on the precision and the scope of testing. However, the inspection of train wheels is complicated due to the large variety of wheel geometries and the further influence of different milage. Moreover, extended inspection areas up to a full body inspection define many different defect positions, which are to some part difficult to access. This article describes industrial solutions, which allow the full-body inspection of train wheels including face, flange, web and tread for volumetric defects (from 2mm diameter) and surface cracks (from 5 mm length and 1 mm depth). In the latest upgrade, these systems can be now equipped with matrix probes. At the beginning of the article basic principles and general probe configurations will be discussed. The main part of this article is then dedicated to highlight the positive impact of the implementation of matrix probes. Besides a reduction of the form factor due to the decreased number of probes, matrix probes allow a direct coupling check thus fundamentally amending the existing state-of-the-art. Other advantages refer to improved settings for a higher reliability of the system. Finally, it is outlined in which way matrix probes provide additional functionalities to extend the current state-of-the-art towards a smart inspection.

Three-dimensional quantitative characterization of defects in inconel 625 superalloy based on deep learning image identification

Three-dimensional (3D) quantitative characterization of defects in superalloys is an important way to promote the ability of material design and service life prediction. In this work, 3D spatial distribution of defects for Inconel 625 superalloy manufactured by laser additive manufacturing (LAM) is carried out deep learning (DL) image identification technology and 3D image reconstruction. Firstly, computer tomography (CT) technology was used to obtain continuous slice images of sample. The U-net DL algorithm was applied to intelligently identify material defects in the continuous slices. On this basis, quantitative identification and analysis of spatial defect positions and typical sizes is achieved by using 3D reconstruction software. Compared with traditional threshold segmentation (TS) techniques, the defect recognition rate has significantly improved from 61.90 ​% to 95.00 ​%. This work provides a promising characterization method for efficient characterizing alloy defects and damage especially during material performance evaluation.

Accounting for defect position in ultrasonic fatigue test specimen with heterogeneous stress distribution

To account for or neglect the defect position, i.e. the fatigue initiating defect location, both radially and axially, is evaluated for hourglass-shaped ultrasonic fatigue specimen. The commonly used analytical equations to calculate the stress is compared against a finite element (FE) based approach, which is able to fully considering the stress state at the defect position. Notably, the effects on several common fatigue analyses are evaluated: the fatigue strength distribution, the stress–life and the stress–defect relationships. Fracture mechanical assessment is also performed, for a comprehensive VHCF characterization of the EN-GJS-500-7 ductile cast iron used in the study. The VHCF properties are characterized up to 3⋅108 cycles, using the Step-Stress fatigue testing method under fully reversed loading. The FE-model and Weibull distribution as the choice of fatigue strength distribution, enables size effect evaluation by Weakest-link effective volume with the highly stressed volume method as benchmark. The work shows that it is imperative to use the local stress state at the defect position, as the distribution of failures can diverge largely from the center of the specimen, and that neglecting this causes systematic error and flawed potentially results.

Acoustic emission source localization in complex pipe structure using multi-task deep learning models

Localizing defects in long-range pipelines is essential to reduce the inspection time and develop timely repair strategies. The acoustic emission (AE) method is employed to pinpoint the position of defects in pipelines. The conventional 1-D localization algorithm requires time of arrival differences between two sensors, which may not be accurately captured due to the dispersive nature of the pipe structures. The geometric variations such as elbows and welds can influence the propagating elastic waves and, consequently, arrival time. In this study, an AE source localization approach using a deep learning model is developed to tackle the influences of sensor-source distance and geometric variables. The multi-task learning model first identifies the impact of the elbow and subsequently integrates this information when predicting the source location. The proposed model is evaluated on a complex piping system, which features welded elbows in its connections. Incorporating the elbow effect into the model shows a notable improvement in overall accuracy, rising from 53% (conventional method) to 94% (proposed multi-task learning method).

Simulation study on breakdown evolution of tri-post insulator under electric-mechanical-thermal stresses in 1100 kV HVAC-GIL

Due to the presence of electric-mechanical-thermal stress, inner cracks and reduced adhesion strength between metal and epoxy can cause the breakdown of a tri-post insulator in the GIL. In this study, a simulation model was developed to investigate the breakdown evolution of the tri-post insulator subjected to combined electrical, mechanical, and thermal stresses. Various temperature gradients and defect positions were considered, along with the application of an AC voltage of 1100 kV. At room temperature, the tri-post insulator undergoes considerable residual internal stress of up to 35 MPa, primarily concentrated around the conductor and inserts, thereby accelerating the breakdown process. The temperature gradient across the insulator post has a notable impact on breakdown characteristics. Specifically, when the conductor temperature increases from 25 °C to 125 °C, the breakdown time of the insulator with the HV side defect increases by 83.1 %, while the damaged area reduces by 41.9 %. This is attributed to the release of the residual internal stress and the reduction of strain energy. When the defect is located at the GND side, the breakdown channel forms more rapidly, and the impact of temperature gradient on the breakdown process is weakened, because the tensile stress is high but the temperature is low around the GND inserts. The outcomes of this study are hoped to enhance understanding and enable the prediction of insulation breakdown behaviors in practical projects.

Explainable artificial intelligence prediction of defect characterization in composite materials

Non-destructive evaluation (NDE) techniques are integral across diverse applications for void detection within composites. Infrared (IR) thermography (IRT) is a prevalent NDE technique that utilizes reverse heat transfer principles to infer defect characteristics by analyzing temperature distribution. Although the forward heat transfer problem is well-posed, its inverse counterpart lacks uniqueness, posing non-unique solutions. The present study performs simulations using finite element analysis (FEA) in defective (a penny-shaped defect) composites through which the heat transfer flux is modeled. A total of 2100 simulations with various defect positions and sizes (depth, size, and thickness) are executed, and the corresponding surface temperature vs. time and vs. distance diagrams are extracted. The FEA outputs provide ample input data for developing an explainable artificial intelligence (XAI) model to estimate the defect characteristics. A detailed feature engineering task is performed to select the representative information from the diagrams. Explainable decision tree-based machine learning (ML) models with transparent decision paths based on derived features are developed to predict the defect depth, size, and thickness. The ML models’ results suggest superb accuracy (R2 = 0.92 to 0.99) across all three defect characteristics. The provided workflow sets a benchmark applicable to a range of fields, including health monitoring.

Investigation of the Defect Effects on the Load-Carrying Capacity of Butt Joints: A Numerical Study

Determining the behavior of joints under a specific load and estimating the damage potential is essential for ensuring the durability of joints in engineering structures. Defects might occur at the adhesive layer, which causes a reduction in the durability of joints. This work aims to examine the effects of the defect presence, defect volume fraction, defect position, and random distribution of defects at the adhesive layer on the durability of the butt joint. A finite element (FE) model of the butt joint was constructed using the commercially available FE software Abaqus/Standard. The validation of the FE model was conducted by comparing its results with experimental finding reported in existing literature. Numerical and experimental results showed strong agreement, with relative errors of 2.46% and 2.95% at peak force and displacement at peak force, respectively. Defect presence significantly influences the durability of the butt joint. Defect volume fraction and defect location are the dominant parameters affecting the durability of the butt joint. The square defects at the center of the bonding layer, with volume fractions of 0.05, 0.10, and 0.15, lowers the peak force by 5.08%, 10.56%, and 15.73%, respectively. When the defect is positioned at the center of the bonding layer, adhesive failure starts at the edges of the defects. However, relocating the defect from the center to the left or upper side of the bonding layer results in adhesive failure initiation at the corresponding edges of the adhesive. Random defect distribution in the adhesive layer doesn’t affect joint durability.

Tailoring Defects in B, N-Codoped Carbon Nanowalls for Direct Electrochemical Oxidation of Glyphosate and its Metabolites.

Tailoring the defects in graphene and its related carbon allotropes has great potential to exploit their enhanced electrochemical properties for energy applications, environmental remediation, and sensing. Vertical graphene, also known as carbon nanowalls (CNWs), exhibits a large surface area, enhanced charge transfer capability, and high defect density, making it suitable for a wide range of emerging applications. However, precise control and tuning of the defect size, position, and density remain challenging; moreover, due to their characteristic labyrinthine morphology, conventional characterization techniques and widely accepted quality indicators fail or need to be reformulated. This study primarily focuses on examining the impact of boron heterodoping and argon plasma treatment on CNW structures, uncovering complex interplays between specific defect-induced three-dimensional nanostructures and electrochemical performance. Moreover, the study introduces the use of defect-rich CNWs as a label-free electrode for directly oxidizing glyphosate (GLY), a common herbicide, and its metabolites (sarcosine and aminomethylphosphonic acid) for the first time. Crucially, we discovered that the presence of specific boron bonds (BC and BN), coupled with the absence of Lewis-base functional groups such as pyridinic-N, is essential for the oxidation of these analytes. Notably, the D+D* second-order combinational Raman modes at ≈2570 cm-1 emerged as a reliable indicator of the analytes' affinity. Contrary to expectations, the electrochemically active surface area and the presence of oxygen-containing functional groups played a secondary role. Argon-plasma post-treatment was found to adversely affect both the morphology and surface chemistry of CNWs, leading to an increase in sp3-hybridized carbon, the introduction of oxygen, and alterations in the types of nitrogen functional groups. Simulations support that certain defects are functional for GLY rather than AMPA. Sarcosine oxidation is the least affected by defect type.