Defect Shape Research Articles

Spermatozoa in mice lacking the nucleoporin NUP210L show defects in head shape and motility but not in nuclear compaction or histone replacement.

Biallelic loss-of-function mutation of NUP210L, encoding a testis-specific nucleoporin, has been reported in an infertile man whose spermatozoa show uncondensed heads and histone retention. Mice with a homozygous transgene intronic insertion in Nup210l were infertile but spermatozoa had condensed heads. Expression from this insertion allele is undefined, however, and residual NUP210L production could underlie the milder phenotype. To resolve this issue, we have created Nup210lem1Mjmm , a null allele of Nup210l, in the mouse. Nup210lem1Mjmm homozygotes show uniform mild anomalies of sperm head morphology and decreased motility, but nuclear compaction and histone removal appear unaffected. Thus, our mouse model does not support that NUP210L loss alone blocks spermatid nuclear compaction. Re-analyzing the patient's exome data, we identified a rare, potentially pathogenic, heterozygous variant in nucleoporin gene NUP153 (p.Pro485Leu), and showed that, in mouse and human, NUP210L and NUP153 colocalize at the caudal nuclear pole in elongating spermatids and spermatozoa. Unexpectedly, in round spermatids, NUP210L and NUP153 localisation differs between mouse (nucleoplasm) and human (nuclear periphery). Our data suggest two explanations for the increased phenotypic severity associated with NUP210L loss in human compared to mouse: a genetic variant in human NUP153 (p.Pro485Leu), and inter-species divergence in nuclear pore function in round spermatids.

High-Precision Corrosion Detection via SH1 Guided Wave Based on Full Waveform Inversion.

Corrosion detection for industrial settings is crucial for safe and efficient operations. Due to its high imaging resolution, the guided-wave full-waveform inversion tomography technique has significant potential for corrosion detection of plate metals. Limited by the long wavelengths of A0 and S0 mode waves, this method exhibits inadequate detection resolution for the earlier shallow and small corrosion defects. Based on the relatively short wavelength characteristics of the SH1 mode wave, we propose a high-precision corrosion detection method via SH1 guided wave using the full waveform inversion algorithms. By conducting finite element simulations of ultrasonic-guided waves on aluminum plates with varying corrosion defects, a comparison was made to assess the detection precision across A0, S0, and SH1 modes. The comparison results showed that, whether for regular or irregular defects, the SH1 mode wave always exhibited higher imaging accuracy than the A0 and S0 mode waves for shallow and small-sized defects. The corresponding experiments were conducted on an aluminum plate with simple or complex defects. The results of the experiments reconfirmed that the full waveform inversion method using SH1 guided wave can effectively reconstruct the shape and size of small and shallow corrosion defects within aluminum plates.

Quantitative test of delamination defects in CFRP with surface interference by laser thermography

Remote onsite inspection and quantitative evaluation of internal delamination defects in carbon fiber reinforced polymer (CFRP) structures is of great significance in practical engineering applications. In this paper, a new method for quantitative test and 3D imaging of internal delamination defects in CFRP with surface interference by long pulse laser thermography is developed. The variation of surface temperature field in a CFRP plate with both defect and surface interference irradiated by a uniform laser source was analyzed by simulation and experiment. Image processing of the collected laser thermography image sequence was carried out to obtain the size and shape of the possible defects. Meanwhile, a method with using the peak time of the temperature difference curve was developed to precisely evaluate the defect depth and eliminate the surface interference. The information was then combined to achieve 3D visualization imaging of the actual defects.

Modelling tamping effectiveness for track geometry longitudinal levelling defects

The geometric recovery achieved with tamping is affected by several factors, including maintenance history, ballast condition, type of tamping, age and configuration of track components, operational speeds, and human factors. In this paper, data from 2,819 defects of longitudinal levelling are analysed considering 66,892 km of inspections carried out on 19 lines of the Portuguese railway network over 10 years. The influence of infrastructure clusters, defect level, ballast age, degradation rate before tamping, and the still little-studied shape of defects on geometry recovery is firstly investigated by analysing relationships between the raw data (scatterplots). A multiple linear regression model is then developed to predict the defect level after tamping, taking into account both the defect level before tamping and its shape, which is modelled through a proxy parameter. It is concluded that track sections where there are singularities (underpasses, culverts, level crossings, bridges, tunnels and viaducts) usually have much shorter maintenance cycles (median values up to 60% lower). Ballast age and degradation rate before tamping did not prove to have a marked influence on geometry recovery. Although the defect level before tamping is found to be the factor that most conditions geometry recovery, thus corroborating the critical importance of timely intervention, its shape also proves to have a significant influence, giving rise to differences in geometry recovery of up to 20% of the defect level before tamping.

Investigation into the mechano-electrochemical interaction of internal and external corrosion defects on pipe surfaces

Revealing the mechanical-electrochemical interactions (M-E) on internal and external defects in pipelines is of paramount importance for ensuring the safe operation of pipelines. The present study establishes a mechano-electrochemical multi-physics coupling model and develops a two-dimensional finite element (FE) model of internal and external defects on the pipe surface to investigate the M-E interaction of X100 steel pipe with corrosion defects on both internal and external surfaces. The results demonstrate that the M-E interaction is significantly influenced by the geometrical shape, relative position, and internal pressure of defects. Moreover, altering a single defect parameter can simultaneously impact the M-E interaction of two defects. As the depth of defects increases, the cross-sectional area between internal and external defects decreases, leading to an intensified interaction between mechanics and electromagnetics and a gradual concentration of M-E interaction at the center of defects. The magnitude of the M-E interaction exhibits a negative correlation with the length of the defect, while the presence of a long defect minimally impacts the M-E interaction on the opposing side. As the external defects gradually distance themselves from the internal defects, the M-E interaction diminishes and the concentration area of M-E interaction shifts in the opposite direction to defect migration, while stress in the non-defect region adjacent to both internal and external defects progressively intensifies. The increase in internal pressure significantly enhances the M-E interaction of internal and external defects, with the M-E interaction primarily focused on the minimum cross-sectional area of radial overlap between these defects. At low internal pressures, however, the M-E interaction becomes negligible.

Вплив дефектів зварювання на втомні показники зразків зі сплаву ВТ-22 при застосуванні відновлювального наплавлення присадковими дротами

The restoration of the surface of damaged parts made of high-strength titanium alloys by the method of restorative surfacing with the use of filler wires is a quite reasonable and promising technique since its aims is to restore a partially damaged surface of parts while preserving their strength properties. However, this technique requires a significant number of static, fatigue and corrosion tests to be followed by suitable processing of the obtained results to correct or improve even the welding technology. The main purpose of the research was to study the fatigue behavior of samples with a restored surface by the method of metal surfacing. Fatigue tests were carried out on VT-22 alloy samples, the surface of which was restored by surfacing using SP-15 and VT-22 filler wires. Fractography revealed the presence of several welding defects (pores mainly) located both in the zone of the deposited metal and on its borders. These conclusions summarize the obtained research results and provide important information about the correlation between welding defects, fatigue strength, and the properties of the VT-22 alloy. The results showed that the presence of these defects is primarily related to the initiation of fatigue cracks. The size and shape of defects, as well as their location relative to the deposited layer, also affected the reduction of fatigue properties. Correcting the surfacing conditions may significantly reduce the critical size of defects and, as a result, significantly increase the residual life of restored parts. Keywords: high-strength titanium alloy VT-22, welding pores, segregation of defects, zone of thermal influence, cyclic loading.

Electromagnetic image recognition of a defect profile on a metal surface with a protective layer based on magnetic disturbance

In order to obtain defect information quickly and effectively and improve the accuracy and evaluation ability of traditional electromagnetic non-destructive testing (NDT), an electromagnetic image recognition method for the defect profile based on magnetic field disturbance is proposed in this paper. The excitation coil structure is designed, the excitation mode of the signal source is optimised and a three-dimensional electromagnetic transient analysis model is established for defect profile identification of a metal surface with an anti-corrosion protective layer. The research shows that the disturbed magnetic field Bz has the characteristics of high-resolution imaging and symmetry. The orientation of the defect on the surface has different effects on the clarity of image recognition. The larger the angle between the defect boundary and the induced current, the more complete and clear the image formed by the disturbed magnetic field Bz . A rectangular square wave is the best excitation signal for defect recognition. Its Bz image at t = 0 can present complete shape and position information about the defect. In addition, the excitation coil structure based on the principle of the disturbed magnetic field must provide a uniform induced current to produce a pronounced disturbed magnetic field. It is concluded that electromagnetic imaging technology based on the disturbed magnetic field Bz can better detect and characterise the shape of metal surface defects without damaging the metal protective layer and has good application potential for NDT and safety evaluation of in-service equipment.

Surface defects and shape defects of ceramic fibers from refractory oxides

The present article discusses ceramic fibers of refractory oxides used as the main components of heat-resistant refractory materials. Obviously, external defects affect the mechanical characteristics of refractory oxide ceramic fibers. In this regard, using optical and electron microscopy, the morphology of the surface of ceramic fibers was investigated, defects were classified, analysis was carried out, the causes of their occurrence were established and methods for their elimination were proposed.

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

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

Real-time defect detection of saw chains on automatic assembly lines based on residual networks and knowledge coding

The use of machine vision methods has rapidly developed for detecting product defects, but it remains challenging to identify shape and color defects as well as the assembly relationship of parts due to occlusion between components and changes in assembly structures. This paper proposed a method for detecting saw chain assembly defects based on residual networks and knowledge coding to achieve parts segmentation and identification of assembly defects. Firstly, we design an adaptive segmentation algorithm for saw chain parts, which uses the Hough transform to locate rivets and completes the image segmentation based on the inherent assembly features between parts. Secondly, A trained ResNet (Residual Network) based deep learning model was used to identify the individual defects of saw chain parts, in which the transfer learning method was used to improve the detection speed of individual defects. Finally, a parallel inference method based on a knowledge encoding matrix is proposed for detecting part defects and constructing a sparse matrix to locate assembly defects. Evaluating the proposed method in building a saw chain testing platform. Results show that the proposed method can achieve real-time and high-precision detection requirements in different application scenarios. The detection accuracy on individual and assembly defects reaches 94.2% and 89.2%, respectively, which is better than the current state-of-the-art object detection models. Assembly defect detection methods are an important reference for inspecting other types of simple assemblies in the industry.

An image fusion-based method for recovering the 3D shape of roll surface defects

Most of the existing studies on roll surface defects focus on qualitative detection and lack quantitative analysis, while the commonly used methods for detecting the three-dimensional shape of small objects such as defects are the stylus method, laser scanning method, and structured light scanning method, but these methods are difficult to accurately measure the complex defect variations on the roll surface. In this paper, we propose a method for recovering the 3D shape of roll surface defects based on image fusion. The traditional 3D reconstruction problem is transformed into a 2D image fusion problem using a focusing method. The non-subsampled shear wave transform is used as the base algorithm for image fusion, combined with an enhanced fusion strategy called modified multi-state pulse-coupled neural network to obtain a fully focused image. The method achieves 3D shape recovery of defects by modeling the relationship between the defect depth, the fully focused image, and the original image. To evaluate the performance of the method, experiments were carried out using data involving craters and scratches on the roll surface. This method significantly improves the quality of defect detection images, with a 98% better gradient and a 28% increase in overall image quality. Additionally, it keeps 3D reconstruction errors under 4%, ensuring high accuracy and noise resistance.

Analysis of effects of material anisotropy on ductile damage using microscopic unit‐cell model

AbstractIt is experimentally observed that the failure in ductile metals is mainly due to the nucleation, growth, and coalescence of micro‐voids as well as micro‐shear‐cracks. Furthermore, plastic anisotropy has significant role in damage and failure behavior of ductile metals. Finite element simulations of unit cell provide a basis to understand different mechanisms on micro‐scale, for example, changes in shape and size of single voids and defects as well as localization of plastic strains. This contribution deals with the numerical analysis of unit cell containing spherical void subjected to symmetrical boundary conditions taking material anisotropy into account. Elastic isotropic behavior is described by Hooke's law while Hoffman yield criterion considering the strength‐differential effect is used to model the anisotropic plastic behavior. Generalized anisotropic stress invariants, generalized stress triaxiality, and generalized Lode parameter are introduced to characterize the stress state in the anisotropic ductile metal. The effect of plastic anisotropy on the damage behavior of the aluminum alloy EN AW‐2017A is studied in detail by performing a series of numerical simulations covering a wide range of stress triaxialites and Lode parameters. Stress triaxility and Lode parameter are controlled and kept constant during the entire loading process. The numerical results are then used to discuss general mechanisms of damage and failure process in ductile metals.

Cascaded adaptive global localisation network for steel defect detection

Defect detection is crucial in ensuring the quality of steel products. This paper proposes a novel deep neural network, cascaded adaptive global location network (CAGLNet), for detecting steel surface defects. The main objective of this study is to address the challenges associated with the irregular shape and dense spatial distribution of defects on steel. To achieve this goal, CAGLNet integrates a feature extraction network that combines residual and feature pyramid networks, a cascade adaptive tree-structure region proposal network (CAT-RPN) that eliminates the need for prior knowledge, and a global localisation regression for steel defect detection. This paper evaluates the effectiveness of CAGLNet on the NEU-DET dataset and demonstrates that the proposed model achieves an average accuracy of 85.40% with a fast frames per second of 10.06, outperforming those state-of-the-art methods. These results suggest that CAGLNet has the potential to significantly improve the effectiveness of defect detection in industrial production processes, leading to increased production yield and cost savings. Abbreviations: AT-RPN, adaptive tree-structure region proposal network; CAGLNet, cascaded adaptive global location network; CAT-RPN, cascade adaptive tree-structure region proposal network; CNN, convolutional neural network; DNN, deep neural network; EPNet, edge proposal network; FPN, feature pyramid network; FCOS, fully convolutional one-stage detector; FPS, frames per second; GMM, Gaussian mixture model; IoU, intersection-over-union; ROIAlign, region of interest align; RPN, region proposal network; ResNet, residual network; ResNet50_FPN, residual network and feature pyramid network; SABL, side aware boundary localisation; SSD, single-shot multiBox detector; TPE, Tree-structured Parzen estimator

Baseline-Free Damage Imaging of Composite Lap Joint via Parallel Array of Piezoelectric Sensors.

This paper presents a baseline-free damage imaging technique using a parallel array of piezoelectric sensors and a control board that facilitates custom combinations of sensor selection. This technique incorporates an imaging algorithm that uses parallel beams for generation and reception of ultrasonic guided waves in a pitch-catch configuration. A baseline-free reconstruction algorithm for probabilistic inspection of defects (RAPID) algorithm is adopted. The proposed RAPID method replaces the conventional approach of using signal difference coefficients with the maximum signal envelope as a damage index, ensuring independence from baseline data. Additionally, conversely to the conventional RAPID algorithm which uses all possible sensor combinations, an innovative selection of combinations is proposed to mitigate attenuation effects. The proposed method is designed for the inspection of lap joints. Experimental measurements were carried out on a composite lap joint, which featured two dissimilar-sized disbonds positioned at the lap joint's borderline. A 2D correlation coefficient was used to quantitatively determine the similarity between the obtained images and a reference image with correct defect shapes and locations. The results demonstrate the effectiveness of the proposed damage imaging method in detecting both defects. Additionally, parametric studies were conducted to illustrate how various parameters influence the accuracy of the obtained imaging results.

Study on fatigue performance of steel bridge welded joints considering initial defects

Orthotropic steel bridge decks (OSBDs) are extensively utilized in both medium-span and long-span bridges due to their lightweight and high strength properties. The current predominant method for connecting steel bridge decks is welding. However, the discontinuous geometric shape and inevitable welding defects at the welded joints are prone to stress concentration. This leads to fatigue cracks in steel bridge decks predominantly occuring at locations where defects are present within the welded joints. This study analyzes the fatigue performance and crack propagation of the rib-to-deck connection details considering the influence of initial defects. The experimental results of the T-joint revealed that the undercuts exerted a substantial influence on the fatigue performance, whereas partial penetration had a relatively small impact. The S–N curve of the specimen with undercuts can be derived by reducing 4 standard deviations from the S–N curve without initial defect. Alternatively, the fatigue strength of the specimen without initial defects can be divided by a safety factor of 1.4. The Paris formula was employed to compute pertinent parameters for crack propagation, followed by the utilization of the finite element method to simulate the process of crack propagation within the rib-to-deck details. The feasibility of this approach was confirmed by the simulation results. Furthermore, this study discussed the impact of initial crack size parameters on crack propagation mode and fatigue performance, providing valuable insights into the mechanisms underlying the fatigue crack propagation within welded joints of steel bridges.

Fracture failure of internal thread joint on back redressing stabilizer

Stabilizers have been one of the most critical components in the drilling string, due to their great function to prevent the unintentional sidetracking and vibrations in whole drilling assembly. In recent years, with the increasing of the well depth, the rising of the harsh service conditions have pose big challenges to the stabilizers. So it is very necessary to conduct systematic research on the failure mechanism of the stabilizer and carry out ideal failure prevention measures. In this article, the fracture failure analysis of an internal thread joint of the back redressing stabilizer was carried out, and the failure causes were investigated by nondestructive testing, mechanical property tests, chemical composition analysis, metallographic analysis and scanning electron microscope analysis. The results show that the mechanical tests and chemical composition analysis results all meet the requirements of the SY/T 5051-2016 standard, the fracture mode of the back redressing stabilizer is fatigue fracture, and the causes of the failure is mainly belong to the pit shape defects on the surface of internal thread and the quenching crack in the internal thread.

Understanding the hot isostatic pressing effectiveness of laser powder bed fusion Ti-6Al-4V by in-situ X-ray imaging and diffraction experiments

In the present study, in-situ observation of Hot Isostatic Pressure (HIP) procedure of laser powder bed fusion manufactured Ti-6Al-4V parts was performed to quantitatively estimate the densification rate of the material and the influence of the defect initial size and shape on such rate. The observations were performed in-situ using the Ultrafast Tomography Paris-Edinburgh Cell and the combination of fast phase-contrast synchrotron X-ray tomography and energy dispersive diffraction. With this strategy, we could quantify how the effectiveness of HIP depends on the characteristics of a defect. Smaller defects showed a higher densification rate, while the defect shape did not have significant effect on such rate.

SGN-YOLO: Detecting Wood Defects with Improved YOLOv5 Based on Semi-Global Network

Object detection based on wood defects involves using bounding boxes to label defects in the surface image of the wood. This step is crucial before the transformation of wood products. Due to the small size and diverse shape of wood defects, most previous object detection models are unable to filter out critical features effectively. Consequently, they have faced challenges in generating adequate contextual information to detect defects accurately. In this paper, we proposed a YOLOv5 model based on a Semi-Global Network (SGN) to detect wood defects. Unlike previous models, firstly, a lightweight SGN is introduced in the backbone to model the global context, which can improve the accuracy and reduce the complexity of the network at the same time; the backbone is embedded with the Extended Efficient Layer Aggregation Network (E-ELAN), which continuously enhances the learning ability of the network; and finally, the Efficient Intersection and Merger (EIOU) loss is used to solve the problems of slow convergence speed and inaccurate regression results. Experimental results on public wood defect datasets demonstrated that our approach outperformed existing target detection models. The mAP value was 86.4%, a 3.1% improvement over the baseline network model, a 7.1% improvement over SSD, and a 13.6% improvement over Faster R-CNN. These results show the effectiveness of our proposed methodology.

Cranial Neural Crest Specific Deletion of Alpl (TNAP) via P0-Cre Causes Abnormal Chondrocyte Maturation and Deficient Cranial Base Growth.

Bone growth plate abnormalities and skull shape defects are seen in hypophosphatasia, a heritable disorder in humans that occurs due to the deficiency of tissue nonspecific alkaline phosphatase (TNAP, Alpl) enzyme activity. The abnormal development of the cranial base growth plates (synchondroses) and abnormal skull shapes have also been demonstrated in global Alpl-/- mice. To distinguish local vs. systemic effects of TNAP on skull development, we utilized P0-Cre to knockout Alpl only in cranial neural crest-derived tissues using Alpl flox mice. Here, we show that Alpl deficiency using P0-Cre in cranial neural crest leads to skull shape defects and the deficient growth of the intersphenoid synchondrosis (ISS). ISS chondrocyte abnormalities included increased proliferation in resting and proliferative zones with decreased apoptosis in hypertrophic zones. ColX expression was increased, which is indicative of premature differentiation in the absence of Alpl. Sox9 expression was increased in both the resting and prehypertrophic zones of mutant mice. The expression of Parathyroid hormone related protein (PTHrP) and Indian hedgehog homolog (IHH) were also increased. Finally, cranial base organ culture revealed that inorganic phosphate (Pi) and pyrophosphate (PPi) have specific effects on cell signaling and phenotype changes in the ISS. Together, these results demonstrate that the TNAP expression downstream of Alpl in growth plate chondrocytes is essential for normal development, and that the mechanism likely involves Sox9, PTHrP, IHH and PPi.

Optimization of Fixations for Additively Manufactured Cranial Implants: Insights from Finite Element Analysis.

With the emergence of additive manufacturing technology, patient-specific cranial implants using 3D printing have massively influenced the field. These implants offer improved surgical outcomes and aesthetic preservation. However, as additive manufacturing in cranial implants is still emerging, ongoing research is investigating their reliability and sustainability. The long-term biomechanical performance of these implants is critically influenced by factors such as implant material, anticipated loads, implant-skull interface geometry, and structural constraints, among others. The efficacy of cranial implants involves an intricate interplay of these factors, with fixation playing a pivotal role. This study addresses two critical concerns: determining the ideal number of fixation points for cranial implants and the optimal curvilinear distance between those points, thereby establishing a minimum threshold. Employing finite element analysis, the research incorporates variables such as implant shapes, sizes, materials, the number of fixation points, and their relative positions. The study reveals that the optimal number of fixation points ranges from four to five, accounting for defect size and shape. Moreover, the optimal curvilinear distance between two screws is approximately 40 mm for smaller implants and 60 mm for larger implants. Optimal fixation placement away from the center mitigates higher deflection due to overhangs. Notably, a symmetric screw orientation reduces deflection, enhancing implant stability. The findings offer crucial insights into optimizing fixation strategies for cranial implants, thereby aiding surgical decision-making guidelines.