Distribution Of Defects Research Articles

Analytical modeling of energy-based matrix crack growth and fiber breakage in unidirectional ceramic matrix composites under static tensile behavior in the longitudinal direction

An analytical model was developed to characterize the material behavior of ceramic matrix composites (CMCs). The model incorporates energy-based matrix crack growth, accounting for thermal residual and debonding stresses. Matrix crack growth was analyzed using the Monte Carlo method to represent the random nature of crack growth, which considers the distribution of defects within the material. Additionally, a periodic matrix crack growth model was examined. The results from the model, enhanced with correction factors, showed strong agreement with those obtained from Monte Carlo simulations. Furthermore, the global load sharing (GLS) model that includes the probability of matrix cracks propagating into the fibers was proposed and integrated with the periodic matrix crack growth model. This combined model accurately expressed the stress–strain and crack density–stress relationships up to the point of fiber breakage. The study also examined the propagation of matrix cracks into the fibers in relation to debonding stress and energy, leading to the successful validation of the proposed model.

A study on CT detection image generation based on decompound synthesize method

Background Nuclear graphite and carbon components are vital structural elements in the cores of high-temperature gas-cooled reactors(HTGR), serving crucial roles in neutron reflection, moderation, and insulation. The structural integrity and stable operation of these reactors heavily depend on the quality of these components. Helical Computed Tomography (CT) technology provides a method for detecting and intelligently identifying defects within these structures. However, the scarcity of defect datasets limits the performance of deep learning-based detection algorithms due to small sample sizes and class imbalance. Objective Given the limited number of actual CT reconstruction images of components and the sparse distribution of defects, this study aims to address the challenges of small sample sizes and class imbalance in defect detection model training by generating approximate CT reconstruction images to augment the defect detection training dataset. Methods We propose a novel CT detection image generation algorithm called the Decompound Synthesize Method (DSM), which decomposes the image generation process into three steps: model conversion, background generation, and defect synthesis. First, STL files of various industrial components are converted into voxel data, which undergo forward projection and image reconstruction to obtain corresponding CT images. Next, the Contour-CycleGAN model is employed to generate synthetic images that closely resemble actual CT images. Finally, defects are randomly sampled from an existing defect library and added to the images using the Copy-Adjust-Paste (CAP) method. These steps significantly expand the training dataset with images that closely mimic actual CT reconstructions. Results Experimental results validate the effectiveness of the proposed image generation method in defect detection tasks. Datasets generated using DSM exhibit greater similarity to actual CT images, and when combined with original data for training, these datasets enhance defect detection accuracy compared to using only the original images. Conclusion The DSM shows promise in addressing the challenges of small sample sizes and class imbalance. Future research can focus on further optimizing the generation algorithm and refining the model structure to enhance the performance and accuracy of defect detection models.

Decision Making in the Searching Weakness Process of Server Side Web Application.

The vulnerability search process consists of three stages. At the first stage, the weakness of software and firmware components is identified and their suitability for exploitation. The second stage is devoted to choosing an existing or developing a new exploit for the identified weakness. The third stage configures the usability and reliability of the exploit for the identified vulnerability. The vulnerability of the system of the cyber protection object to negative technical impact is always based on the exploitation of its defects. Weakness identification with applicability assessment is a multi-stage process of choosing solutions under conditions of uncertainty. The efficiency of the weakness search process can be improved by using decision making theory. The idea is to reduce uncertainty by using an information model to search for weakness and to make it possible to process it quantitative analysis. For quantitative analysis, classical criteria for choosing solutions under uncertainty conditions were used. The information model is a hierarchy, has four levels and includes four types of elements. First level consists a techniques of Initial Access. First level consists a techniques of Initial Access, second – of category wekness Web Application. The third and fourth levels of assessment, respectively, of weaknesses and vulnerabilities. The expediency, rationality and reasonableness of solutions for finding defects is based on the application of proven sources of world-class expert experience. Thus, Adversarial Tactics, Techniques & Common Knowledge (ATT&CK) defines Server-side Web Application class systems as an attractive attack target for hackers with the highest average number of potential defects in at least three components: Web Server, Web Application Server, DBMS Server. To find out the distribution of Server-side Web Application defects by characteristic classes, it is necessary to use the Open Worldwide Application Security Project (OWASP), for a detailed study of them - Common Weakness Enumeration (CWE). The application of the National Vulnerability Database (NVD) and Common Vulnerabilities and Exposures (CVE) complements the evaluation of the found defect.

Luminescence properties of zircon single crystals doped with europium and vanadium

Crystals of ZrSiO4; ZrSiO4: Eu3+; and ZrSiO4: (Eu3++V) have been grown by the flux method. Their structural and luminescent properties have been studied demonstrating heterogeneity in cathodoluminescence (CL) for all samples. Such a heterogeneity of CL in ZrSiO4; ZrSiO4: Eu3+ is related to irregular distribution of point defects associated presumably with oxygen vacancies. Uneven distribution of vacancies as assumed led to the formation of different sites for europium ion. Moreover, in different areas of the crystal matrix the number of sites and their types vary. Activation of zircon with europium and vanadium simultaneously caused an increase of europium incorporation in comparison with zircon doped with europium only. It was found that heterogeneity of CL properties of ZrSiO4: (Eu3++V) is related to the irregular distribution of vanadium in crystal matrix. Polarization studies of CL spectra suggest that there are at least four europium sites in ZrSiO4: (Eu3++V).

Identification and characteristic analysis of internal defects in rock-filled concrete based on deep learning method

The distribution characteristics and geometric morphology characteristics of defects within RFC are important factors affecting the strength properties and rupture morphology of RFC. However, the excessive size of commonly used aggregates for RFC leads to difficulties in conducting in-depth experimental studies indoors. Based on the improved U-Net and image processing technology, this research establishes an integrated model for the identification, classification, and extraction of defects inside the RFC, quantitatively counts and analyzes the acquired defect distribution characteristics and geometrical morphology characteristics, and establishes a defect characteristic distribution function that can be used for the numerical reconstruction of defects. In order to realize the acceleration of U-Net training using training weights, use VGG-16 with the fully connected layer removed instead of the Encoder part of the U-Net. The integrated model in this research can realize automatic identification, classification, and extraction of multiple types of defects at the same time, and the established distribution function of defect characteristics provides a data basis and new ideas for the establishment of RFC three-dimensional numerical models containing real defects.

Inclusion-Based Model: Calculating Tooth Root Bending Strength Considering Steel Cleanliness

Current gear design guidelines and standards have given little or no consideration to the increase in strength that can be achieved by using ultra-clean steels. In order to fully exploit the potential of ultra-clean steels, it is therefore necessary to use higher-quality calculation methods that combine FEM stress calculations with local strength calculations. Therefore, the aim of this paper is to extend the inclusion-based model to allow for the calculation of the tooth root bending strength of gears with different steel cleanliness. For this purpose, a material analysis of 20MnCr5 with three different degrees of cleanliness is carried out and the respective material defect distribution is determined. In order to be able to represent the determined material defect distributions of the different cleanliness grades in the inclusion-based model, the model is extended accordingly, and a sensitivity analysis is carried out on the influence of the material defect distribution functions on the tooth root bending strength calculation. Finally, the model is applied to a pulsator test to verify the applicability of the model. The results of the verification show that the calculation of the mean bending strength of gears in pulsator investigations is generally possible with the extended inclusion-based model. The inclusion-based model thus offers the potential to improve the statistical significance of pulsator test results by supplementing the limited number of practical test points with the virtual test points of the inclusion-based model.

Local stress/strain field analysis of die-casting Al alloys via 3D model simulation with realistic defect distribution and RVE modelling

The deformation and fracture behavior of die-casting aluminium (Al) alloys is very complex. Due to local variations in properties, the microstructure and mechanical behavior of materials are highly anisotropic. In this paper, an attempt has been made to quantitatively study the defect characteristics of high pressure cast Al alloy parts using experimental and finite element calculation methods, and to analysis the effect of local porosity and pore size on plasticity. Three-dimensional solids with real defect distribution are obtained by using 3D X-ray computed tomography, and used as an input for building a finite element model. The damage initiation of cast Al alloys under complex stress states is analyzed from micro to macro scales. Crack propagation occurs through two modes of microporous agglomeration: aggregated pores produce cracks from internal necking and stress concentration. Then, they expand in the same direction, agglomerate in a specific direction and eventually fracture. Subsequently, the effect of porosity on non-homogeneity was elucidated by obtaining local stress/strain behavior through digital image correlation measurements. Additionally, a theoretical framework of elasto-plastic deformation of microstructures and a 3D representative volume element model are developed to simulate the deformation and damage processes under cyclic boundary conditions of materials. The simulation results show that the local stress/strain around the pores evolves gradually with deformation. During the die casting process, the method demonstrates the ability to predict the mechanical behavior of Al alloys.

Defective regression models for cure rate data with competing risks

ABSTRACT In this paper, we propose a novel method for the analysis of cure rate data with competing risks using defective distributions. We develop two defective regression models for the analysis of competing risk data subjected to random right censoring. The proposed models enable us to estimate the cure fraction directly from the model. Simultaneously, we also estimate the regression parameters corresponding to each cause of failure using the method of maximum likelihood. We conduct a simulation study to evaluate the finite sample performance of the proposed estimators. The practical usefulness of the procedures is illustrated using two real-life data sets.

Impact of submicron Nb3Sn stoichiometric surface defects on high-field superconducting radiofrequency cavity performance

Nb3Sn film coatings have the potential to drastically improve the accelerating performance of Nb superconducting radiofrequency (SRF) cavities in next-generation linear particle accelerators. Unfortunately, persistent Nb3Sn stoichiometric material defects formed during fabrication limit the cryogenic operating temperature and accelerating gradient by nucleating magnetic vortices that lead to premature cavity quenching. The SRF community currently lacks a predictive model that can explain the impact of chemical and morphological properties of Nb3Sn defects on vortex nucleation and maximum accelerating gradients. Both experimental and theoretical studies of the material and superconducting properties of the first 100 nm of Nb3Sn surfaces are complicated by significant variations in the volume distribution and topography of stoichiometric defects. This work contains a coordinated experimental study with supporting simulations to identify how the observed chemical composition and morphology of certain Sn-rich and Sn-deficient surface defects can impact the SRF performance. Nb3Sn films were prepared with varying degrees of stoichiometric defects, and the film surface morphologies were characterized. Both Sn-rich and Sn-deficient regions were identified in these samples. For Sn-rich defects, we focus on elemental Sn islands that are partially embedded into the Nb3Sn film. Using finite element simulations of the time-dependent Ginzburg-Landau equations, we estimate vortex nucleation field thresholds at Sn islands of varying size, geometry, and embedment. We find that these islands can lead to significant SRF performance degradation that could not have been predicted from the ensemble stoichiometry alone. For Sn-deficient Nb3Sn surfaces, we experimentally identify a periodic nanoscale surface corrugation that likely forms because of extensive Sn loss from the surface. Simulation results show that the surface corrugations contribute to the already substantial drop in the vortex nucleation field of Sn-deficient Nb3Sn surfaces. This work provides a systematic approach for future studies to further detail the relationship between experimental Nb3Sn growth conditions, stoichiometric defects, geometry, and vortex nucleation. These findings have technical implications that will help guide improvements to Nb3Sn fabrication procedures. Our outlined experiment-informed theoretical methods can assist future studies in making additional key insights about Nb3Sn stoichiometric defects that will help build the next generation of SRF cavities and support related superconducting materials development efforts. Published by the American Physical Society 2024

Predictive model of spatial nematic order in confined cell populations

Two-dimensional tissues made of spindle-shaped cells have many applications in the fields of tissue engineering and biotechnology. The uniformity of the tissues is critically affected by topological defects, which are singular points of cell alignment. For systematic control and analysis of defect distributions, this paper proposes a numerical method to predict and quantify spatial distributions of defects in two-dimensional domains. In the proposed method, spindle-shaped cells are modeled as nematic liquid crystals, whose alignment and Frank elastic energy are explicitly expressed. The equilibrium distributions of the defects can then be calculated using a Markov chain Monte Carlo method. The proposed method was experimentally verified by culturing mouse myoblast (C2C12) cells on microwells. The order of the defect scattering was almost the same as for the proposed estimation method, indicating that the proposed method can be used for the systematic design of topographical guides for controlling defect distributions.

Research on frozen sand mold casting technology for complex thin-walled aluminum alloy castings

With the growing emphasis on environmental sustainability and the modernization of the manufacturing sector, the pursuit of eco-friendly practices within the foundry industry has become a critical objective. This paper proposes an innovative solution for environmentally friendly casting: the digital frozen sand mold casting technology. To evaluate its effectiveness, this study analyzed complex thin-walled grid rudder aluminum alloy castings. A comparative analysis was conducted between the binder jetting 3D printing and frozen sand mold (sand mold milling) casting processes. By utilizing Anycasting software, the research explored the filling and solidification processes of ZL101 grid rudder castings under varying mold conditions, aiming to predict the distribution and severity of shrinkage defects. Validation of this approach indicated a machining error of ±0.25 mm for frozen sand molds. The dimensional accuracy of the grid rudder castings achieved a casting tolerance grade of 9 (CT9), and the direct recovery rate of used sand exceeded 98 %, thus positioning this method as a viable alternative to 3D-printed resin sand. Additionally, the study assessed the economic feasibility of frozen sand mold casting technology in terms of resource consumption and production efficiency. The research findings hold significant relevance, offering valuable insights and guidance for traditional casting enterprises seeking to adopt sustainable development practices through the application of leveraging frozen sand casting technology.

Role of mechanical stress localizations on the radiation hardness of AlGaN/GaN high electron mobility transistors

Abstract Multi-material, multi-layered systems such as AlGaN/GaN high electron mobility transistors (HEMTs) contain residual mechanical stresses that arise from sharp contrasts in device geometry and materials parameters. These stresses, which can be either tensile or compressive, are difficult to detect and eliminate because of their highly localized nature. We propose that their high-stored internal energy makes potential sites for defect nucleation sites under radiation, particularly if their locations coincide with the electrically sensitive regions of a transistor. In this study, we validate this hypothesis with molecular dynamic simulation and experiments exposing both pristine and annealed HEMTS to 2.8 MeV Au+3 irradiation. Our unique annealing process uses mechanical momentum of electrons, also known as the electron wind force (EWF) to mitigate the residual stress at room temperature. High-resolution transmission electron microscopy and cathodoluminescence spectra reveal the reduction of point defects and dislocations near the two-dimensional electron gas region of EWF-treated devices compared to pristine devices. The EWF-treated HEMTs showed relatively higher resilience with approximately 10% less degradation of drain saturation current and ON-resistance and 5% less degradation of peak transconductance. Both mobility and carrier concentration of the EWF-treated devices were less impacted compared to the pristine devices. Our results suggest that the lower density of nanoscale stress localization contributed to the improved radiation tolerance of the EWF-treated devices. Intriguingly, the EWF is found to modulate the defect distribution by moving the defects to electrically less sensitive regions in the form of dislocation networks, which act as sinks for the radiation induced defects and this assisted faster dynamic annealing.

Fast prediction of irradiation-induced cascade defects using denoising diffusion probabilistic model

Irradiation-induced cascade collisions produce numerous point defects within materials, which can severely deteriorate their thermo-mechanical properties and overall performance. We propose a computational scheme that combines molecular dynamic (MD) simulations with a denoising diffusion probabilistic model (DDPM) to rapidly and accurately predict the spatial coordinates of point defects at any given primary knock atom (PKA) energy, ranging from 0 to 100.0 keV. Importantly, this capability extends to PKA energies that are exclusive from the training data set, demonstrating the robustness and generalizability of the model. The proposed scheme has been thoroughly validated by several designed indicators, including the Fréchet inception distance, the number of point defects, the distance from vacancies and self-interstitial atoms (SIAs) to their respective centroids, the inter-centroid distance between the vacancies and SIAs, the probability density of clustered defect sizes, and the sub-cascade number. Compared to MD simulations, the DDPM can generate point defects at a specific PKA energy at least ten thousand times faster. By offering a rapid and reliable means to model defect distributions across various energy levels, the proposed scheme benefits the comprehension of the cascade process and provides a valuable database for both experimental investigations and large-scale simulations.

Effect of multi‐angle lamination on mechanical properties and damages behavior of carbon fiber reinforced resin matrix composites

AbstractCarbon fiber reinforced resin matrix composites, when subjected to multi‐angle delamination during vacuum hot pressing, suffered from defects such as uneven fiber distribution, resin‐rich zones, and voids. These defects caused varied damage and failure behaviors in the composites, seriously affecting their mechanical properties. In this paper, the defect distribution, tensile and flexural properties, and damage behaviors of multi‐angle lay‐up specimens prepared in an autoclave were studied. The results indicated that irregularly shaped voids in laminates with a 0° angle difference primarily led to local stress concentration. Elliptical and elongated voids in laminates with a 90° angle difference were mainly susceptible to delamination. The [0/90]8 layer exhibited the best overall mechanical properties. The proportion of 0° laminates had a direct impact on the mechanical properties, with 0° contact laminates on the bending‐compression side capable of withstanding higher bending loads. The [+45/−45]8 plyer demonstrated pseudo‐delayed behavior, enabling the laminate to show better toughness under load. Variations in damage due to the proportions and positions of the laminates influenced the mechanical properties and damages behavior of multi‐angle laminates.Highlights Angular differences were observed to affect void and resin distribution. The best overall mechanical properties were attributed to [0/90]8 laminates. Pseudo‐delay behavior was exhibited by [+45/−45]8 laminates. Proportion of 0° layers impacted mechanical properties. Greater bending loads were withstood by the 0° contact layer.

Effect of Microstructure on Chemo-Mechanical Damage Evolution in Aluminum Foil Anodes for Lithium-Ion Batteries

Aluminum-based foil anodes could enable lithium-ion batteries with high energy density comparable to silicon and lithium metal. However, mechanical pulverization and lithium trapping within aluminum tend to cause capacity fading. The complex interplay between these damage modes is not well understood, as well as the role of microstructure on reaction front evolution. Here, we investigate aluminum foils with different compositions and processing conditions to understand how microstructure influences chemo-mechanical degradation. Backscattered electron imaging of ion-milled foil cross sections is used to visualize reaction front evolution and chemo-mechanical degradation. We find that the shape of the reaction front during lithiation is strongly affected by foil composition, defect distribution, and grain size, which, in turn, affects the evolution of chemo-mechanical damage within the foils. Furthermore, a key finding is that the extent of fracture upon delithiation is inversely related to lithium trapping, with foil compositions that exhibit uniform lithiation, and therefore, less fracturing showing greater lithium trapping. Nonreactive precipitate inclusions within alloy foils are also found to induce void formation. This work shows that material design strategies designed to overcome the inverse fracture-lithium trapping relationship could be useful for improving performance. Published by the American Physical Society 2024

The effect of scanning strategies on the defect distribution and mechanical properties of additive manufactured 316L stainless steel components

The effect of scanning strategies on defect distribution and mechanical properties of additive-manufactured (AM) 316L stainless steel components was investigated. Three scanning strategies (A1-stripe, A2-loop, A3-chessboard) were applied to study the defect characteristics using X-ray microscopy (XRM) and evaluate the resulting mechanical behavior through tensile tests. The results indicate that the chessboard strategy produces fewer, smaller, and more uniform defects, contributing to superior tensile strength and reduced anisotropy. In contrast, the loop strategy results in a higher density of elongated defects near the surface, leading to increased stress concentrations and lower tensile performance. This study provides insights into optimizing scanning strategies to enhance defect control, thereby improving the overall mechanical properties of 316L stainless steel in additive manufacturing. These findings are instrumental in advancing the process design for defect mitigation and structural integrity in AM components.

Molecular dynamics simulation of particle radiation damage processes in ZnO crystals

Abstract The cascade collision process in ZnO crystal caused by Primary Knocked Atom (PKA) under different incident conditions has been simulated by the Molecular Dynamics (MD) method. Simulation results show that the incident depth is smaller than 20 atomic layers for PKAs with initial kinetic energy less than 2 keV, and more than 30 atomic layers for PKAs with energy larger than 5 keV. For PKAs with lower energy, the defects are mainly distributed near the incident trajectory, and the distribution range of defects becomes wider with the increase of PKA energy. When the PKA energy is less than 10 keV, the number of defects will reach the peak within 1 ps, and then the system enters into the annihilation process and reaches to steady state within 20 ps. In addition to the incident energy, the incident direction of PKA also has a great influence on the cascade collision process and defect distribution. Compared with two other cases, <111> direction and <001> direction, PKAs along <011> direction produces significantly more defects, with a distribution more concentrated, and the defect recombination rate is also higher in the annihilation process. The evolution of different types of defects in the cascade collision process is different, and the number of O vacancies is the largest, followed by the number of O interstitial atoms, Zn interstitial atoms, and Zn vacancies. Some of the interstitial O atoms replace the Zn vacancies during the annihilation process.

Fatigue characteristics of a newly developed laser powder bed fused scandium-free Al-Mg-Zr-Mn alloy

This study investigates the fully reversed force-controlled fatigue response of a newly developed laser powder bed fused (LPBF) Al-Mg-Zr-Mn alloy (EOS Al5X1) in the post-aged condition. The fatigue behavior revealed a defect-driven response with a fatigue strength of approximately 140 MPa at 5 million cycles. Comprehensive microstructural analyses, including grain size, texture, and precipitate characterization, were performed using advanced microscopy techniques. Additionally, X-ray computed micro-tomography (XCT) was employed to assess defect size and distribution, yielding a relative density of 99.93 %. Fracture surfaces of all fatigue-failed specimens were examined using optical and scanning electron microscopy to determine the primary failure mechanisms, with a focus on distinguishing between defect-driven and microstructural causes. The results indicated that nearly all specimens, tested across seven stress levels, exhibited crack initiation from process-induced volumetric defects, such as pores and lack of fusion. At lower stress levels (up to 195 MPa), single crack initiation sites driven by defects were identified at either surface or subsurface locations. In contrast, at higher stress levels (234 to 351 MPa), multiple crack initiation sites were observed, also at the surface or subsurface.

Imaging of permeability defect distribution by electromagnetic tomography with hybrid L1 normand nuclear normpenalty terms.

Electromagnetic tomography (EMT), with the advantages of being non-contact, non-invasiveness, low cost, simple structure, and fast imaging speed, is a multi-functional tomography technique based on boundary measurement voltages to image the conductivity distribution within the sensing field. EMT is widely used in industrial and biomedical fields. Currently, there are few studies on the application of EMT in magnetic permeability materials, which makes it difficult to obtain high-quality reconstructed images due to its own properties that lead to obvious attenuation of electromagnetic waves during propagation, as well as the ill-posed and ill-conditioned characteristics of EMT. In this paper, a multi-feature objective function integrating L2 normregularization, L1 normregularization, and low-rank normregularization is proposed to solve the challenge of magnetic permeability material imaging. This approach emphasizes the smoothness and sparsity. The split Bregman algorithm is introduced to efficiently solve the proposed objective function by decomposing the complex optimization problem into several simple sub-task iterative schemes. In addition, a nine-coil planar array electromagnetic sensor was developed and a flexible modular EMT system was constructed. We use correlation coefficient and error coefficient as indicators to evaluate the performance of the proposed image reconstruction algorithm. The effectiveness of the proposed method in improving the reconstruction accuracy and robustness is verified through numerical simulations and experiments.

A Strip Steel Surface Defect Salient Object Detection Based on Channel, Spatial and Self-Attention Mechanisms

Strip steel is extensively utilized in industries such as automotive manufacturing and aerospace due to its superior machinability, economic benefits, and adaptability. However, defects on the surface of steel strips, such as inclusions, patches, and scratches, significantly affect the performance and service life of the product. Therefore, the salient object detection of surface defects on strip steel is crucial to ensure the quality of the final product. Many factors, such as the low contrast of surface defects on strip steel, the diversity of defect types, complex texture structures, and irregular defect distribution, hinder existing detection technologies from accurately identifying and segmenting defect areas against complex backgrounds. To address the above problems, we propose a novel detector called S3D-SOD for the salient object detection of strip steel surface defects. For the encoding stage, a residual self-attention block is proposed to explore semantic information cues of high-level features to locate and guide low-level feature information. In addition, we apply a general residual channel and spatial attention to low-level features, enabling the model to adaptively focus on the key channels and spatial areas of feature maps with high resolutions, thereby enhancing the encoder features and accelerating the convergence of the model. For the decoding stage, a simple residual decoder block with an upsampling operation is proposed to realize the integration and interaction of feature information between different layers. Here, the simple residual decoder block is used for feature integration due to the following observation: backbone networks like ResNet and the Swin Transformer, after being pretrained on the large dataset ImageNet and then fine-tuned on a smaller dataset for strip steel surface defects, are capable of extracting feature maps that contain both general image features and the specific characteristics required for the salient object detection of strip steel surface defects. The experimental results on the SD-saliency-900 dataset show that S3D-SOD is better than advanced methods, and it has strong generalization ability and robustness.