Journal articles on the topic 'Surface defects'
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1
CONRAD, EDWARD H. "THE STABILITY OF LOW INDEX METAL SURFACES TO TOPOLOGICAL DEFECTS." International Journal of Modern Physics B 05, no. 03 (February 10, 1991): 427–59. http://dx.doi.org/10.1142/s0217979291000274.
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The study of defect formation at metal surfaces is a fundamental problem in surface physics. An understanding of defect formation is pertinent to growth and diffusion mechanisms. In addition, surface roughening, faceting, and surface melting are all defect mediated phase transitions involving the formation of different topological defects. While the importance of defects at surfaces is well recognized, the study of surface defects has been hampered by the lack of sufficiently accurate experimental techniques. In fact, it is only in the past 6 years that experiments on the thermal generation of defects on metal surfaces have been performed. This review attempts to outline both the theoretical and experimental work on surface defect formation on metal systems.
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Zhao, Xiaoji, Yanlu Li, and Xian Zhao. "Density Functional Theory Study of the Point Defects on KDP (100) and (101) Surfaces." Molecules 27, no. 24 (December 17, 2022): 9014. http://dx.doi.org/10.3390/molecules27249014.
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Surface defects are usually associated with the formation of other forms of expansion defects in crystals, which have an impact on the crystals’ growth quality and optical properties. Thereby, the structure, stability, and electronic structure of the hydrogen and oxygen vacancy defects (VH and VO) on the (100) and (101) growth surfaces of KDP crystals were studied by using density functional theory. The effects of acidic and alkaline environments on the structure and properties of surface defects were also discussed. It has been found that the considered vacancy defects have different properties on the (100) and (101) surfaces, especially those that have been reported in the bulk KDP crystals. The (100) surface has a strong tolerance for surface VH and VO defects, while the VO defect causes a large lattice relaxation on the (101) surface and introduces a deep defect level in the band gap, which damages the optical properties of KDP crystals. In addition, the results show that the acidic environment is conducive to the repair of the VH defects on the surface and can eliminate the defect states introduced by the surface VO defects, which is conducive to improving the quality of the crystal surface and reducing the defect density. Our study opens up a new way to understand the structure and properties of surface defects in KDP crystals, which are different from the bulk phase, and also provides a theoretical basis for experimentally regulating the surface defects in KDP crystals through an acidic environment.
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Zhan, Hai Fei, Yuan Tong Gu, Cheng Yan, and Prasad K. D. V. Yarlagadda. "Numerical Exploration of the Defect’s Effect on Mechanical Properties of Nanowires under Torsion." Advanced Materials Research 335-336 (September 2011): 498–501. http://dx.doi.org/10.4028/www.scientific.net/amr.335-336.498.
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Molecular dynamics (MD) simulations have been carried out to investigate the defect’s effect on the mechanical properties of single-crystal copper nanowire with different surface defects, under torsion deformation. The torsional rigidity is found insensitive to the surface defects and the critical angle appears an obvious decrease due to the surface defects, the largest decrease is found for the nanowire with surface horizon defect. The deformation mechanism appears different degrees of influence due to surface defects. The surface defects play a role of dislocation sources. Comparing with single intrinsic stacking faults formation for the perfect nanowire, much affluent deformation processes have been activated because of surface defects, for instance, we find the twins formation for the nanowire with a surface 45odefect.
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Gabániová, Mária. "Surface Chemistry-Based Surface Defects Situated on Steel Strips Edges." Defect and Diffusion Forum 405 (November 2020): 199–204. http://dx.doi.org/10.4028/www.scientific.net/ddf.405.199.
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Two thirds of all examined defect cases present on rolled steel strips appeared to be chemical in nature. They are characterized by a modification in surface chemistry. Chemistry-based defects on the steel strips can vary in composition and generally consist of reaction products with the steel substrate. First big category of widely occurring chemistry-based defects is corrosion or oxidation, second contamination with alien matter and third defect category is related to carbon sediments. A number of different surface chemistry-based defects are related to annealing process. Common problem, that occurs in communication is, that identical defects are often indicated by different names and identical names are given for different defects. In the present study an overview including possible causes of three types of the continuous chemistry-based defects situated on the steel strip edges, that appeared to be the same at first glance, is presented: carbon edge deposit, low reflectivity band and annealed border.
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Duarte Naia, Marco, Paulo M. Gordo, Orlando M. N. D. Teodoro, Adriano P. de Lima, Augusto M. C. Moutinho, and Roberto S. Brusa. "Sub-Surface Defects Induced by Low Energy Ar+ Sputtering of Silver." Materials Science Forum 514-516 (May 2006): 1608–12. http://dx.doi.org/10.4028/www.scientific.net/msf.514-516.1608.
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Induced defects in silver polycrystalline samples irradiated with 4 keV Ar+ were characterised with slow positron implantation spectroscopy. The implanted gas was found to interact with ion irradiation defects. The evolution of the defects and gas-defect interactions were followed through a multi-step isochronal annealing treatment. Two different defected regions were detected. A region near to the surface, due to a distribution of vacancy-like defects produced by irradiation, and a deeper one due to coalescence of Ar. The deeper defects evolve with thermal treatments and probably produce cavities which are not easily recovered.
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Akdemir, Bayram, and Şaban Öztürk. "Glass Surface Defects Detection with Wavelet Transforms." International Journal of Materials, Mechanics and Manufacturing 3, no. 3 (2015): 170–73. http://dx.doi.org/10.7763/ijmmm.2015.v3.189.
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Yixuan, Liu, Wu Dongbo, Liang Jiawei, and Wang Hui. "Aeroengine Blade Surface Defect Detection System Based on Improved Faster RCNN." International Journal of Intelligent Systems 2023 (May 10, 2023): 1–14. http://dx.doi.org/10.1155/2023/1992415.
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Aiming at the difficulty of automatic blade detection and the discontinuous defects on the full image, an aeroengine blade surface defect detection system based on improved faster RCNN is designed. Firstly, a dataset of blade surface defects is constructed. To solve the problem that the original faster RCNN is hard to detect tiny defects, RoI align is adopted to replace RoI pooling in the improved faster RCNN and the feature pyramid networks (FPN) combined with ResNet-50 are introduced for feature extraction. To address the issue of discontinuous defects on the full image, the nonmaximum suppression (NMS) algorithm is improved to ensure the continuity of defects. A four-degree-of-freedom (4-DOF) motion platform and an industrial camera are used to collect images of blade surfaces. The detection results generated by the improved faster RCNN are compared with the results of the unimproved method. The experimental results prove that the defect detection system based on the improved faster RCNN can realize automatic defect detection on the blade surface with high accuracy. It also solves the issues of tiny defect detection and discontinuous defects on the full result image of the blade.
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Odgaard, P. F., J. Stoustrup, and P. Andersen. "Detection of Surface Defects on Compact Discs." Journal of Control Science and Engineering 2007 (2007): 1–10. http://dx.doi.org/10.1155/2007/36319.
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Online detection of surface defects on optical discs is of high importance for the accommodation schemes handling these defects. These surface defects introduce defect components to the position measurements of focus and radial tracking positions. The respective controllers will accordingly try to suppress these defect components resulting in a wrong positioning of the optical disc drive. In this paper, two novel schemes for detecting these surface defects are introduced and compared. Both methods, which are an extended threshold scheme and a wavelet packet-based scheme, improve the detection compared with a standard threshold scheme. The extended threshold scheme detects the four tested defects with a maximal detection delay of 3 samples while the wavelet packet-based scheme has a maximal detection delay of 6 samples. Simulations of focus and radial positions in the presence of a surface defect are performed in order to inspect the importance and consequences of the size of the detection delay, from which it can be seen that focus and radial position errors increase significantly due to the defect as the detection delay increases.
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Almazova, L. A., and O. S. Sedova. "SIMULATION OF THE SURFACE DEFECTS INFLUENCE ON THE ALUMINUM ALLOY BEHAVIOUR UNDER THE CYCLIC LOAD CONDITIONS." Frontier materials & technologies, no. 1 (2022): 7–14. http://dx.doi.org/10.18323/2782-4039-2022-1-7-14.
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Aluminum and its alloys, such as the Al–Si–Mg alloy, are widely used in various industrial and engineering fields due to their mechanical properties. In this case, the defects occurring during the casting process adversely affect the behavior of this alloy under cyclic load conditions. Therefore, the study aimed to investigate the surface defect influence on the material's fatigue strength is currently of great importance. The paper presents a numerical investigation based on the finite element method intended to evaluate the effect of the interaction of the complex-shaped defects on the stress of the Al–Si–Mg aluminum alloy. The developed complex-defect model consists of a hemispherical main (base) defect and a secondary defect at the bottom of the main one. The authors use the Chaboche model to describe the material’s behavior under the cyclic load conditions. The paper contains the computational solution constructed with the ANSYS Workbench platform. The authors supposed that it is possible to approximate the considered complex defect form by an equivalent simplified defect. The study shows that the maximum von Mises stress values for the complex-shaped defects are achieved at the joint of the secondary defect with the main one. In the case of an equivalent defect, the maximum values are observed at the defect's bottom and on the periphery. The authors comparatively estimated the uncertainty obtained using an equivalent defect and the cases of three complex-shaped defects and three hemispherical defects without additional (secondary) damage. This estimation shows that in the case of a complex-shaped defect, the equivalent defect model has an error of 14.5 %, which is 6.5 % greater than in the case of the hemispherical defects without secondary damages at the bottom.
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Zhang, Zhongli, Can Wang, Xiaowen Hu, and Yushan Ni. "Shape Effect of Surface Defects on Nanohardness by Quasicontinuum Method." Micromachines 11, no. 10 (September 30, 2020): 909. http://dx.doi.org/10.3390/mi11100909.
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Nanoindentation on a platinum thin film with surface defects in a rectangular shape and triangular shape was simulated using the quasicontinuum method to study the shape effect of surface defects on nanohardness. The results show that the nanohardness of thin film with triangular defects is basically larger than those with rectangular defects, which is closely related to the height of the surface defects at the boundary near to the indenter. Moreover, the triangular defect might have an enhancement effect on nanohardness by a certain size of the defects and the boundary orientation of the defect, where such an enhancement effect increases as the defect grows. Furthermore, the nanohardness decreases when the defect is folded from wide to narrow in the same atom cavity, and particularly expresses a more obvious drop when the height of the defects increases. In addition, larger sizes of the rectangular defect induce more reduction in nanohardness, while the nanohardness of the triangular surface defect is sensitive to the periodic arrangement of atoms changed by the boundary orientation of the defect, which is well explained and demonstrated by the calculation formula theory of necessary load for dislocation emission.
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Luo, Hui, Lianming Cai, and Chenbiao Li. "Rail Surface Defect Detection Based on An Improved YOLOv5s." Applied Sciences 13, no. 12 (June 20, 2023): 7330. http://dx.doi.org/10.3390/app13127330.
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As the operational time of the railway increases, rail surfaces undergo irreversible defects. Once the defects occur, it is easy for them to develop rapidly, which seriously threatens the safe operation of trains. Therefore, the accurate and rapid detection of rail surface defects is very important. However, in the detection of rail surface defects, there are problems, such as low contrast between defects and the background, large scale differences, and insufficient training samples. Therefore, we propose a rail surface defect detection method based on an improved YOLOv5s in this paper. Firstly, the sample dataset of rail surface defect images was augmented with flip transformations, random cropping, and brightness transformations. Next, a Conv2D and Dilated Convolution(CDConv) module was designed to reduce the amount of network computation. In addition, the Swin Transformer was combined with the Backbone and Neck ends to improve the C3 module of the original network. Then, the global attention mechanism (GAM) was introduced into PANet to form a new prediction head, namely Swin transformer and GAM Prediction Head (SGPH). Finally, we used the Soft-SIoUNMS loss to replace the original CIoU loss, which accelerates the convergence speed of the algorithm and reduces regression errors. The experimental results show that the improved YOLOv5s detection algorithm reaches 96.9% in the average precision of rail surface defect detection, offering the accurate and rapid detection of rail surface defects, which has certain engineering application value.
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Szuromi, Phil. "Overcoming surface defects." Science 367, no. 6479 (February 13, 2020): 752.6–753. http://dx.doi.org/10.1126/science.367.6479.752-f.
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Shi, Meng, Lijian Yang, Songwei Gao, and Guoqing Wang. "Metal Surface Defect Detection Method Based on TE01 Mode Microwave." Sensors 22, no. 13 (June 27, 2022): 4848. http://dx.doi.org/10.3390/s22134848.
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With the aim of addressing the difficulty of detecting metal surface cracks and corrosion defects in complex environments, we propose a detection method for metal surface cracks and corrosion defects based on TE01-mode microwave. The microwave detection equations of cracks and corrosion defects were established by the Maxwell equations when the TE01 mode was excited by microwaves, and the relationship model between the defect size and the microwave characteristic quantity was established. A finite integral simulation model was established to analyze the influence of defects on the microwave electric field, magnetic field, and tube wall current in the rectangular waveguide, as well as the return loss at the defect; an experimental platform for the detection of metal surface cracks and corrosion defects was built. The absolute value of the return loss of the microwave reflected wave increased, and with the increase of the defect width, the microwave detection frequency at the defect decreased. The TE01-mode microwave has good detection ability for metal surface cracks and corrosion defects and can effectively detect cracks with a width of 0.3 mm.
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Dobmann, G., R. Becker, H. J. Salzburger, W. Arnold, E. Waschkies, and A. Wirtz. "Nondestructive Testing of Surface Breaking Defects and Near Surface Defects." CIRP Annals 34, no. 1 (1985): 495–97. http://dx.doi.org/10.1016/s0007-8506(07)61819-8.
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Zhong, Zhiyan, Hongxin Wang, and Dan Xiang. "Small Defect Detection Based on Local Structure Similarity for Magnetic Tile Surface." Electronics 12, no. 1 (December 30, 2022): 185. http://dx.doi.org/10.3390/electronics12010185.
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Surface defect detection is critical in manufacturing magnetic tiles to improve production yield. However, existing detection methods are difficult to use to accurately locate and segment small defects on magnetic tile images, because these defects always occupy extremely low proportions of images, and their visual features are difficult to identify, which means their feature representation for defect detection is quite weak. To address this issue, we propose an effective and feasible detection algorithm for small defects on magnetic tile surfaces. Firstly, based on local structure similarity of magnetic tile surfaces, the image is decomposed into low-rank and sparse matrices for estimating possible defect regions. To accurately locate defect areas while filtering out stains, textures, and noises, the sparse matrix is binarized and used for connected components analysis. Then, pixel values in the defect area are normalized, and the Retinex theory is applied to enhance the contrast between defects and background. Finally, an optimal threshold is determined by an automatic threshold segmentation method to segment the defect areas and edges precisely. Experimental results on a number of magnetic tile samples containing different types of defects demonstrated that the proposed algorithm outperforms the existing methods in terms of all evaluation metrics, showing broad industrial application prospects.
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Pilkey, A. K., C. J. Bayley, and M. Györffy. "Influence of Surface Defect Geometry on the Localization and Failure of AA6111 Sheet: Necking versus Shear." Materials Science Forum 519-521 (July 2006): 131–38. http://dx.doi.org/10.4028/www.scientific.net/msf.519-521.131.
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The influence of surface defect geometry on the localization and failure behaviour of AA6111 sheet has been investigated through experimentation and numerical modelling. A series of uniaxial tensile samples were produced with idealized top and bottom surface defects (i.e. grooves), located either symmetrically or asymmetrically on the opposing surfaces. The symmetric arrangement corresponds to the “groove-like” initial imperfection of the classical Marciniak- Kuczyński (M-K) model. Experimental results indicate that both the symmetry of the defects and their wavelength have a profound effect on the resulting mode of localization and failure as well as on the limit strains. Specifically, symmetric surface defects are seen to induce localization and failure through simple necking, whereas asymmetric defects tend to promote macroscopic, throughthickness shearing. Furthermore, asymmetric surface defect geometries are found to produce lower limit strains in the AA6111 sheet under study for defect wavelengths below about 1.5 mm, while the reverse is true when defect wavelengths are above 1.5 mm. Finite element method (FEM) modelling simulations are also presented, demonstrating that the experimentally-observed trends in localization and failure behaviour can be replicated using a mixed isotropic-kinematic hardening implementation of the Gurson-Tvergaard-Needleman (GTN) material model.
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Liu, Lijia, Hua Ma, Jinxi Bai, Zhendong Shi, and lin Zhang. "Research on Digital Testing Technology for Full Surface Defects of Cross scale Heteromorphic Metal Devices." Journal of Physics: Conference Series 2464, no. 1 (March 1, 2023): 012034. http://dx.doi.org/10.1088/1742-6596/2464/1/012034.
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Abstract There are many types of cross scale special-shaped metal components in various fields, and the surface quality (defects) of some key components will affect the device performance and even cause the long-term stability and reliability of the system to decline. The existing manual inspection methods are difficult to accurately quantify and establish a digital traceability database, so it is urgent to study the automatic digital inspection technology of full surface defects. However, most of the devices are curved surfaces with different shapes, large size spans, various types of surface defects and diffuse reflection imaging, and the background of machine vision images is complex, which greatly increases the difficulty of defect detection. Based on the principle of machine vision imaging, this paper designs a long depth of field and low distortion imaging system for curved surfaces. Combined with a flexible scanning motion mechanism with multiple degrees of freedom, this paper studies the technology of complex background defect extraction to realize the full surface defect detection of cross scale special-shaped metal devices.
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Xiaoning Bo, Xiaoning Bo, Jin Wang Xiaoning Bo, Qingfang Liu Jin Wang, Peng Yang Qingfang Liu, and Honglan Li Peng Yang. "Computer Vision Recognition Method for Surface Defects of Casting Workpieces." 電腦學刊 34, no. 3 (June 2023): 305–13. http://dx.doi.org/10.53106/199115992023063403022.
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<p>To improve the recognition efficiency of surface defects in castings, this article first uses median filtering algorithm to denoise the defect image to distinguish between defects and background. Then, gray threshold method is used to segment the image, and the processed image is sent to the improved RefineDet network structure. Improving the RefineDet network structure mainly improves the network depth and incorporates dataset augmentation algorithms. Finally, an experimental platform was built to train, recognize, and compare the collected image dataset. The results show that the accuracy of detecting porosity, blowhole, and flaw defects is 95.6% and 97.3% and 98.15%, the method proposed in this article is accurate and efficient. </p> <p> </p>
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Morishita, Kazuyuki, Taichi Yamaguchi, Kentaro Wada, and Junichiro Yamabe. "Technique for Introducing Internal Defects with Arbitrary Sizes and Locations in Metals via Additive Manufacturing and Evaluation of Fatigue Properties." International Journal of Automation Technology 17, no. 4 (July 5, 2023): 378–87. http://dx.doi.org/10.20965/ijat.2023.p0378.
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Mechanical component failure is usually caused by metal fatigue originating from small defects in metallic materials. Thus, it is important to precisely capture the fatigue properties of materials containing small defects. Fatigue tests of materials with artificial surface defects introduced by drilling have been conducted. Using the resulting data, an equation for predicting the material fatigue limit has been proposed on the basis of the √area parameter model, and its effectiveness has been confirmed for various materials. However, for additive manufactured (AM) materials that contain internal defects resulting in failure, controlling the size of the defect where the fracture originates is extremely difficult. Therefore, verification of the predictive ability of the √area parameter model for AM materials is impossible, in contrast with other materials that fail because of surface defects. In this context, developing a technique to intentionally introduce internal defects with arbitrary sizes at arbitrary locations can provide insights that help predict the fatigue limit of AM materials. This study aimed to establish a technology for quantitatively evaluating the effect of internal defects on the fatigue properties of AM materials by introducing internal defects with arbitrary sizes at arbitrary locations via AM. Specimens with different defect sizes and locations were prepared. Prior to the fatigue tests, the defect sizes and locations were measured non-destructively via X-ray computed tomography (CT). The fatigue tests were conducted in air at room temperature. All the specimens failed because of the intentionally introduced internal defects, and the fatigue lives became shorter with increasing defect sizes, except for the specimens with defects adjacent to the surface. In those cases, fatigue cracks easily reached the surface; therefore, the fatigue lives were speculated to be shorter than those of the specimens with the same defect sizes. Moreover, the defect sizes determined from the fracture surfaces by scanning electron microscopy were nearly consistent with those determined by X-ray CT.
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Wilson, Marshall, Alexander Savtchouk, Andrew Findlay, Jacek Lagowski, Piotr Edelman, Dmitriy Marinskiy, John D’Amico, Ferenc Korsos, Norbert Orsos, and Mariann Csegazine Varga. "Surface Voltage and μPCD Mapping of Defect in Epitaxial SiC." Materials Science Forum 858 (May 2016): 353–56. http://dx.doi.org/10.4028/www.scientific.net/msf.858.353.
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Kelvin-probe surface voltage mapping, SVM, on epitaxial SiC, charged with corona into deep depletion, reveals SV defects manifested as spots with decreased surface voltage. For 150μm thick epi-layer, SV defects coincide with low carrier lifetime spots revealed by microwave detected photoconductance decay, μPCD. In the photoluminescence image, these defects are seen as triangular dark spots, described in literature as stacking-fault related triangular defects. For thin epi-layers (2.2μm), defects are visible only in SVM. In this case, high resolution SVM performed with Kelvin Force Microscopy identifies a triangular defect shape. Two mechanisms are proposed, accounting for SV defects. For high intensity defects exhibiting large magnitude fast decreasing voltage, the probable mechanism is defect related leakage; causing neutralization of corona surface ions. Low intensity defects can be explained considering deep level emission. The latter mechanism has been investigated using SV transient and spectral analysis analogous to isothermal DLTS and Laplace DLTS.
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Zeng, Chao, Chunqing Wang, Yanhong Tian, Chunjin Hang, Wei Liu, and Rong An. "Characterization of the Hot-Cutting Defect Generated from Shape Machining of Alumina Green Tape in the Ceramic-Package Manufacturing Process." Journal of Microelectronics and Electronic Packaging 12, no. 1 (January 1, 2015): 55–60. http://dx.doi.org/10.4071/imaps.433.
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This paper characterizes the rarely mentioned but very damaging defects formed during the hot-cutting process of alumina green tape. Surface and edge morphologies of the hot-cutting surface and a natural surface were examined by optical microscope and scanning electron microscope before and after the sintering process. Obviously different from the natural surface, the hot-cutting surface had a transverse crack-like defect scattered over the surface and a tearing, notch-like defect on the lower edge. A 31 × 22 full factorial experiment was conducted to evaluate the flexural strength controlled by the hot-cutting defect and inherent defects in ceramics. Variance analysis revealed a significant interaction between surface and wearing. The wearing factor had a statistically significant impact on the fracture strength of the top surface but not that of the side and bottom surfaces.
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Grinko, А. M., А. V. Brichka, О. М. Bakalinska, and М. Т. Каrtel. "Application of nano cerium oxide in solid oxide fuel cells." Surface 12(27) (December 30, 2020): 231–50. http://dx.doi.org/10.15407/surface.2020.12.231.
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This review is analyzed the state of modern literature on the nanoceria based materials application as components for solid oxide fuel cells. The principle of operation of fuel cells, their classification and the difference in the constructions of fuel cells are described. The unique redox properties of nanosized cerium oxide make this material promising for application as components for solid oxide fuel cells (SOFC). Because of high ionic conductivity, high coefficient of thermal expansion and low activation energy at relatively low temperatures, cerium-containing materials are widely used as a solid electrolyte. On the surface of nanosized CeO2 there many surface defects (which is determined by the concentration of oxygen vacancies) that lead to the electronic conductivity increases even at temperatures (300 - 700 °C). The concentration of surface defects can be increased by doping the surface of nanoceria by divalent and trivalent cations. The ionic and electrical properties of the obtained nanocomposites dependent from synthesis methods, ionic radii and concentration of doping cations. It is explained the effect of the transition in the size of cerium oxide particles in the nanoscale region on the concentration of surface defects and defects in the sample structure. Particular attention is paid to the effect of doping nanosized CeO2 by transition metal cations and lanthanides on the characteristics of the obtained material, namely, on the increase of concentration of surface defects due to the increase of oxygen vacancies. It is established that nanosized cerium oxide is used for the development and implementation of the main components of SOFC: electrolyte, anode and cathode. Advantages of using solid electrolytes based on nanosized cerium oxide over the classical electrolytes are listed. It was shown that doping of cerium oxide by double and triple cations lead to increase the ionic conductivity and reduces the activation energy and has a positive effect on its characteristics as a SOFC electrolyte. Composites, based on nanoscaled cerium oxide, are actively developed and studied for use as electrodes of solid oxide fuel cells. Cerium-containing anodes are resistant to the deposition of carbon and fuel impurities, increase the catalytic activity of solid oxide fuel cells, and compatible with other components. Nanosized cerium oxide particles are sprayed onto the cathode to prevent the cathode from interacting with the electrolyte. The prospects for the use of cerium-containing materials for the conversion of chemical energy of fuel into electrical energy are analyzed.
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Tomic, Ljubisa, Dalibor Jovanovic, Radovan Karkalic, Vesna Damnjanovic, Branko Kovacevic, Dalibor Filipovic, and Sonja Radakovic. "Application of pulsed flash thermography method for specific defect estimation in aluminum." Thermal Science 19, no. 5 (2015): 1845–54. http://dx.doi.org/10.2298/tsci150307080t.
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Nondestructive thermal examination can uncover the presence of defects via temperature distribution profile anomalies that are created on the surface as a result of a defect. There are many factors that affect the temperature distribution map of the surface being tested by Infrared Thermography. Internal defect properties such as thermal conductivity, heat capacity and defect depth, play an important role in the temperature behavior of the pixels or regions being analyzed. Also, it is well known that other external factors such as the convection heat transfer, variations on the surface emissivity and ambient radiation reflectivity can affect the thermographic signal received by the infrared camera. In this paper we considered a simple structure in the form of flat plate covered with several defects, whose surface we heated with a uniform heat flux impulse. We conducted a theoretical analysis and experimental test of the method for case of defects on an aluminum surface. First, experiments were conducted on surfaces with intentionally created defects in order to determine conditions and boundaries for application of the method. Experimental testing of the pulsed flash thermography (PFT) method was performed on simulated defects on an aluminum test plate filled with air and organic compound n-hexadecane, hydrocarbon that belongs to the Phase Change Materials (PCMs). Study results indicate that it is possible, using the PFT method, to detect the type of material inside defect holes, whose presence disturbs the homogeneous structure of aluminum.
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Zhong, Zhiyan, Hongxin Wang, and Dan Xiang. "Weighted Matrix Decomposition for Small Surface Defect Detection." Micromachines 14, no. 1 (December 29, 2022): 92. http://dx.doi.org/10.3390/mi14010092.
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Detecting small defects against a complex surface is highly challenging but crucial to ensure product quality in industry sectors. However, in the detection performance of existing methods, there remains a huge gap in the localization and segmentation of small defects with limited sizes and extremely weak feature representation. To address the above issue, this paper presents a weighted matrix decomposition model (WMD) for small defect detection against a complex surface. Firstly, a weighted matrix is constructed based on texture characteristics of RGB channels in the defect image, which aims to improve contrast between defects and the background. Based on the sparse and low-rank characteristics of small defects, the weighted matrix is then decomposed into low-rank and sparse matrices corresponding to the redundant background and defect areas, respectively. Finally, an automatic threshold segmentation method is used to obtain the optimal threshold and accurately segment the defect areas and their edges in the sparse matrix. The experimental results show that the proposed model outperforms state-of-the-art methods under various quantitative evaluation metrics and has broad industrial application prospects.
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Jóźwik, Wojciech, and Tomasz Samborski. "Influence of Geometrical Features of Material Defects on the Identification Level by the Eddy Current Method." Solid State Phenomena 237 (August 2015): 136–41. http://dx.doi.org/10.4028/www.scientific.net/ssp.237.136.
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The article presents the results of the influence of geometrical features of defects in materials on the level of identification by the eddy current method. The study involved the inner ring of the tapered roller bearing. Four test defects, located at a constant distance from the inner surface, and a subsurface marker defect were performed in the treadmill of the tested ring. The test defects had a constant cross-sectional area in a perpendicular direction to the surface of the eddy current head. The geometrical features of each defect were the following: shape, the perimeter of the defect projected onto the surface of the ring, and the width and height of the defect projected on the face of the measuring head. The study involved an inner surface (subsurface defect detection) and external surface (the study of surface defects). It has been shown that the shape of the defect affects the level of detection using the eddy current method.
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Hooke, Chris J. "Rapid analysis of the effects of surface and sub-surface defects in elastohydrodynamic contacts." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 236, no. 1 (October 8, 2021): 593–606. http://dx.doi.org/10.1177/0954406221994874.
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Surface and sub-surface defects alter the stress distribution in the material and tend to significantly reduce the life of elastohydrodnamic (EHL) contacts. Where the defects lie well below the surface their effect may be obtained by taking the EHL pressures from standard analyses and using these to determine the stress distribution. However, defects lying closer to the surface can produce significant local displacements and alter the EHL pressures. Analysis is difficult because these displacements change as the defect passes under the conjunction and calculation tends to be extremely time consuming. This paper presents an extension to the perturbation technique used for the rapid analysis of surface roughness. This extension enables solutions for both two and three dimensional defects to be obtained rapidly. The procedure is outlined and a number of examples given.
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Fadli, Vira Fitriza, and Iwa Ovyawan Herlistiono. "Steel Surface Defect Detection using Deep Learning." International Journal of Innovative Science and Research Technology 5, no. 7 (July 23, 2020): 244–50. http://dx.doi.org/10.38124/ijisrt20jul240.
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Steel defects are a frequent problem in steel companies. Proper quality control can reduce quality problems arising from steel defects. Nowadays, steel defects can detect by automation methods that utilize certain algorithms. Deep learning can help the steel defect detection algorithm become more sophisticated. In this study, we use deep learning CNN with Xception architecture to detect steel defects from images taken from high-frequency and high-resolution cameras. There are two techniques used, and both produce respectively 0.94% and 0.85% accuracy. The Xception architecture used in this case shows optimal and stable performance in the process and its results.
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Packard, William E., John D. Dow, Kathleen Doverspike, Ray Kaplan, and Ruth Nicolaides. "Vacancy structures on the GaN(0001) surface." Journal of Materials Research 12, no. 3 (March 1997): 646–50. http://dx.doi.org/10.1557/jmr.1997.0098.
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Scanning tunneling microscopy images are reported for the wurtzite GaN(0001) surface. Terraces are observed, with three kinds of defect structures that are assigned to ordered N-vacancies: (i) striations perpendicular to the step edges, (ii) row defects spaced about 16 Å that intersect the steps at an angle of 30°, and (iii) “oval” defects that result from intersections of lines of vacancies (oriented at 60° with respect to step edges) with the row defects.
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Tao, Xian, Dapeng Zhang, Wenzhi Ma, Xilong Liu, and De Xu. "Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks." Applied Sciences 8, no. 9 (September 6, 2018): 1575. http://dx.doi.org/10.3390/app8091575.
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Automatic metallic surface defect inspection has received increased attention in relation to the quality control of industrial products. Metallic defect detection is usually performed against complex industrial scenarios, presenting an interesting but challenging problem. Traditional methods are based on image processing or shallow machine learning techniques, but these can only detect defects under specific detection conditions, such as obvious defect contours with strong contrast and low noise, at certain scales, or under specific illumination conditions. This paper discusses the automatic detection of metallic defects with a twofold procedure that accurately localizes and classifies defects appearing in input images captured from real industrial environments. A novel cascaded autoencoder (CASAE) architecture is designed for segmenting and localizing defects. The cascading network transforms the input defect image into a pixel-wise prediction mask based on semantic segmentation. The defect regions of segmented results are classified into their specific classes via a compact convolutional neural network (CNN). Metallic defects under various conditions can be successfully detected using an industrial dataset. The experimental results demonstrate that this method meets the robustness and accuracy requirements for metallic defect detection. Meanwhile, it can also be extended to other detection applications.
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., Rutu, Rakesh M R, Pooja L, and Hema C. "Product Defect Identification System." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 459–67. http://dx.doi.org/10.22214/ijraset.2023.51485.
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Abstract: Some uncontrollable defects will occur on the surface of metal work pieces during processing. The existence of surface defects not only affects the appearance of the finished product, but also affects the quality to a certain extent. Surface defect detection of metal work pieces can effectively improve product quality and production efficiency, and is an important link in the process of product quality control. This proposed system uses the convolutional neural network algorithm in deep learning to classify and detect metal surface defects. The surface defect recognition accuracy and defect detection rate of metal work is computed.
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Song, Qiang. "Automated Visual Inspection of Surface Defects on Hot-Rolled Plate." Advanced Materials Research 201-203 (February 2011): 1619–22. http://dx.doi.org/10.4028/www.scientific.net/amr.201-203.1619.
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This paper is concerned with the problem of automatic inspection of hot-rolled plate surface using machine vision. An automated visual inspection (AVI) system has been developed to take images of external hot-rolled plate surfaces and the detailed characteristics of the sensor system which include the illumination and digital camera are described. An intelligent surface defect detection paradigm based on morphology is proposed to detect structural defects on plate surfaces. The proposed method has been implemented and tested on a number of hot-rolled plate surfaces. The results suggest that the method can provide an accurate identification to the defects and can be developed into a commercial visual inspection system.
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Sharma, Mansi, Jongtae Lim, and Hansung Lee. "The Amalgamation of the Object Detection and Semantic Segmentation for Steel Surface Defect Detection." Applied Sciences 12, no. 12 (June 13, 2022): 6004. http://dx.doi.org/10.3390/app12126004.
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Steel surface defect detection is challenging because it contains various atypical defects. Many studies have attempted to detect metal surface defects using deep learning and had success in applying deep learning. Despite many previous studies to solve the steel surface defect detection, it remains a difficult problem. To resolve the atypical defects problem, we introduce a hierarchical approach for the classification and detection of defects on the steel surface. The proposed approach has a hierarchical structure of the binary classifier at the first stage and the object detection and semantic segmentation algorithms at the second stage. It shows 98.6% accuracy in scratch and other types of defect classification and 77.12% mean average precision (mAP) in defect detection using the Northeastern University (NEU) surface defect detection dataset. A comparative analysis with the previous studies shows that the proposed approach achieves excellent results on the NEU dataset.
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Shi, Zhaohua, Laixi Sun, Ting Shao, Hongjie Liu, Jin Huang, Xin Ye, Fengrui Wang, Liming Yang, and Wanguo Zheng. "Statistically Correlating Laser-Induced Damage Performance with Photothermal Absorption for Fused Silica Optics in a High-Power Laser System." Photonics 9, no. 3 (February 26, 2022): 137. http://dx.doi.org/10.3390/photonics9030137.
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Photothermal weak absorption is useful for the diagnosis of absorbing defects on the surface of fused silica optics in high-power lasers. However, how they relate to the laser-induced damage performance remains unclear, especially for a fused silica surface that has been post-treated with different processes (e.g., dynamic chemical etching or magnetorheological finishing). Here, we present a correlation study on the surface defect absorption level and laser-induced damage performance of fused silica optics post-treated with different processes using the photothermal common-path interferometer method. Statistical distribution of the absorbing defects at various absorption levels is obtained. The relationship between the defect density and the laser damage performance was analyzed. We show that the surface absorbing defects of fused silica can be affected by the post-treatment type and material removal amount. Furthermore, we show that the density of the defects with the absorption over 2 ppm is strongly correlated with the damage initiation threshold and damage density. Especially, for high-density defects at this absorption level, the damage density of fused silica optics can be well-predicted. In the low-density range, the density of this kind of defect can reflect the zero-probability damage threshold well. The study exhibits the potential of this methodology to non-destructively detect the key absorbing defects on fused silica surfaces as well as evaluate and optimize the post-treatment level of fused silica optics for high-power laser applications.
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Kwon, Seok Jin, Dong Hyung Lee, Jung Won Seo, and Chan Woo Lee. "Evaluation of Surface and Internal Defects of Railway Wheel using Induced Current Focusing Potential Drop." Key Engineering Materials 321-323 (October 2006): 1483–86. http://dx.doi.org/10.4028/www.scientific.net/kem.321-323.1483.
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In the present paper, the induced current focusing potential drop (ICFPD) technique is applied to the detection of surface and internal defects for railway wheels. To detect the defects for railway wheels, the sensors for ICFPD are optimized and the tests are carried out with respect to 4 surface defects and 3 internal defects each other. The results show that the surface defect of 0.5 mm and internal crack of 1.0 mm apart from surface of wheel tread could be detected by using this method. The ICFPD method is useful to detect the defect that initiated in the tread of railway wheels.
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Guo, Ling, Koji Kamei, Kenji Momose, and Hiroshi Osawa. "Evaluation and Reduction of Epitaxial Wafer Defects Resulting from Carbon-Inclusion Defects in 4H-SiC Substrate." Materials Science Forum 897 (May 2017): 39–42. http://dx.doi.org/10.4028/www.scientific.net/msf.897.39.
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In this study, we investigated the epitaxial surface defects resulting from the carbon-inclusion defects in 4H-SiC substrate. Most carbon-inclusion defects developed into one of three types of epitaxial surface defects under normal epitaxial growth conditions. Among them, we found a regular hexagonal pit by high-resolution microscopy, which we regarded as a large-pit defect, and which had an adverse impact on the reverse electrical characteristics of Schottky barrier diodes. Conversion of a carbon-inclusion defect to a large-pit defect or a triangular defect could be reduced by reducing the C/Si ratio.
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Zhan, Hai Fei, and Yuan Tong Gu. "Exploration of the Defect’s Effect on the Mechanical Properties of Different Orientated Nanowires." Advanced Materials Research 328-330 (September 2011): 1239–44. http://dx.doi.org/10.4028/www.scientific.net/amr.328-330.1239.
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Molecular dynamics (MD) simulations have been carried out to investigate the defect’s effect on the mechanical properties of copper nanowire with different crystallographic orientations, under tensile deformation. Three different crystallographic orientations have been considered. The deformation mechanism has been carefully discussed. It is found that the Young’s modulus is insensitive to the defect, even when the nanowire’s crystallographic orientation is different. However, due to the defect’s effect, the yield strength and yield strain appear a large decrease. The defects have played a role of dislocation sources, the slips or stacking faults are first generated around the locations of the defects. The necking locations have also been affected by different defects. Due to the surface defect, the plastic deformation has received a large influence for the and orientated nanowires, and a relative small influence is seen for the nanowire.
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Popov, V. D. "Investigation of the Surface Defects in N-Channel MOS Transistors Under Long-Term Low-Dose-Rate Irradiation." Journal of Materials Science Research 6, no. 2 (March 21, 2017): 16. http://dx.doi.org/10.5539/jmsr.v6n2p16.
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Gamma-radiation is commonly used to study surface defects in MOS transistors. Early experiments show two stages of surface-defect formation in a MOS structure under low-intensity gamma irradiation (Popov & Vin, 2014; Popov, 2016). On the first stage the defect formation take place on interface Si-SiO2 from the oxide side. This process is described by an exponential dependence (Rashkeev et al., 2002). In the second stage “additional” surface defects are formed from the Si side. Radiation defects of silicon migrated to interface Si-SiO2 from the semiconductor.The goal of this paper is investigation of surface-defect formation in a MOS transistor using the changing of surface electron mobility.
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Zhang, Chi, Jian Cui, and Wei Liu. "Multilayer Feature Extraction of AGCN on Surface Defect Detection of Steel Plates." Computational Intelligence and Neuroscience 2022 (October 3, 2022): 1–13. http://dx.doi.org/10.1155/2022/2549683.
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The development of industry is inseparable from the support of steel materials, and the modern industry has increasingly high requirements for the quality of steel plates. But the process of steel plate production produces many types of defects, such as roll marks, scratches, and scars. These defects will directly affect the quality and performance of the steel plate, so it is necessary to effectively detect them. Steel plate surface defects are characterized by their types, shape, and size: the same defect can have different morphologies, and similarities can exist between different defects. In this paper, industrial steel plate surface defect samples are analyzed, and a sample set is established by screening the collected defect images. Then, annotation and classification are performed. A multilayer feature extraction framework is developed in experiments to train a neural network on the sample set of defects. To address the problems of low automation, slow detection speed, and low accuracy of the traditional defect detection methods, the attention graph convolution network (AGCN) is investigated in this paper. Firstly, faster R-CNN is used as the basic network model for defect detection, and the visual features are jointly refined by combining attention mechanism and graph convolution neural network. The latter network enriches the contextual information in the visual features of steel plates and explores the semantic association between vision and defect types for different kinds of defects using the attention mechanism to achieve intelligent detection of defects, thus enabling our method to meet the practical needs of steel plate production.
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Prince, K. C., A. Morgante, D. Cvetko, and F. Tommasini. "Surface burgers vectors and surface defects." Surface Science 297, no. 2 (November 1993): 235–44. http://dx.doi.org/10.1016/0039-6028(93)90267-n.
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Nguyen, Thanh-Hung, Huu-Long Nguyen, Ngoc-Tam Bui, Trung-Hieu Bui, Van-Ban Vu, Hoai-Nam Duong, and Hong-Hai Hoang. "Vision-Based System for Black Rubber Roller Surface Inspection." Applied Sciences 13, no. 15 (August 6, 2023): 8999. http://dx.doi.org/10.3390/app13158999.
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This paper proposes a machine vision system for the surface inspection of black rubber rollers in manufacturing processes. The system aims to enhance the surface quality of the rollers by detecting and classifying defects. A lighting system is installed to highlight surface defects. Two algorithms are proposed for defect detection: a traditional-based method and a deep learning-based method. The former is fast but limited to surface defect detection, while the latter is slower but capable of detecting and classifying defects. The accuracy of the algorithms is verified through experiments, with the traditional-based method achieving near-perfect accuracy of approximately 98% for defect detection, and the deep learning-based method achieving an accuracy of approximately 95.2% for defect detection and 96% for defect classification. The proposed machine vision system can significantly improve the surface inspection of black rubber rollers, thereby ensuring high-quality production.
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Naumenko, E. A., O. V. Rozhkova, and I. A. Kovaleva. "Comprehensive study of characteristic signs of defects detected during magnetic powder control at the final stage of production of seamless hot‑rolled pipes." Litiyo i Metallurgiya (FOUNDRY PRODUCTION AND METALLURGY), no. 1 (March 13, 2023): 69–72. http://dx.doi.org/10.21122/1683-6065-2023-1-69-72.
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Defects on the outer surface of seamless hot‑rolled steel pipes are formed both as a result of the defects’ transformation in the surface and macrostructure of the initial workpiece, and due to non‑compliance with the rolling technology. Timely detection of defects and elimination of the causes of their formation, allows you to get high‑quality products with high operational reliability. Detection of violations of technology, control of the technological process, carrying out metallographic studies allow classifying defects and establishing the nature and causes of their formation.The article presents the results of a metallographic study of a defect on the outer surface of a hot‑rolled seamless pipe. The genetic and morphological signs of the defect were determined. Descriptions of the appearance of the defect, the microstructure in the defect zone are given. A comparative analysis of the defect detected during magnetic powder control with defects on experimental pipe blanks with artificial defects was carried out. Based on the data obtained, the causes of the defect formation are determined, its classification is given.
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Li, Xuyang, Yu Zheng, Bei Chen, and Enrang Zheng. "Dual Attention-Based Industrial Surface Defect Detection with Consistency Loss." Sensors 22, no. 14 (July 8, 2022): 5141. http://dx.doi.org/10.3390/s22145141.
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In industrial production, flaws and defects inevitably appear on surfaces, resulting in unqualified products. Therefore, surface defect detection plays a key role in ensuring industrial product quality and maintaining industrial production lines. However, surface defects on different products have different manifestations, so it is difficult to regard all defective products as being within one category that has common characteristics. Defective products are also often rare in industrial production, making it difficult to collect enough samples. Therefore, it is appropriate to view the surface defect detection problem as a semi-supervised anomaly detection problem. In this paper, we propose an anomaly detection method that is based on dual attention and consistency loss to accomplish the task of surface defect detection. At the reconstruction stage, we employed both channel attention and pixel attention so that the network could learn more robust normal image reconstruction, which could in turn help to separate images of defects from defect-free images. Moreover, we proposed a consistency loss function that could exploit the differences between the multiple modalities of the images to improve the performance of the anomaly detection. Our experimental results showed that the proposed method could achieve a superior performance compared to the existing anomaly detection-based methods using the Magnetic Tile and MVTec AD datasets.
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Hu, Yazhe, and Tomonari Furukawa. "Degenerate Near-Planar 3D Reconstruction from Two Overlapped Images for Road Defects Detection." Sensors 20, no. 6 (March 15, 2020): 1640. http://dx.doi.org/10.3390/s20061640.
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This paper presents a technique to reconstruct a three-dimensional (3D) road surface from two overlapped images for road defects detection using a downward-facing camera. Since some road defects, such as potholes, are characterized by 3D geometry, the proposed technique reconstructs road surfaces from the overlapped images prior to defect detection. The uniqueness of the proposed technique lies in the use of near-planar characteristics of road surfaces‘ in the 3D reconstruction process, which solves the degenerate road surface reconstruction problem. The reconstructed road surfaces thus result from the richer information. Therefore, the proposed technique detects road surface defects based on the accuracy-enhanced 3D reconstruction. Parametric studies were first performed in a simulated environment to analyze the 3D reconstruction error affected by different variables and show that the reconstruction errors caused by the camera’s image noise, orientation, and vertical movement are so small that they do not affect the road defects detection. Detailed accuracy analysis then shows that the mean and standard deviation of the errors are less than 0.6 mm and 1 mm through real road surface images. Finally, on-road tests demonstrate the effectiveness of the proposed technique in identifying road defects while having over 94% in precision, accuracy, and recall rate.
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Opryshko, L. V., and T. V. Golovnyak. "Study of surface defects in tubes made from nondeformed continuously cast billets." Metaloznavstvo ta obrobka metalìv 98, no. 2 (June 7, 2021): 54–62. http://dx.doi.org/10.15407/mom2021.02.054.
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Defects of outer and inner surfaces of hot-rolled tubes of various steel grades and sizes manufactured on tube-rolling unit with a continuous mill (TPA 30-102) at Interpipe Nikotube LLC from a nondeformed continuously cast billets produced by MZ Dniprostal LLC have been studied. Characteristic genetic and morphological signs of defects were revealed which makes it possible to reliably classify them, identify cause of defect formation and recommend measures to eliminate them. Defects on the outer and inner surfaces of tubes are of metallurgical origin and associated with quality of initial continuously cast billets (a consequence of violation of the smelting and continuous casting technology). Defects on the inner surface of tubes were caused on defects in the axial zone of original billets (unacceptable porosity, looseness, chemical inhomogeneity, liquation stripes and cracks, etc.) and are classified as steel-smelting films and bulges. It was found that displacement of the thermal center of crystallization (a feature of the machines for continuous steel casting of curvilinear type) had an additional negative effect on quality of the inner surface of the studied tubes. Defects on the outer surface of tubes are tears of burning in places of accumulation of low-melting inclusions and their eutectics, as well as steel-smelting scabs on rolled dirt and gas bubbles. Likelihood of formation of scabs on outer surface of the studied tubes over rolled crust introversions is not excluded. The study results will allow manufacturers to reliably classify defects, promptly reject tubes with unacceptable defects of metallurgical origin and minimize supply of low-quality products to consumers. These results will later be included in the classifier of defects in tubes manufactured on the TPA 30-102 unit from nondeformed continuously cast billets. The results of the study of natural signs of defects of metallurgical origin in the tube surface will be useful for elaboration of measures aimed at improvement of the technology of manufacturing initial tube billets. Keywords: tube surface defects, continuously cast billets, microstructure, rolled contamination, low-melting inclusions, eutectic, gas bubbles, decarburization, liquation.
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Xia, Yuwei, Sang Wook Han, and Hyock Ju Kwon. "Image Generation and Recognition for Railway Surface Defect Detection." Sensors 23, no. 10 (May 16, 2023): 4793. http://dx.doi.org/10.3390/s23104793.
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Railway defects can result in substantial economic and human losses. Among all defects, surface defects are the most common and prominent type, and various optical-based non-destructive testing (NDT) methods have been employed to detect them. In NDT, reliable and accurate interpretation of test data is vital for effective defect detection. Among the many sources of errors, human errors are the most unpredictable and frequent. Artificial intelligence (AI) has the potential to address this challenge; however, the lack of sufficient railway images with diverse types of defects is the major obstacle to training the AI models through supervised learning. To overcome this obstacle, this research proposes the RailGAN model, which enhances the basic CycleGAN model by introducing a pre-sampling stage for railway tracks. Two pre-sampling techniques are tested for the RailGAN model: image-filtration, and U-Net. By applying both techniques to 20 real-time railway images, it is demonstrated that U-Net produces more consistent results in image segmentation across all images and is less affected by the pixel intensity values of the railway track. Comparison of the RailGAN model with U-Net and the original CycleGAN model on real-time railway images reveals that the original CycleGAN model generates defects in the irrelevant background, while the RailGAN model produces synthetic defect patterns exclusively on the railway surface. The artificial images generated by the RailGAN model closely resemble real cracks on railway tracks and are suitable for training neural-network-based defect identification algorithms. The effectiveness of the RailGAN model can be evaluated by training a defect identification algorithm with the generated dataset and applying it to real defect images. The proposed RailGAN model has the potential to improve the accuracy of NDT for railway defects, which can ultimately lead to increased safety and reduced economic losses. The method is currently performed offline, but further study is planned to achieve real-time defect detection in the future.
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ZORGATI, HANEN, MONCEF SAID, CHRISTOPHE RAMSEYER, and CLAUDE GIRARDET. "CHARACTERIZATION OF SURFACE DEFECTS THROUGH THE MODIFICATION OF THE INFRARED PROFILE OF ADMOLECULES: APPLICATION TO CO MOLECULES ADSORBED ON (100) MgO AND NaCl SURFACES." Surface Review and Letters 17, no. 04 (August 2010): 431–36. http://dx.doi.org/10.1142/s0218625x1001420x.
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Real surfaces display defects originating either from thermodynamic principles or from local instabilities. In order to help and characterize these surfaces and their defects, infrared spectroscopy of physisorbed test molecules can be used. As an example, we study the behavior of the infrared response of CO molecules randomly deposited on ionic surfaces of MgO and NaCl with the concentration of dipolar defects randomly distributed on these surfaces. The vibrational peak is shifted and asymmetrically broadened when the defect concentration increases as a result of surface inhomogeneity, in semi-quantitative agreement with experimental data.
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Wang, Kun, Zixuan Teng, and Tengyue Zou. "Metal Defect Detection Based on Yolov5." Journal of Physics: Conference Series 2218, no. 1 (March 1, 2022): 012050. http://dx.doi.org/10.1088/1742-6596/2218/1/012050.
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Abstract Metal surface defect detection has been a challenge in the industrial field. The current metal surface defect algorithms target only at a few types of defects and fail to perform well on defects with different scales. In this paper, a large number of metal surface defects are studied based on GC10-DET data set. An improved yolov5 detection network is designed targeting defects of various scales, especially of small-scaled objects, using a specific data enhancement method to regularize and an effective loss function to address data imbalance caused by small-scaled object defects. Finally, the comparative experiment on GC10-DET data set proves the major improvements on accuracy superiority of the proposed method.
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Sioma, Andrzej. "Automated Control of Surface Defects on Ceramic Tiles Using 3D Image Analysis." Materials 13, no. 5 (March 10, 2020): 1250. http://dx.doi.org/10.3390/ma13051250.
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This paper presents a method of acquisition and analysis of three-dimensional images in the task of automatic location and evaluation of defects on the surface of ceramic tiles. It presents a brief description of selected defects appearing on the surface of tiles, along with the analysis of their formation. The paper includes the presentation of the method of constructing a 3D image of the tile surface using the Laser Triangulation Method (LTM), along with the surface imaging parameters employed in the research. The algorithms of three-dimensional surface image analysis of ceramic tiles used in the process of image filtering and defect identification are presented. For selected defects, the method of measuring defect parameters and the method of visualization of defects on the surface are also presented. The developed method was tested on defective products to confirm its effectiveness in the field of quick defect detection in automated control systems installed on production lines.
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Xia, Baizhan, Hao Luo, and Shiguang Shi. "Improved Faster R-CNN Based Surface Defect Detection Algorithm for Plates." Computational Intelligence and Neuroscience 2022 (May 17, 2022): 1–11. http://dx.doi.org/10.1155/2022/3248722.
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Defect recognition plays an important part of panel inspection, and most of the current manual inspection methods are used, but the recognition efficiency and recognition accuracy are low. The Fast-Convolutional Neural Network (Faster R-CNN) algorithm is improved, and a surface defect detection algorithm based on the improved Faster R-CNN is proposed. Firstly, the algorithm improves the bilateral filtering algorithm to smooth the image texture background. Subsequently, a feature pyramid network with a shape-variable convolutional ResNet50 network can be applied to acquire defect semantic feature maps to improve the network’s ability to express the features of multiscale defects while solving the difficulty problem of many types of defects and variable shapes. To obtain more accurate defect localization information, the algorithm in this paper uses the Region of Interest Align (ROI Align) algorithm instead of the crude Region of Interest Pooling (ROI Pooling) algorithm. Then, an improved attention region recommendation network is used to improve the focus of the model on plate defects and suppress the features of complex background. Finally, a K-means algorithm is added to cluster the defect data to derive anchor frames that are better adapted to the plate defects. In this paper, a dataset containing 3216 images of surface defects of plate metal is made by acquiring surface defect images from the production site of the plate metal factory, which mainly include various defect types. This dataset is used to train and test the algorithm model of this paper, and the results of detection accuracy and detection speed are compared with those of other algorithms, which prove that the algorithm of this paper can achieve real-time detection of plate defects with high detection accuracy.
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Lu, Ji Li, and Ming Xing Lin. "An Online Surface Defects Detection System for Step-Axis." Advanced Materials Research 472-475 (February 2012): 954–57. http://dx.doi.org/10.4028/www.scientific.net/amr.472-475.954.
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Surface defects detection is an important application of machine vision. In this paper, an online surface defects detection system of step-axis is studied based on image recognition. To ensure the real-time property, a fast axial surface defects inspection method is put forward, including improved median filtering to reduce noise, gray variation for fast judgment, the maximum variance method (OTSU) to select threshold automatically, contour features for feature extraction, mathematical morphology to detect defect targets, and finally, support vector machine (SVM) to classify and recognize the surface defects of ladder shaft. Experimental results show that the system can detect surface defects of the step-axis in 0.5s, which can meet the real-time requirements.