Kinds Of Surface Defects Research Articles

Dynamic response of aluminum-graphene layered nanocomposites under shock compression and release based on atomistic simulations

To understand the role of aluminum-graphene (Al-Gr) interface, molecular dynamics simulations have been conducted to investigate the dynamic response of Al-Gr layered nanocomposites. The propagation law of shock waves is revealed and the dislocation nucleation at Al-Gr interface may dominate the plastic deformation mechanism at low impact velocities, which results in the propagation of stepped waveforms. In other words, Gr facilitates dislocation nucleation and its full development in each Al layer. At high impact velocities, Gr can hinder the forward propagation of BCC Al, although Gr can promote BCC nucleation. During shock release, Gr reduces the spall strength of the Al-Gr layered nanocomposites, and then the fracture is dominated by interface fracture, exhibiting single-layer or multi-layer delamination. Gr is found to undergo bending and folding, but Gr remains intact owing to its exceptionally high tensile strength and resistance to high temperatures. With the development of voids and cracks near the Al-Gr interface, the Al layer undergoes significant geometric deformation without internal fragmentation..

The decisive effects of the stress states and brittle-plasticity of the surface defects on their laser-induced damage thresholds on fused silica surfaces

Micro surface defects (SDs) would be unavoidably induced on most fused silica surfaces (FSSs), substantially reducing their laser-induced damage thresholds (LIDTs). There is still no clear understanding of the relation between SD structural feature and their LIDTs, which poses a challenge to the recognition and mitigation of the dangerous SDs with low LIDTs in industrial production. In this work, the brittle-plasticity and stress states of the SDs are found to dominate the atomic point-defect densities on FSSs and resultantly dominate their LIDTs for the first time. Based on this, the SDs have been divided into four categories: pressure-induced brittle defects (PBDs), pressure-induced plastic defects (PPDs), tension-induced brittle defects (TBDs) and tension-induced plastic defects (TPDs). Based on the tested LIDTs, laser damage morphologies and photoluminescence (PL) characteristics, the laser damage resistance of different kinds of SDs are compared, whose reasons are revealed on the basis of the molecular dynamics (MD) simulations. The sharp increase of the atomic point-defect density from the plasticity to the brittleness of the material, the significant disparity in tensile and compressive strength of fused silica, the distinctly different material response behaviours under tensile and compressive forces, as well as the opposite effects of the tensile and compressive forces on the material compactness significantly affect the atomic point-defect densities on FSSs and resultantly contribute to the different laser damage resistance of different kinds of SDs. This work possesses great significance in solving the laser damage on FSSs. It could also provide a new understanding of the impact of the tensile and compressive forces on the LIDTs of various optical elements.

Surface Defect Detection of Strip Steel Based on YOLOv5

Given the various kinds of surface defects and the lack of obvious features, which lead to detection error and leakage detection, a method of strip surface defects detection with a decoupling head, YOLOv5s, is proposed. Firstly, the K-means + + method was utilized to relocate the anchor frames to produce an optimised coupling between the transcendental and the real frame. SimAM's attention free mechanism was incorporated into the Neck network to improve the performance of the model. Finally, the head of the network is replaced by the decoupling head, which separates classified tasks from regressive tasks, thus enhancing convergence and recognition accuracy. The results indicate that the average precision of the proposed algorithm is 80.7% on the NEU-DET dataset, an increase of 3.2% compared to YOLOv5s, and a transfer frame number of FPS of 50 per second, which balanced detection accuracy and operational efficiency. The increased accuracy of detection as compared to other techniques meets the requirements of precision and timeliness.

Research on Curved Parts Surface Quality Detection during Laser‐Directed Energy Deposition Based on Blurry Inpainting Network

The laser‐directed energy deposition technology can be used for additive/subtractive hybrid manufacturing (ASHM). ASHM can realize the manufacturing of some complex parts, such as curved parts. Curved parts will inevitably have some defects during the manufacturing process. However, it is difficult to detect these defects, due to the edge blur of the surface. Therefore, a curved surface quality detection method is proposed. First, the error effect of the curved surface on the surface quality detection is quantitatively analyzed. An efficient channel attention network–DPD network (ECANet–DPDNet) blurry inpainting network model is proposed to effectively reduce the adverse effect of edge blurring. Then, the feature parameters of the repaired image are extracted. The backpropagation (BP) neural network trained by the feature parameters is used to predict curved surface roughness. Finally, two kinds of surface defects are identified using our proposed method based on adaptive threshold segmentation matrix and interference region filtering. The constructed support vector machine (SVM) defect type recognition model is trained using the 15 feature parameters extracted from the defect region. The experimental results show that the accuracy rates for the judgment of scratch defects and pit defects can reach 96.00% and 94.00%, respectively.

Welding seam detection and location: Deep learning network-based approach

There are many kinds of surface defects, large difference in size and strong randomness in the process of material welding, which makes it difficult for some conventional target detection algorithms to quickly and accurately locate and identify. This paper works out an improved Faster R–CNN network model. By adding FPN pyramid structure, variable convolution network and background suppression function to the traditional Faster R–CNN network, the path between the lower layer and the upper layer is greatly shortened, and the location information is better preserved. The simulation test shows that the model above improves the detection accuracy of the network, shortens the detection time, and reduces the loss value. The mAP is increased by 0.243, and the detection time of a single picture is shortened by 0.019s. The detection accuracy of five weld defects is significantly improved, and the accuracy of weld deviation is improved by 0.234, which is the one that improved the most significantly. It shows that the improved model is effective and has good convergence, and can meet the requirements of the detection of weld defects in the actual production process.

A comprehensive review on surface quality improvement methods for additively manufactured parts

PurposeThe purpose of this paper is to study the functionality of additively manufactured (AM) parts, mainly depending on their dimensional accuracy and surface finish. However, the products manufactured using AM usually suffer from defects like roughness or uneven surfaces. This paper discusses the various surface quality improvement techniques, including how to reduce surface defects, surface roughness and dimensional accuracy of AM parts.Design/methodology/approachThere are many different types of popular AM methods. Unfortunately, these AM methods are susceptible to different kinds of surface defects in the product. As a result, pre- and postprocessing efforts and control of various AM process parameters are needed to improve the surface quality and reduce surface roughness.FindingsIn this paper, the various surface quality improvement methods are categorized based on the type of materials, working principles of AM and types of finishing processes. They have been divided into chemical, thermal, mechanical and hybrid-based categories.Research limitations/implicationsThe review has evaluated the possibility of various surface finishing methods for enhancing the surface quality of AM parts. It has also discussed the research perspective of these methods for surface finishing of AM parts at micro- to nanolevel surface roughness and better dimensional accuracy.Originality/valueThis paper represents a comprehensive review of surface quality improvement methods for both metals and polymer-based AM parts.Graphical abstract of surface quality improvement methods

Surface defects in 4H-SiC homoepitaxial layers

Although a high-quality homoepitaxial layer of 4H-silicon carbide (4H-SiC) can be obtained on a 4° off-axis substrate using chemical vapor deposition, the reduction of defects is still a focus of research. In this study, several kinds of surface defects in the 4H-SiC homoepitaxial layer are systemically investigated, including triangles, carrots, surface pits, basal plane dislocations, and step bunching. The morphologies and structures of surface defects are further discussed via optical microscopy and potassium hydroxide-based defect selective etching analysis. Through research and analysis, we found that the origin of surface defects in the 4H-SiC homoepitaxial layer can be attributed to two aspects: the propagation of substrate defects, such as scratches, dislocation, and inclusion, and improper process parameters during epitaxial growth, such as in-situ etch, C/Si ratio, and growth temperature. It is believed that the surface defects in the 4H-SiC homoepitaxial layer can be significantly decreased by precisely controlling the chemistry on the deposition surface during the growth process.

Mechanism of chip formation and surface-defects in orthogonal cutting of soft-brittle potassium dihydrogen phosphate crystals

Micromachining repair of surface defects on KH2PO4 (KDP) optics is an emerging technique in the construction of Inertial Confinement Fusion facilities for obtaining clean nuclear fusion energy. However, this method is yet facing considerable challenges owing to the soft-brittle nature of single-crystal KDP, hence it is necessary to understand its ductile-regime cutting mechanism to generate crack-free surfaces. This paper seeks to investigate the evolution of different cutting mechanism with the change of uncut chip thickness (UCT) in KDP orthogonal cutting processes. A transition of cutting modes from plastic cutting to shear-crack cutting and then fracture cutting with the rise of UCT has been revealed. To explain these cutting phenomena, a novel theoretical model was proposed by calculating the specific energy dissipation for crack/fracture propagations during cutting processes based on fracture mechanics. This analytical model was well validated by the analysis of cutting forces and machined surface quality. Nevertheless, three kinds of surface defects have been observed, i.e. micro pits, micro craters and edge chipping. These surface defects were caused by tearing and spalling of materials with elastic recovery, crack propagation along cleavage planes with ploughing effect, and the peeling away of large-size fracture, respectively. The presented results of great significance for promoting the application of micromachining processes in future engineering repair of KDP optics.

Surface defect detection of steel strips based on classification priority YOLOv3-dense network

ABSTRACT The steel strip is an essential raw material in the machinery industry. Besides, the surface defects of the steel strip directly determine its performance. To achieve rapid and effective detection of the defects, a CP-YOLOv3-dense (classification priority YOLOv3 DenseNet) neural network was proposed in the present study. The model used YOLOv3 as basic network, implemented priority classification on the images, and then replaced the two residual network modules with two dense network modules. Therefore, the network can receive the multi-layer convolution features output by the dense connection block before making predictions, consequently enhancing the reuse and fusion of features. Finally, the six kinds of surface defects were detected by the improved network. According to the results, the detection precision of the CP-YOLOv3-dense network is 85.7%, the recall rate is 82.3%, the mean average precision is 82.73%, and the detection time of each image is 9.68 ms.

The multi-scale analysis of ceramic surface topography created in abrasive machining process

The paper looks at the issues related to the shaping technology of the parts made of ceramic materials and at the influence that machined surface topography has on surface functional properties. The mono- and polycrystalline ceramic parts were shaped in the four treatments of finishing process. The studies of surface topography were performed using three measurement instruments: interference microscope, scanning electron microscope, and atomic force microscope. It was noticed that multi-scale analysis of surface topography allows for obtaining a complementary and detailed overview of changes that ceramic parts undergo during abrasive machining process. The characteristics of the machined surface created in finishing operations were prepared based on the quantitative and qualitative descriptions. Parametric analysis of surface texture allowed for assessing the quality and changes of the machined surface in the technological process. During the non-parametric analysis, surface views after each abrasive machining treatment showed different kinds of surface defects (cracks, crumbles, dents or scratches) which could play an important role in the operation process, i.e. generating various wear mechanisms.

Surface formation mechanism in ultraprecision diamond turning of coarse-grained polycrystalline ZnSe

Zinc selenide is an excellent infrared optical material. In this study, ultraprecision diamond turning experiments were carried out on coarse-grained polycrystalline ZnSe (p-ZnSe) under various conditions and the corresponding surface formation mechanisms were investigated by examining the surface topography, chip morphology, material microstructural change, and cutting forces. Two kinds of surface defects were observed (i.e. plowing-induced micron-scale cleavage craters and tearing-induced submicron-scale tearing pits). It was determined that plowing induced micron-scale cleavage craters could be suppressed by reducing undeformed chip thickness. Furthermore, it was ascertained that tearing-induced submicron-scale tearing pits could be restrained by using a cutting tool with a zero rake angle. The minimal surface roughness was dominated by grain boundary steps formed when the cutting tool crossed twin boundaries at a large angle. A model was proposed for correlating surface defects and boundary steps with crystal orientations and cutting directions. By using a large-nose diamond tool for cutting, a smooth surface of 1.5 nm Sa was obtained. In addition, a zinc blende to cinnabar phase transformation was observed in the cutting chips, and metallization of cutting chips occurred at an extremely small undeformed chip thickness (~20 nm). Moreover, it was discovered that phase transformation inside the workpiece depends on the radius of the tool nose. The findings of this study provide an important reference for ultraprecision machining of brittle polycrystalline materials.

Characterization of the ballistic properties of ejecta from laser shock-loaded samples using high resolution picosecond laser imaging

A high resolution picosecond laser imaging diagnostic has been developed for making high-resolution spatial measurements of ejecta particles moving at high velocities (a few km.s-1). Preliminary results obtained with both visible (532 nm) and UV (355nm) lighting are presented for laser shock-driven tin ejecta experiments performed with different kind of surface defects. These results are compared with those obtained at LANL under high explosive loading using ultraviolet in-line Fraunhofer holography, and also with molecular dynamics (MD) simulations performed at lower space and time scales.

Effect of Local Corrosion Caused by Inclusions and Surface Scratches of Steel in High Temperature and Pressure Flue Gas Environment

By simulating the high temperature and high pressure flue gas environment in heavy oil thermal recovery, the influence of two kinds of surface defects, steel inclusions and surface scratches, on local corrosion of steel was studied. And the mechanism of local corrosion caused by inclusions and surface scratches was analysed steel #20 and 26CrMo4 were used as experimental materials, and reacted in a 9 MPa flue gas environment for 24h. The reaction temperatures were 120 °C and 250 °C. The results show that the non-metallic inclusions in the steel are weak parts of the passivation film on the surface of the sample and are easily corroded by corrosive media. The scratched area is more likely to cause aggregation of corrosive media than other parts of the steel surface, and the iron elements in the metal in this area are more likely to reach the surface and react with the corrosive medium.

A Carrot Sorting System Using Machine Vision Technique

Abstract. Carrot grading is a labor intensive, time-consuming process and is usually performed manually in practical manufacturing. Manual inspection poses many problems in maintaining consistency and guaranteeing the detection efficiency. To improve the grading efficiency and achieve automatic detection, we developed an automated carrot sorting system using machine vision technology. The system consisted of an image processing system, an image acquisition system, a roller conveying system and a control system. It first picked out carrots with surface defects and then graded the qualified carrots by length. We proposed detection methods for three kinds of surface defects: misshapen, fibrous root, and surface crack. Given the fact that the regular carrot is convex-shaped, convex polygon was used to detect carrot shape. We proposed concave point method to detect the fibrous root and adopted Hough Transform to detect surface crack. Experimental results showed that the proposed methods could not only achieve satisfying detection accuracy but also high efficiency. The accuracy of curvature, fibrous root, and surface crack were 95.5%, 98% and 88.3%, respectively. The proposed methods and constructed sorting system could meet the demand of carrot grading and sorting.

Giant perpendicular magnetic anisotropy energies in CoPt thin films: impact of reduced dimensionality and imperfections

The impact of reduced dimensionality on the magnetic properties of the tetragonal L10 CoPt alloy is investigated from ab initio considering several kinds of surface defects. By exploring the dependence of the magnetocrystalline anisotropy energy (MAE) on the thickness of CoPt thin films, we demonstrate the crucial role of the chemical nature of the surface. For instance, Pt-terminated thin films exhibit huge MAEs which can be 1000% larger than those of Co-terminated films. Besides the perfect thin films, we scrutinize the effect of defective surfaces such as stacking faults or anti-sites on the surface layers. Both types of defects reduce considerably the MAE with respect to the one obtained for Pt-terminated thin films. A detailed analysis of the electronic structure of the thin films is provided with a careful comparison to the CoPt bulk case. The behavior of the MAEs is then related to the location of the different virtual bound states utilizing second order perturbation theory.

Novel Approach for Modeling of Nonuniform Slag Layers and Air Gap in Continuous Casting Mold

Various kinds of surface defects on the continuous casting slab usually originate from nonuniform heat transfer and mechanical behavior, especially during the initial solidification inside the mold. In this article, a model-coupled inverse heat transfer problem incorporating the effect of slag layers and air gap is developed to study the nonuniform distribution of liquid slag, solid slag, and air gap layers. The model considers not only the formation and evolution of slag layers and air gap but also the temperatures in the mold copper as measured by thermocouples. The simulation results from the model and the measured temperatures from experiments are shown to be in good agreement with each other. At the casting speed of 0.65 m/min, the liquid slag film disappears and transforms into solid slag entirely at about 400 mm away from meniscus, and an air gap begins to form. Until the mold exit, the maximum thickness of the solid slag layer and air gap gradually increases to 1.34 and 0.056 mm, respectively. The results illustrate that the magnitude and nonuniform distribution of the slag layers and air gap along the cross direction, correlating with heat flux between the shell and mold, eventually determine the temperature profiles of the mold hot face and slab surface. The proposed model may provide a convenient approach for analyzing nonuniform heat transfer and mechanical behaviors between the mold and slab in the real casting process.

Relations between Surface Morphology and Dislocations of SiC Crystal

We observed fine surface morphology of silicon carbide wafers using a low energy scanning electron microscope (LESEM). Typical kinds of surface defects were observed by LESEM. After low temperature KOH treatment, it is confirmed that positions of etch pits are the same positions of these defects. Correlation between LESEM imaging and cross-sectional scanning transmission electron microscopy (STEM) of the same defects reveals threading dislocations and basal plane dislocations at the core of the defects.

On the mechanical integrity of retrieved dental implants

The objective of this work is to investigate the potential state of mechanical damage in used, albeit mechanically intact, dental implants, after their retrieval from the oral cavity because of progressive bone loss (peri-implantitis).100 retrieved dental implants were characterized with no medical record made available prior to the analysis. The implants’ composition, dimensions, and surface treatments were characterized using energy dispersive X-ray analysis and scanning electron microscopy (SEM-EDX). Each implant was thoroughly examined for signs of mechanical defects and damage.The implants represent a random combination of two materials, titanium alloy (Ti–6Al–4V) and commercially pure titanium (CP-Ti), surface treatments and geometries. Two kinds of surface defects were identified: crack-like defects and full cracks that were arbitrarily divided according to their length and appearance. We found that over 60% of the implants contained both crack-like defects and full cracks. In the retrieved sample, we observed that the CP-Ti implants contained more defects and cracks than the Ti–6Al–4V ones. For the various surface roughening treatments, a general correlation with the presence of defects was observed, but without a clear differentiation between the treatments. The high incidence of embedded particles among the observed defect further strengthens the role played by the particles upon defects generation, some of which later evolve into full cracks. It was also found that the dimensions of the implant (width and length) were not correlated with the observed defects, for this specific sample.Our observations indicate that early retrieval of biologically failed implants, many of which contain early signs of mechanical failure as shown here, does actually hinder the later occurrence of implant fracture. It seems that once biological complications will be successfully overcome, such defects might grow later into full cracks as a result of cyclic mastication loads (fatigue). In such a case, the occurrence of implants’ fracture is likely to markedly increase.

Numerical evaluation of irradiation diagram by plasmonic gratings and slit apertures

We describe a fast method to evaluate the far-field profile of a generic planar disposition of plasmonic gratings and slit apertures, based on the ideal decomposition of the structure into a linear array of distinct scattering elements and provided an accurate description of their plasmonic and radiating behaviors. This approach allows for a fast calculation of the irradiation diagram, and has general validity for any kind of surface defects and for multiple set of gratings and slit apertures.

Surface Spot Defects Inspection of Multi-Crystalline Silicon Wafers Based on HALCON

As a common kind of surface defects on multi-crystalline silicon solar cells (about 65% of total defects). Based on HALCON image processing library, an automatic silicon wafer surface spot defects detection and classification system has been developed: captured color images via CCD image sensor, located wafers and spots, extracted the spot features, computed and separated the qualified regions from the unqualified ones by SVM classifier. The experimental result showed a high accuracy of 95.7% and fast image analyzing and classifying process (less than 600ms).