Presence Of Surface Defects Research Articles

Automatic identification and classification of surface defects in small-sized optics

Abstract Precision optics are widely used in lighting systems, imaging systems, and high-precision inspection equipment. The presence of surface defects in optics can seriously affect the design performance of the equipment, so the optics must be rigorously inspected to eliminate defective optics. Currently, engineering products are moving toward smaller sizes, resulting in smaller optics in them also moving towards smaller sizes. However, realizing the surface defect detection of small-sized optics is a great challenge because it requires micrometer-scale high-resolution image acquisition and automatic detection of defects on their surfaces. A machine learning-based automatic surface defect detection method is proposed for surface defects with many types and small sizes. A dark-field micro-scattering imaging system is used to acquire dark-field images. Then the image segmentation and feature analysis are performed on each image to extract the original feature data, and the original feature data are compressed by reliefF algorithm. Based on the compressed feature data, a classification model is built using a support vector machine. The detection method can classify three kinds of defects, namely, dig, scratch, and scuff mark. The accuracy of the method is up to 98%. The experimental results of this study show that the method can automatically and accurately detect a wide range of micro defects occurring in small-sized optics, thus providing valuable insights into the realization of mass production of small-sized optics. In addition, our proposed method provides some ideas for further research in the field of defect detection in small-sized optics.

Passivating Defects and Constructing Catalytic Sites on CsPbBr3 with ZnBr2 for Photocatalytic CO2 Reduction.

In recent years, halide perovskites have attracted considerable attention for photocatalytic CO2 reduction. However, the presence of surface defects and the lack of specific catalytic sites for CO2 reduction lead to low photocatalytic performance. In this study, we demonstrate a facile method that post-treats CsPbBr3 with ZnBr2 for photocatalytic CO2 reduction. Our experimental and characterization results show that ZnBr2 has a dual role: the Br- ions in ZnBr2 passivate Br vacancies (VBr) on the CsPbBr3 surface, while Zn2+ cations act as catalytic sites for CO2 reduction. The ZnBr2-CsPbBr3 achieves a photocatalytic CO evolution rate of 57 μmol g-1 h-1, which is nearly three times higher than that of the pristine CsPbBr3. The enhanced performance over ZnBr2-CsPbBr3 is mainly due to the decreased VBr and lower reaction energy barrier for CO2 reduction. This work presents an effective method to simultaneously passivate surface defects and introduce catalytic sites, providing useful guidance for the regulation of perovskite photoelectric properties and the design of efficient photocatalysts.

Detection and segmentation of wire rope surface deficiency based on YOLOv8 and U-Net

Abstract The presence of surface defects in wire ropes (WR) may lead to potential safety hazards and performance degradation, necessitating timely detection and repair. Hence, this paper proposes a method for detecting surface defects in WR based on the deep learning models YOLOv8s and U-Net, aiming to identify surface defects in real-time and extract defect data, thereby enhancing the efficiency of surface defect detection. Firstly, the ECA attention mechanism is incorporated into the YOLOv8 algorithm to enhance detection performance, achieving real-time localization and identification of surface defects in WR. Secondly, in order to obtain detailed defect data, the U-Net semantic segmentation algorithm is employed for morphological segmentation of defects, thereby obtaining the contour features of surface defects. Finally, in conjunction with OpenCV technology, the segmentation results of the defects are quantified to extract data, obtaining parameters such as the area and perimeter of the surface defects in the WR. Experimental results demonstrate that the improved YOLOv8-ECA model exhibits good accuracy and robustness, with the model’s mAP@0.5 reaching 84.78%, an increase of 1.13% compared to the base model, an accuracy rate of 90.70%, and an FPS of 65. The U-Net model can efficiently perform segmentation processing on surface defects of WR, with an mIOU of 83.54% and an mPA of 90.78%. This method can rapidly, accurately, and specifically detect surface defects in WR, which is of significant importance in preventing industrial production safety accidents.

Dual-branch information extraction and local attention anchor-free network for defect detection

In the production process, the presence of surface defects seriously affects the quality of industrial products. Existing defect detectors are not suitable for surface with scattered distribution and complex texture of defects. In this study, a dual-branch information extraction and local attention anchor-free network for defect detection (DLA-FCOS), which is based on the fully convolutional one-stage network, is proposed to accurately locate and detect surface defects of industrial products. Firstly, a dual-branch feature extraction network (DFENeT) is proposed and used to improve the extraction ability of complex defects. Then, a local feature enhancement module is proposed, and a residual connection is established to enrich local semantic information. Meanwhile, the self-attention mechanism is introduced to form local attentional residual feature pyramid networks (LA-RFPN) to eliminate the influences of feature misalignments. The mean average accuracy (mAP) and frames per second (FPS) of the proposed DLA-FCOS on the cut layer of the tobacco packet defect dataset (CLTP-DD) are 96.8% and 20.7, respectively, which meets the requirements for accurate and real-time defect detection. Meanwhile, the average accuracy of the proposed DLA-FCOS on the NEU-DET and GC10-DET datasets is 78.4% and 67.7%, respectively. The results demonstrate that the DLA-FCOS has good feasibility and high generalization capability to perform defect detection tasks of industrial products.

Transfer Learning-Based Artificial Neural Network for Predicting Weld Line Occurrence through Process Simulations and Molding Trials

Optimizing process parameters to minimize defects remains an important challenge in injection molding (IM). Machine learning (ML) techniques offer promise in this regard, but their application often requires extensive datasets. Transfer learning (TL) emerges as a solution to this problem, leveraging knowledge from related tasks to enhance model training and performance. This study explores TL’s viability in predicting weld line visibility in injection-molded components using artificial neural networks (ANNs). TL techniques are employed to transfer knowledge between datasets related to different components. Furthermore, both source datasets obtained from simulations and experimental tests are used during the study. In order to use process simulations to obtain data regarding the presence of surface defects, it was necessary to correlate an output variable of the simulations with the experimental observations. The results demonstrate TL’s efficacy in reducing the data required for training predictive models, with simulations proving to be a cost-effective alternative to experimental data. TL from simulations achieves comparable predictive metric values to those of the non-pre-trained network, but with an 83% reduction in the required data for the target dataset. Overall, transfer learning shows promise in streamlining injection molding optimization and reducing manufacturing costs.

Surface-dominated conductance scaling in Weyl semimetal NbAs

Protected surface states arising from non-trivial bandstructure topology in semimetals can potentially enable advanced device functionalities in compute, memory, interconnect, sensing, and communication. This necessitates a fundamental understanding of surface-state transport in nanoscale topological semimetals. Here, we investigate quantum transport in a prototypical topological semimetal NbAs to evaluate the potential of this class of materials for beyond-Cu interconnects in highly-scaled integrated circuits. Using density functional theory (DFT) coupled with non-equilibrium Green’s function (NEGF) calculations, we show that the resistance-area RA product in NbAs films decreases with decreasing thickness at the nanometer scale, in contrast to a nearly constant RA product in ideal Cu films. This anomalous scaling originates from the disproportionately large number of surface conduction states which dominate the ballistic conductance by up to 70% in NbAs thin films. We also show that this favorable RA scaling persists even in the presence of surface defects, in contrast to RA sharply increasing with reducing thickness for films of conventional metals, such as Cu, in the presence of surface defects. These results underscore the potential of topological semimetals as future back-end-of-line (BEOL) interconnect metals.

Bi site doped Ferroelectric BiFe0.95Mn0.05O3 Nanoparticles for Hydrogen Evolution Reaction

AbstractThe investigation delves into the functionality exhibited by ferroelectric BiFe0.95Mn0.05O3 (BFM) nanoparticles (NPs) concerning the hydrogen evolution reaction (HER). The electrocatalytic activity of BFM NPs undergoes a transformative shift as a consequence of mono‐, di‐, and tri‐valent cation substitution. Notably, the strategic engineering of doping at the Bi site within BFM NPs yields a remarkable outcome, namely the conspicuous reduction of the kinetic overpotential prerequisite for HER. This diminished overpotential in doped BFM NPs arises from the confluence of multifarious factors: diminished charge transfer resistance, augmented specific surface area, a discernible distribution of pore sizes ranging from narrow to broad, particles endowed with a shape boasting abundant active facets, and the integration of dopants as novel active sites on the surface. Furthermore, the presence of surface defects, oxygen vacancies, and amplified microstrain within doped BFM NPs contributes to the reduction in overpotential.

Strong electronic metal-support interactions as a strategy for boosting selective hydrogenation of resorcinol to 1,3-cyclohexanedione

Strong electronic metal-support interactions (EMSIs) present in supported metal catalysts play a crucial role in determining their catalytic performances. In this study, we demonstrate the successful utilization of nanosized TiO2 (∼8 nm) as a carrier to achieve highly dispersed Pd clusters (∼0.59 nm) with robust EMSIs, even at a high Pd loading of 5 wt%. The presence of surface defects in nanosized TiO2 facilitates the anchoring of Pd clusters onto the support surface, leading to enhanced EMSIs between TiO2 and Pd. The prepared Pd/TiO2 catalyst delivers drastically improved catalytic property in the hydrogenation of resorcinol (RES), achieving >99 % conversion and >99 % selectivity towards 1,3-cyclohexanedione (1,3-CHD) within 60 min at 2 MPa hydrogenation pressure and 373 K. This performance is superior to that of Pd nanoparticles on TiO2 support with a larger particle size of ∼200 nm, which only achieves 80 % conversion and 37 % selectivity to 1,3-CHD. Both experimental data and theoretical calculations confirm that TiO2 donates electrons to the Pd clusters, thereby enhancing the hydrogenation properties of RES. Specifically, the electron-deficient TiO2 favors the adsorption and activation of phenoxy anion, while the electron-enriched Pd induces a nonplanar conformation of the adsorbed RES molecules and facilitates the electrophilic attack of adsorbed hydrogen on RES by weakening the strength of hydrogen adsorption. Furthermore, the Pd/TiO2 catalyst exhibits excellent recyclability, maintaining its efficiency over 7 cycles, attributed to the stabilizing effect of nanosized TiO2 on Pd clusters. The simple design strategy presented in this work enables the development of supported metal catalysts with significant EMSIs and is expected to attract more attention in future research.

Effect of laser-assisted surface modification of NdFeB permanent magnets applicable in high-demand working environment

This article focuses on research on innovative coatings for permanent magnets used in electrical machines, basing on Ti and ZrO2 powders. The coatings provide a permanent chemical bond and have been prepared by applying chemical powders with the help of TRUMPF TruLaser Robot 5020 in a continuous manner. Comprehensive tests of the prepared samples, like scanning electron microscopy (SEM), were carried out to analyze surface quality, porosity, homogeneity, and the possible presence of surface defects. Additionally, the FEM field analysis were performed using the ANSYS Maxwell software which allowed to determine magnetic flux distribution for NdFeB magnets. Verification of software calculations was carried out according to the different environment conditions like high temperature, water, seawater or soil. The authors performed studies of the Magnetic flux density changes for selected magnets without and with selected coatings. Obtained results showed that newly developed, industrial scale laser-assisted method for Neodymium magnets modifications constitutes an ecofriendly, promising way to prepare fine elements for modern machines construction.

Acoustic process monitoring during the structuring of the diffusion media for fuel cells with Ultrashort Laser Pulses

Inline monitoring is already an essential part of laser material processing. Due to the ongoing technological improvements as well as the development of new sensor technology, new fields of application emerge. In this regard, optical microphones are a promising alternative to conventional microphones as the membrane-free measuring principle allows them to cover a significantly wider frequency spectrum and, thus, enable new possibilities for process observation. Simultaneously to the development in sensor technology, ultrashort-pulse laser beam sources are being further commercialized, which results in a gradual enlargement of their fields of application due to the improved economic efficiency. In order to increase the productivity of ultrashort-pulse laser beam sources, high pulse repetition rates in the upper kilohertz or even in the megahertz region are often used. Together with the emitted pulses exhibiting durations in the picosecond or femtosecond range, the inline process observation is challenging. In this work, the diffusion media for polymer electrolyte membrane fuel cells, consisting of a carbon black particle-based microporous layer and a carbon fiber-based substrate layer, were structured with a laser beam source emitting pulses in the picosecond range. An optical microphone was used for process monitoring in combination with dedicated data processing methods. It was shown that acoustic data can be used to determine the focus position as well as the material transitions during ultrashort-pulsed laser material processing. Additionally, information about the ablation volume and the presence of surface defects can be drawn from the data.

Green synthesis of blue titania nanoparticles by pulsed laser ablation in water

This work aimed at using pulsed laser ablation in water (PLAW) for the production of titanium dioxide nanoparticles in water from a commercially pure titanium target. There is great commercial interest in nanotitania, which can be found in products including paints, coatings, textiles, cosmetics, solar panels, and catalysts. Nevertheless, nanotitania is usually obtained through chemical routes, which are cost-intensive and cause a negative environmental impact. PLAW comes as a cleaner option for obtaining nano-TiO2 since it uses only solid titanium and deionized water as raw materials. After the process, an optical micrography of the Ti targets was carried out to assess the interaction of the laser with the metallic surface. The nanoparticles were characterized by DLS, TEM, Raman, XRD, and UV–Vis. Nano-TiO2 activity as a photocatalyst was tested for the degradation of the dye RR141. TEM images point to two populations of produced nanoparticles: one spherical with average sizes between 8 and 13 nm, and one spindle-like with the major axis average ranging between 17 and 35 nm. Composition analyses with XRD and Raman indicate the formation of anatase, rutile, and amorphous titania. The color of the solution produced was bluish, which may indicate the presence of surface defects that extend the light absorption band to the visible range. The catalysis experiments showed suitable results for the UV spectrum, and the calculated bandgaps were close to that of anatase, around 3.2 eV.

Double‐Side Passivation of Perovskite Solar Cells for High Performance and Stability

AbstractPerovskite solar cells (PSCs) are in the spotlight as promising renewable energy devices by their appealing properties. However, they face challenges both in power conversion efficiency (PCE) and long‐term stability. The presence of surface defects in the PSCs is a significant obstacle to achieving both high efficiency and stability, as these defects cause nonradiative recombination and degradation. Herein, a novel double‐side surface passivation method using phenyltrimethylammonium iodide (PTMAI) salt is applied to remove electronic defects effectively. Furthermore, double‐side passivation with PTMAI contributes to the enhancement of the perovskite crystallinity with the relaxed non‐uniform distribution of local strains. Finally, the efficiency of PSC is significantly improved by double‐side passivation with PTMAI, achieving a PCE of 21.87%. Furthermore, the passivated cell exhibited enhanced long‐term stability, maintaining over 80% of initial PCE after 1860 and 1030 h under 60 °C and 1 sun illumination, respectively.

Effect of surface defects on damage and failure of dynamically driven cylindrical shells

In this work, expanding cylinder experiments are performed using a gas gun on the aluminum 6061 alloy to understand the dynamic deformation and failure behavior of this material in the presence of surface defects in the form of scratches. The experiments are then modeled with the modified Tepla (for tensile plasticity) model, which is equipped with the damage yield function of Gurson [“Continuum theory of ductile rupture by void nucleation and growth: Part I—Yield criteria and flow rules for porous ductile media,” (1977)] and Tvergaard and Needleman [Acta Metall. 32(1), 157–169 (1984)], over-stress viscosity [F. Addessio and J. Johnson, J. Appl. Phys. 74(3), 1640–1648 (1993)], and the underlying viscoplastic model of Preston et al. [J. Appl. Phys. 93(1), 211–220 (2003)] (Preston–Tonks–Wallace model) for the dense material. The Tepla model parameters were calibrated using the Bayesian approach against experimentally measured free-surface velocity data obtained from plate impact experiments on the aluminum 6061 alloy. The evolution of radial expansion and shapes of the deformed cylinder from the simulations on a pristine cylinder wall and a cylinder wall with a longitudinal surface defect shows excellent agreement with the corresponding experimental results. Finally, the role of surface defect size and cylinder wall geometry on the dynamic strength of the cylinder wall was also investigated through a series of simulations.

Fabrication of silane-based surface coating with Ag/TiO2 hybridization and its anti-corrosion performance on metal surface

Surface coating technology is an effective anti-corrosion strategy which can form a layer of anti-corrosion film on metal surface, effectively preventing it from oxidation and corrosion. Among various strategies, silanization treatment of metals is the most commonly used one. However, using silane-based coatings alone often leads to poor anti-corrosion performance due to insufficient mechanical strength and presence of surface defects. In order to improve the corrosion resistance of silane coatings, a hybrid surface coating has been constructed by introducing epoxy resin and Ag/TiO2 nanoparticles into the silane film in this work. After adding epoxy resin, the thickness of the silane film greatly increases, which improves its mechanical properties. Ag/TiO2 nanoparticles can enhance the cross-linking between silane molecules and epoxy resin, thereby preventing formation of surface defects. The epoxy resin-silane-Ag/TiO2 composite surface coating is coated on the surface of AA2024 aluminum alloy using spin coating method. Electrochemical and salt spray tests indicate excellent corrosion resistance performance of the composite coating. This composite coating technology may have potential applications on the surfaces of various metal products such as medical devices and aerospace materials, and will shed light on the development of new surface coating technologies.

Unsupervised surface defect detection of aluminum sheets with combined bright-field and dark-field illumination

In the field of quality control for aluminum sheets, the presence of surface defects can greatly impact the overall product quality. Supervised machine learning methods, which necessitate a substantial quantity of expertly annotated defect samples, present limitations when faced with a limited number and lack of diversity of defect samples. To address these challenges, an unsupervised defect detection method, which utilizes a combination of bright-field and dark-field illumination, is proposed. Combined bright-field and dark-field illumination can provide more information about the surface and enhance the visibility of defects by illuminating the surface from different angles and at different levels of reflectivity. Based on the combined illumination condition, an unsupervised patch-aware feature matching network, which is inspired by the anomaly detection paradigm and only requires defect-free samples for training, is proposed in this paper. The network can extract high-resolution features from bright-field and their corresponding dark-field images simultaneously. Additionally, a well-designed scoring function that considers both intra-field and inter-field relationships is introduced to obtain more accurate anomaly scores for the score map. Moreover, artificially simulated abnormal samples are incorporated into the training phase, which assists the network in explicitly learning potential differences between normal and abnormal samples. The proposed method was thoroughly evaluated on a dataset of surface defects in aluminum sheets. The experimental results demonstrate the superior performance of the proposed method in defect identification and segmentation, achieving a high level of accuracy with small model size and short inference time, outperforming other neural network-based methods. The method has been implemented in a real-time machine vision system, resulting in a significant improvement in detection efficiency and product quality.

Alkali-Assisted Solvothermal Synthesis of I-deficient Nanoplatelet BiOI Microflowers with Enhanced Visible-Light Photocatalytic Performance for Degradation of Organic Pollutants

I-deficient nanoplatelet bismuth oxyiodide (BiOI) microflowers were successfully synthesized via a one-pot solvothermal method using NaOH. Results demonstrated that OH– played a key role in regulating the morphology, pore structure, and energy band structure of the prepared samples. As the volume of NaOH increased, the size of the BiOI microflowers gradually decreased, and the specific surface area and pore volume increased, providing more accessible active sites for photocatalytic reactions. Additionally, some iodine atoms in B-7 (i.e., 7 mL of a 1.0 M NaOH solution was added during the synthesis process) were replaced by hydroxide ions, affording B-7 with slight defects. The loss of iodine atoms shifted the valence band maximum (VBM) from 1.372 eV for the pristine BiOI (B-0) to 1.596 eV for I-deficient BiOI (B-7). The lowered VBM provides more oxidative holes, which is favorable for the oxidation reaction. The photocatalytic activities of the prepared samples were evaluated via the photodegradation of various organic pollutants, such as methyl orange, Rhodamine B, and phenol, under visible light. Owing to the higher surface area and pore volume, lower VBM, and presence of surface defects, B-7 exhibited the highest degradation efficiency among all of the samples.

Dependence of the mechanical properties of a metastable austenitic stainless steel in high-pressure hydrogen gas on machining-induced defects

The susceptibility of metastable 304L to embrittlement in a high-pressure gaseous hydrogen environment was studied from the viewpoint of changes in mechanical properties. The high pressure hydrogen gas leads to deterioration of mechanical properties, which may be accelerated by the presence of surface defects. The tensile specimens were cut with different machining parameters to change the density of the different defects caused by the machining. The maximum dislocation density caused by machining, rather than the maximum strain-induced martensite increased the sensitivity to hydrogen embrittlement.

Neural prediction model for transition onset of a boundary layer in presence of two-dimensional surface defects

Predicting the laminar to turbulent transition is an important aspect of computational fluid dynamics because of its impact on skin friction. Traditional transition prediction methods such as local stability theory or the parabolized stability equation method do not allow for the consideration of strongly non-parallel boundary layer flows, as in the presence of surface defects (bumps, steps, gaps, etc.). A neural network approach, based on an extensive database of two-dimensional incompressible boundary layer stability studies in the presence of gap-like surface defects, is used. These studies consist of linearized Navier–Stokes calculations and provide information on the effect of surface irregularity geometry and aerodynamic conditions on the transition to turbulence. The physical and geometrical parameters characterizing the defect and the flow are then provided to a neural network whose outputs inform about the effect of a given gap on the transition through the ${\rm \Delta} N$ method (where N represents the amplification of the boundary layer instabilities).

Hysteretic behavior of all CVD h-BN/graphene/h-BN heterostructure field-effect transistors by interfacial charge trap

Superior electrical transport properties of graphene field-effect transistors (FETs) can be achieved when graphene is supported by a high-quality hexagonal boron nitride (h-BN) layer. The h-BN layer can serve as a high-performance insulator for a graphene channel to achieve high carrier mobility and to minimize doping effects on graphene by screening out the interaction with a gate oxide substrate. However, chemical vapor deposition (CVD) grown h-BN can have surface defects and impurities unlike mechanically exfoliated h-BN layers, which can result in completely different electrical transport properties of graphene FETs. In this study, we fabricated all CVD h-BN/graphene/h-BN FETs to explore how surface defects and impurities on the supporting h-BN layer can influence electrical transport properties of graphene. The presence of surface defects and impurities on the supporting h-BN layer, as revealed by Raman spectroscopy analysis, resulted in significant p-doping and hysteresis in the h-BN/graphene/h-BN FETs. Hysteresis in the FETs was induced by charge carrier trapping at the surface defects and impurities of the h-BN layer, which can be controlled by modulating a back-gate voltage. Based on the charge trapping mechanism on the surface of the h-BN, electrical charge states in all CVD h-BN/graphene/h-BN can be modulated dynamically by realizing two different resistance states (‘trap’ vs. ‘release’ states), which can be utilized as memristive devices or sensors.

Quenching of oxygen-related defects in graphene oxide nanohybrid: Highly selective room-temperature ethanol sensor

The presence of surface defects, such as epoxy and carbonyl groups, is known to control the charge-carrier transport in graphene oxide (GO). In addition, these surface entities also provide an opportunity to synthesize novel hybrid (NH) materials via chemical bonding. These hybrid materials are particularly interesting for sensing as they offer novel properties like larger surface area and improved physical/chemical properties. Herein, we are proposing a novel SiO2@GO–NH based room-temperature (RT) ethanol sensor. The NH is realized from solution-route by following the sol–gel chemistry of tetraethyl orthosilicate. The attachment of SiO2 with the GO network occurs via the formation of Si–O–C bonds, which also leads to the reduction in the atomic percentage of electron-withdrawing groups. This reduction results in the improvement in electron charge transport in GO, which leads to the RT detection of ethanol. Specifically, the charge transport in NH is found to be dominated by a field-driven temperature-independent 2D variable-range hopping mechanism. While the ethanol sensing mechanism is found to be governed by two processes, i.e., via direct interaction of ethanol with NH and interaction with chemisorbed oxygen ions on the Pt/Si@GO–NH interface. Detailed observations reveal that the SiO2–GO NH has great potential to be used as a biomarker for food quality control.