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To study the polarization reflection characteristics of metal surfaces, a parameter optimization method for the polarization bidirectional reflection distribution function (pBRDF) model of metal surfaces based on the improved strawberry algorithm has been proposed. Firstly, the light scattering characteristics of metal surfaces were analyzed and a multi-parameter pBRDF model was constructed. Then, the working mechanism of the strawberry optimization algorithm was investigated and improved by introducing the chaotic mapping and Levy flight strategy to overcome the shortcomings, such as low convergence rate and easily falling into local optimum. Finally, the method proposed in this paper was validated by simulating open-source data from references and the obtained ones with a self-built experimental platform. The results show that the proposed method outperforms those by nonlinear least squares, particle swarm optimization and the original strawberry algorithm in fitting the detected degree of polarization (DOP) data, indicating the modeling accuracy was significantly improved and better suited to characterize the polarized reflection properties of metal surfaces. [ABSTRACT FROM AUTHOR]

The growing demand for superior metallic components in several industries has emphasized the necessity for effective and dependable inspection techniques. Conventional manual inspection procedures are lengthy, based on personal judgment and susceptible to human mistakes. In this study, we introduce a dynamic multi-layered auto-encoder with a robust deep neural network (DMAE+DNN) system for inspecting flaws in metallic surfaces.We acquired images of the metal surface defects. The neural network design is improved by incorporating a dynamic multi-layered auto-encoder, enabling the system to obtain highly detailed representations of surface data. The results demonstrate the improved performance of the suggested system, showcasing higher levels of recall, precision and F1-score in comparison to conventional defect detection methods. This technological development has the possibility to completely transform quality control procedures by minimizing the need for manual inspections and improving the overall quality of products that heavily rely on metal elements.

ObjectiveThis study focuses on the deposition characteristics of oil mist on metal and fabric surfaces in ship cabin. MethodsOil mist is released in an experimental cabin and allowed to deposit on the target surfaces. The mass difference before and after deposition on the surfaces is weighed to determine the deposition mass, and the oil mist deposition characteristics of surfaces with different orientations and materials are compared. The deposition on the horizontal downward surface is reasonably assumed to be gas phase deposition. Its contribution is then estimated and the particle phase deposition velocity is calculated. ResultsThe deposition mass on the horizontal upward surface of the metal is 3.98 times and 4.66 times greater than that on the vertical surface and horizontal downward surface. For fabric surfaces, the deposition mass on the horizontal upward surface is 1.08 times and 1.20 times greater than that of the vertical surface and horizontal downward surface. For material comparison, the deposition mass on the horizontal upward, vertical and horizontal downward surfaces of the fabric is 49.1 times, 169.8 times and 155.5 times greater than that of the metal surface facing in the same direction respectively. Estimates show that the contribution of phase deposition to the metal horizontal and vertical surfaces are 21.5% and 79.2% respectively. The contribution of phase deposition to the fabric surfaces is about 90%. ConclusionsThe oil mist deposition on fabric surfaces is much stronger than that on metal surfaces. The deposition on the metal horizontal upward surface is much greater than that on the vertical surface and horizontal downward surface. The deposition differences between the fabric surfaces in the three orientations are not significant. Regardless of orientation or material, gas phase deposition cannot be ignored as it plays a dominant role in ship cabin environments.

The growing demand for superior metallic components in several industries has emphasized the necessity for effective and dependable inspection techniques. Conventional manual inspection procedures are lengthy, based on personal judgment and susceptible to human mistakes. In this study, we introduce a dynamic multi-layered auto-encoder with a robust deep neural network (DMAE+DNN) system for inspecting flaws in metallic surfaces. We acquired images of the metal surface defects. The neural network design is improved by incorporating a dynamic multi-layered auto-encoder, enabling the system to obtain highly detailed representations of surface data. The results demonstrate the improved performance of the suggested system, showcasing higher levels of recall, precision and F1-score in comparison to conventional defect detection methods. This technological development has the possibility to completely transform quality control procedures by minimizing the need for manual inspections and improving the overall quality of products that heavily rely on metal elements. [ABSTRACT FROM AUTHOR]

The detection of surface defects on metal products during the production process is crucial for ensuring high-quality products. These defects also lead to significant losses in the high-tech industry. To address the issues of slow detection speed and low accuracy in traditional metal surface defect detection, an improved algorithm based on the YOLOv7-tiny model is proposed. Firstly, to enhance the feature extraction and fusion capabilities of the model, the depth aware convolution module (DAC) is introduced to replace all ELAN-T modules in the network. Secondly, the AWFP- Add module is added after the Concat module in the network's Head section to strengthen the network's ability to adaptively distinguish the importance of different features. Finally, in order to expedite model convergence and alleviate the problem of imbalanced positive and negative samples in the study, a new loss function called Focal-SIoU is used to replace the original model's CIoU loss function. To validate the effectiveness of the proposed model, two industrial metal surface defect datasets, GC10-DET and NEU-DET, were employed in our experiments. Experimental results demonstrate that the improved algorithm achieved detection frame rates exceeding 100 fps on both datasets. Furthermore, the enhanced model achieved an mAP of 81% on the GC10-DET dataset and 80.1% on the NEU-DET dataset. Compared to the original YOLOv7-tiny algorithm, this represents an increase in mAP of nearly 11% and 9.2%, respectively. Moreover, when compared to other novel algorithms, our improved model demonstrated enhanced detection accuracy and significantly improved detection speed. These results collectively indicate that our proposed enhanced model effectively fulfills the industry's demand for rapid and efficient detection and recognition of metal surface defects. [ABSTRACT FROM AUTHOR]

To study the polarization reflection characteristics of metal surfaces, a parameter optimization method for the polarization bidirectional reflection distribution function (pBRDF) model of metal surfaces based on the improved strawberry algorithm has been proposed. Firstly, the light scattering characteristics of metal surfaces were analyzed and a multi-parameter pBRDF model was constructed. Then, the working mechanism of the strawberry optimization algorithm was investigated and improved by introducing the chaotic mapping and Levy flight strategy to overcome the shortcomings, such as low convergence rate and easily falling into local optimum. Finally, the method proposed in this paper was validated by simulating open-source data from references and the obtained ones with a self-built experimental platform. The results show that the proposed method outperforms those by nonlinear least squares, particle swarm optimization and the original strawberry algorithm in fitting the detected degree of polarization (DOP) data, indicating the modeling accuracy was significantly improved and better suited to characterize the polarized reflection properties of metal surfaces.

The detection of surface defects in metal materials has been a challenging issue in the industrial domain. The existing algorithms for metal surface defect detection are limited to a few specific types of defects and exhibit low performance with detection of defects of varying scales. A novel detection method based on the Information Enhancement YOLOv5 Network (IE-YOLOv5) for surface defects in metal parts is proposed, to realize efficient detection, which introduces a lightweight Federated Fusion Slim Neck module (FF-Slim-Neck) and a Parameter-free Spatial Attention mechanism (PSA) in YOLOv5 network. Comparative experiments were conducted using the NEU-DET dataset. The experimental results indicate that the proposed algorithm for detecting defects on metal surfaces achieves an average precision of 96.7% when identifying six different types of surface imperfections: crazing, inclusions, patches, pitting, scaling, and scratches. This represents a 2.4% enhancement in precision compared to the YOLOv5 algorithm. The measured processing velocity of this approach stands at 46.17 frames per second (FPS), highlighting its remarkable qualities of resilience, precision, and real-time capability. [ABSTRACT FROM AUTHOR]

Background. Osseointegration of medical equipment, including implants, into the bone is one of the main conditions for a successful outcome of surgical treatment. To improve the reliability of the functioning of medical equipment products by improving the osseointegrative properties, the authors have developed a unique technology for developing the surface of the implanted component. Thus, the aim of the work is to develop a technology for developing the surface of medical equipment products based on laser metal foaming. Materials and methods. Experimental studies have shown that the surface geometry and structure of the surface layer after laser foaming meet the requirements for medical equipment products for better osseointegration into the patient's bone. The results were confirmed by optical and electron microscopy data. Results. Studies have shown that the composition of the gaseous medium has the most significant effect on the structure of the surface layer. This factor determines the appearance in the surface layer of a large number of chemical compounds that significantly change the properties of the material. During the experiment, this factor was varied, and the rest were stabilized at a constant level. It has been established that the increase in the chemical inhomogeneity of the surface layer increases significantly with the introduction of air into the composition. When Argon is used as a working medium, the surface structure is normalized to the level of the material base. Conclusions. In the process of work, a developed structure was created on the selected surface area without changing the chemical composition with an increase in the area of osseointegrated structures up to 100 % of the original surface. For processing, it is recommended to use an inert gas medium, and the medium pressure must be above 8 bar. The feed speed of the laser head should be at least 5000 mm/min, but not exceed 10 000 mm/min.

The detection of surface defects in metal materials has been a challenging issue in the industrial domain. The existing algorithms for metal surface defect detection are limited to a few specific types of defects and exhibit low performance with detection of defects of varying scales. A novel detection method based on the Information Enhancement YOLOv5 Network (IE-YOLOv5) for surface defects in metal parts is proposed, to realize efficient detection, which introduces a lightweight Federated Fusion Slim Neck module (FF-Slim-Neck) and a Parameter-free Spatial Attention mechanism (PSA) in YOLOv5 network. Comparative experiments were conducted using the NEU-DET dataset. The experimental results indicate that the proposed algorithm for detecting defects on metal surfaces achieves an average precision of 96.7% when identifying six different types of surface imperfections: crazing, inclusions, patches, pitting, scaling, and scratches. This represents a 2.4% enhancement in precision compared to the YOLOv5 algorithm. The measured processing velocity of this approach stands at 46.17 frames per second (FPS), highlighting its remarkable qualities of resilience, precision, and real-time capability.

Computer vision and deep learning approaches have an important role in industrial inspection systems. Computer vision technology is essential for fast, defect-free control of products in the production line. The importance of the computer vision concept is recognized when the problems of the classical methods are taken into consideration. Metallic defect detection is a challenging problem as metal surfaces are easily affected by environmental factors such as lighting and light reflection. Since traditional detection algorithms are inefficient in complex problems, we propose a novel method to detect and classify metal surface defects, such as cracks, scratches, inclusion, etc. The type and location of defects were detected by the Faster Regional Convolutional Neural Network (Faster R-CNN), combined with the Shape From Shading (SFS) method, which can extract surface characteristics. The Northeastern University (NEU) surface defect database was used for defective samples. The proposed algorithm has also been tested on an unlabeled dataset (KolektorSDD2/KSDD2) to show labeling performance. The results on both labeled and unlabeled datasets have demonstrated state-of-the-art performance in automatic defect detection, classification, and labeling. The proposed method has satisfactory results for the detection of defects on the metal surface, and the mean average precision is 0.83. The average precision of crazing, pitted surface, patches, scratches, inclusion, and rolled-in scale are 0.98, 0.81, 0,90, 0.79, 0.88, and 0.62, respectively.

Background: Acinetobacter spp., emerging pathogens equipped with the competence to establish multitudinous severe infections in immunocompromised hosts, are grievous threats to human health. To tackle the enormous burden of disease caused by Acinetobacter spp., the headlong discovery and the advancement of novel therapies are of the essence at this juncture. The present study attempted to determine the antimicrobial effects of copper on the clinical isolates of Acinetobacter spp. (Iso-03 and Iso-04). Methods: The potential deployment of copper-based antibacterial strategies against Acinetobacter spp. was assessed by exposing the isolates to the increasing concentrations of CuSO4 (from 2.5 to 1.5 mM) in liquid culture (M9 minimal medium) for 6 h and also through the exposure of them on solid metal surfaces (stainless steel and copper coupons) for 75 min, wherein the copper sensitivity and resistance of the clinical isolates of Acinetobacter spp. were determined. Results: There was no interference with the growth of the isolates at the low concentrations, whereas the bacterial growth was affected by the high concentrations of CuSO4 at different levels. During the exposure on the solid metal coupons, no loss of viability of isolates was observed on stainless steel, however, the rapid death of isolates was discernible on copper surface, leading to a dramatic decrease in the number of colony-forming units (CFUs), eventually to the limit of detection (3 CFUs per coupon). Conclusion: This study substantiated that copper possesses antimicrobial properties which can be deployed in novel therapies for the prevention of the infections caused by Acinetobacter spp. and other emerging pathogens. Further studies on the sensitivity and resistance of Acinetobacter spp. to copper at the molecular genetics levels can open the door to better exploitation of this metal for the inhibition of the vigorous growth of drug-resistant Acinetobacter spp.

Background: Acinetobacter spp., emerging pathogens equipped with the competence to establish multitudinous severe infections in immunocompromised hosts, are grievous threats to human health. To tackle the enormous burden of disease caused by Acinetobacter spp., the headlong discovery and the advancement of novel therapies are of the essence at this juncture. The present study attempted to determine the antimicrobial effects of copper on the clinical isolates of Acinetobacter spp. (Iso-03 and Iso-04). Methods: The potential deployment of copper-based antibacterial strategies against Acinetobacter spp. was assessed by exposing the isolates to the increasing concentrations of CuSO4 (from 2.5 to 1.5 mM) in liquid culture (M9 minimal medium) for 6 h and also through the exposure of them on solid metal surfaces (stainless steel and copper coupons) for 75 min, wherein the copper sensitivity and resistance of the clinical isolates of Acinetobacter spp. were determined. Results: There was no interference with the growth of the isolates at the low concentrations, whereas the bacterial growth was affected by the high concentrations of CuSO4 at different levels. During the exposure on the solid metal coupons, no loss of viability of isolates was observed on stainless steel, however, the rapid death of isolates was discernible on copper surface, leading to a dramatic decrease in the number of colony-forming units (CFUs), eventually to the limit of detection (3 CFUs per coupon). Conclusion: This study substantiated that copper possesses antimicrobial properties which can be deployed in novel therapies for the prevention of the infections caused by Acinetobacter spp. and other emerging pathogens. Further studies on the sensitivity and resistance of Acinetobacter spp. to copper at the molecular genetics levels can open the door to better exploitation of this metal for the inhibition of the vigorous growth of drug-resistant Acinetobacter spp. [ABSTRACT FROM AUTHOR]

We re-investigate the image charge model of Iori and Corni (Iori and Corni, J. Comput. Chem. 2008, 29, 1656). We find that a simple symmetrization of their model allows to obtain quantitatively correct results for the electrostatic interaction of a water molecule with a metallic surface. This symmetrization reduces the magnitude of the electrostatic interaction to less than 10% of the total interaction energy. © 2017 Wiley Periodicals, Inc.

The article provides an analysis of the physical processes underlying the operation of the measuring transducer, with a time based information presentation. A mathematical model is developed that describes the process of free oscillation attenuation excited in the LC-contour of primary measuring transducer, and analyzes and evaluates the influence of external factors that influence the measurement results. The ways of elimination of their influence on the results of measuring control are offered.

A 2D barcode is a reliable way to provide lifetime traceability of parts that are exposed to harsh environments. However, there are considerable challenges in adopting mobile cameras to read symbols directly marked on metal surfaces. Images captured by mobile cameras are usually of low quality with poor contrast due to the reflective surface of 2D barcode symbols. To deal with this problem, a novel laser-marked Data Matrix symbols reading method based on deep learning is proposed for mobile phone captured images. Utilizing the barcode module features, we train different convolutional neural network (CNN) models to learn the colors of two adjacent modules of a Data Matrix symbol. Depending on whether the colors of the two adjacent modules are the same or not, an edge image is transformed from a square grid, which is the same size as the barcode. A correction method based on the KM algorithm is used to get a corrected edge image, which helps to reconstruct the final barcode image. Experiments are carried out on our database, and the results show that the proposed algorithm outperforms in high accuracy of barcode recognition. [ABSTRACT FROM AUTHOR]

The current trend in automated optical inspection (AOI) systems employs deep learning models to detect defects on a metal surface. The setback of deep learning models is that they are time-consuming because the images obtained after every lighting adjustment must be used to train the deep learning models again and confirm whether the detection results have improved. To save the time spent using datasets to train deep networks, we proposed a comprehensive assessment score that combines defect visibility, visibility distribution, and overexposure based on the operation principles of convolution neural networks. It can be used to assess whether the training image dataset can improve the defect detection rate of the deep learning model such as You Only Look Once (YOLO), Single Shot MultiBox Detector (SSD), and Faster Region-based Convolutional Neural Network (Faster R-CNN) without training defect image datasets. We collected all of the weight combinations with the right prediction results and used linear regression to obtain the optimal weight coefficients. We found that visibility and overexposure had a greater impact on the comprehensive assessment score. We compared the proposed approach with existing image quality assessment methods, including mean absolute error (MAE), peak signal-to-noise ratio (PSNR), universal image quality index (UIQI), natural image quality evaluator (NIQE), perception-based quality evaluator (PIQE), and blind/referenceless image spatial quality evaluator (BRISQUE). The experiment results indicated that our proposed comprehensive assessment score is more correlated to the F2-score of the detection models than the IQA methods by the verification methods of Spearman Rank Correlation Coefficient (SRCC), Pearson Correlation, and Kendall Correlation. Thus, referring to this index during the collection of image data and choosing datasets with the highest score to train the model will produce better detection accuracy.

One of the important characteristics of the surface properties of metal parts subjected to friction is hardness. Hardness measurements are important for determining the operational characteristics of parts and monitoring the technological regimes of surface modification. However, hardness measurements of thin modified layers made by different methods can lead to differences in measurement results. The aim of the article was to study the hardness of a metal surface modified with ultrafine particles of minerals by two different methods (instrumental indentation and Vickers hardness measurement) and a comparative analysis of the measurement results obtained by these methods.Standard Vickers hardness measurements at loads of 0.025, 0.1 and 0.5 kgf showed a qualitative difference between the hardness values of the two samples modified with different mixtures of ultrafine particles of minerals and a large heterogeneity of the hardness values over the area. By the method of instrumental hardness, standard measurements were performed without preliminary selection of the indentation site (at a load of 1.05 N) and measurements during indentation into even sections (at low loads of 10 mN).It is noted that the high precision of measurements implemented by instrumental indentation, due to the large roughness of the samples, leads to large values of the error in calculating the measurement results. An additional difference in the results of measurements performed by two methods at shallow indentation depths may be due to the fact that the object under study has a complex structure consisting of a metal matrix and particles distributed over the depth of the sample. A possible way out of the situation lies in the transition from the use of hardness measures when calibrating instruments to standard samples of properties for which the constancy of mechanical properties in the measured range of indentation depths will be ensured, but which are not yet available in research practice. Therefore, at present, when carrying out work related to the search for optimal conditions for obtaining thin wear-resistant layers on the surface of metals modified with ultrafine particles of minerals, comparative measurements performed by one measurement method are recommended.

One of the important tasks is to deliver to the consumer a calibrated high-quality metal in a saleable condition, which requires protection from atmospheric corrosion and adverse climatic factors, protection from contamination and mechanical damage for the period of transportation to the consumer by motor transport, and storage at intermediate warehouses before going into production. A comprehensive system approach is needed to preserve the quality and appearance of the products on the way from the manufacturer to the consumer. The use of modern packaging materials, such as paper with corrosion inhibitor and polypropylene fabric reinforcement, will allow to obtain permanent guaranteed protection of the rolled surface and expands the possibilities of transportation and sales markets of metal products. The testing stage of the corrosion inhibitor included the determination of its protective properties. It is established that the advantage of packaging of rolled products in anticorrosive paper is that it, combining the functions of packaging means and means of preservation, allows you to completely abandon the expensive and time-consuming preservation of metal products with oils and greases. The use of a volatile corrosion inhibitor in the composition of the paper provides full protection of rolled products.

A recent experiment has reported that body-centered cubic (bcc)-structured Ag is realized by bending face-centered cubic (fcc)-structured Ag nanowires (Sun et al., 2022). However, the bcc phase has been observed only near the Ag surface. Here, we explore how the bcc phase is stabilized near the surface by compressions. Our first-principles calculations for noble metals, Cu, Ag, and Au, indicate that body-centered tetragonal (bct) rather than bcc structure is preferred due to the surface effect. The bct-fcc boundary treated as a fixed boundary condition is necessary to thermodynamically stabilize the bct phase of Ag nanowire. The correlation between crystal structure and electron density-of-states is also discussed for three noble metals. [Display omitted] [ABSTRACT FROM AUTHOR]

To prepare durable superhydrophobic coatings with low-cost is of great importance for its practical application. How to improve the mechanical endurance by a facile method is rarely mentioned in reports. Achieving thicker coating with multi-layer micro-nano structures is critical for obtaining the robust superhydrophobic coating due to the high abrasion resistance to mechanical damage. Herein, a superhydrophobic coating with a multi-layer self-similar structure was fabricated by a facile blending and spraying method. Epoxy resin, neutral silicone glue, polysiloxane and nano-SiO 2 are the main composition of the ESPS coating which are important for the multi-layer micro-nano structure construction and adhesion stability between the coating and the substrate. The water contact angle (WCA) and sliding angle (SA) of the ESPS coating were 162.5 ± 0.5° and 4.6 ± 0.4°, respectively. Notably, the ESPS coating with four layers can be abraded with 1500-grid sandpaper under the load of 400 Pa for 24 m. In addition, the prepared samples were put in an outdoor environment for 180 days and exhibited excellent durability and stability. These outstanding properties could be attributed to the hierarchical self-similar structure of the composited coating and the good resistance and adhesion stability provided by polysiloxane. Additionally, the ESPS coating could restore its superhydrophobic characteristic by self-healing realized by the neutral silicone glue in the composition. The good stability and operability of the ESPS coating make it applicable to a wide range of applications. • A multi-layer self-similar superhydrophobic coating was prepared by a simple, cheap and harmless method. • The mechanical durability of the superhydrophobic coating can be improved by increasing the longitudinal thickness. • Plasticizer was used to improve the ductility of epoxy resin which is important for its superhydrophobic performance. • Self-healing property of the coating could be realized by the neutral silicone glue in the composite. [ABSTRACT FROM AUTHOR]

The modern industrial production of the Republic of Belarus is characterized by the absence of its own raw material base and significant dependence on imported energy carriers and material resources supplied at world prices. When working in such conditions, production efficiency can be achieved through all-round economy and creation of modern energyand resource-saving technologies. However implementation of such technologies, for example, laser cutting and welding, cladding, flame spraying, painting, etc. directly depends on quality of cleaning metal surfaces from corrosion. Theoretical and experimental studies conducted at the Department of Shipbuilding and Hydraulics of the Belarusian National Technical University have shown that it is very economical to remove corrosion products from metal surfaces using new technology of reverse jet cleaning. The reverse jet cleaning technology is based on a physical principle which presupposes that a jet of working fluid (pulp based on river sand or bentonite clay) rotates 180º when it hits the surface to be cleaned and it leads to an increase in jet impact on the surface to be cleaned by 1.5–2 times due to occurrence of a reactive component. In order to ensure a marked jet reversal an original case design has been developed which is characterized by a patent novelty. One of the main elements in this design is a confuser-shaped stream-forming device. Theoretical investigations on pressure losses of working fluid in a confuser channel which are based on the study of functional at the extremum have made it possible to obtain a dependence for calculation of an optimal cone angle at a turbulent mode of motion within the range of Reynolds numbers 4000

The need for environmentally friendly chemical products in daily needs encourages the production of its. The green-chemistry concept is using the process and produces chemical products that are ecofriendly. Including ecofriendly chemical products are base oil and additives for lubricants, grease, and fuels. The production is expected to reduce the consumption of mineral and synthetic base oils, so it will be biodegradable and renewable. This study compares the results of analysis of metallic surfaces immersed in the mixture of mineral and vegetable base oil, with the addition of rice bran oil bioadditive, ie epoxidized methyl ester (EME) and hydroxyl alkylbenzene sulphonic acid ester (HASE). The research method consists of preparing HASE; analyzing the effect of HASE and EME bioadditives addition on the mixture of base oil to the changing of metallic weight immersed in the mixture; determining the inhibition efficiency of the EME and HSAE additions; analyzing the metal surface using SEM-EDX (Scanning Electron Microscope) / (Energy Dispersive X-ray Spectrometry) to find images of microstructure and chemical compounds contained in specimens, and testing the metal difractogram immersed in base oil mixtures with bioadditive using XRD (X-Ray Diffraction). SEM test results of carbon steel immersed in a mixture of base oil and bioadditives show corrosion in which the metal surface color immersed in EME bioadditive mixtures is brighter. EDX spectra of metal sample surfaces immersed in a mixture of base oil, EME and HASE contain carbon (C) and iron (Fe). The carbon content in carbon steel samples immersed in the mixtures and HASE is higher. XRD test results show Fe2O3 phases in carbon steel samples immersed in the HASE bioadditive mixture are higher than in EME. While Fe3O4 phases in carbon steel samples immersed in the EME bioadditive mixture are higher than Fe2O3 phases in samples immersed in HASE bioadditive mixture.

In this paper, a widely applicable method that has promise in endowing various metals with oil-water separation ability by femtosecond laser processing is reported. We take an iron sheet as an example to show how to use a femtosecond laser to prepare underwater superoleophobic microstructure on metal substrates and then achieve oil-water separation. An array of through microholes was previously prepared on the iron sheet by a mechanical drilling process. Then, rough nanostructures were created on the surface of the porous sheet by femtosecond laser ablation, resulting in excellent superoleophobicity in water. When the mixture of water and oil was poured onto the porous underwater superoleophobic metal sheet, only the water in the mixture could pass through the sheet while the oil was intercepted, thus the oil/water mixture was successfully separated with high efficiency. Such a novel preparation process and separation manner can extend to different metal substrates. We believe that a wide range of metals like iron sheet can be potentially endowed with oil-water separation ability by femtosecond laser processing because femtosecond laser can process almost all of the metals.

A 2D barcode is a reliable way to provide lifetime traceability of parts that are exposed to harsh environments. However, there are considerable challenges in adopting mobile cameras to read symbols directly marked on metal surfaces. Images captured by mobile cameras are usually of low quality with poor contrast due to the reflective surface of 2D barcode symbols. To deal with this problem, a novel laser-marked Data Matrix symbols reading method based on deep learning is proposed for mobile phone captured images. Utilizing the barcode module features, we train different convolutional neural network (CNN) models to learn the colors of two adjacent modules of a Data Matrix symbol. Depending on whether the colors of the two adjacent modules are the same or not, an edge image is transformed from a square grid, which is the same size as the barcode. A correction method based on the KM algorithm is used to get a corrected edge image, which helps to reconstruct the final barcode image. Experiments are carried out on our database, and the results show that the proposed algorithm outperforms in high accuracy of barcode recognition.

The field research results of the austenitic steel wear under the droplet impact erosion are presented. The stages of the erosive wear origin and development are considered. The leading part of the side cracks in the forming and growth of the erosion fracture craters, as specified in Marshall-Lawn model, is shown by the high-resolution scanning electron microscopy technique.

The detection of surface defects on metal products during the production process is crucial for ensuring high-quality products. These defects also lead to significant losses in the high-tech industry. To address the issues of slow detection speed and low accuracy in traditional metal surface defect detection, an improved algorithm based on the YOLOv7-tiny model is proposed. Firstly, to enhance the feature extraction and fusion capabilities of the model, the depth aware convolution module (DAC) is introduced to replace all ELAN-T modules in the network. Secondly, the AWFP-Add module is added after the Concat module in the network's Head section to strengthen the network's ability to adaptively distinguish the importance of different features. Finally, in order to expedite model convergence and alleviate the problem of imbalanced positive and negative samples in the study, a new loss function called Focal-SIoU is used to replace the original model's CIoU loss function. To validate the effectiveness of the proposed model, two industrial metal surface defect datasets, GC10-DET and NEU-DET, were employed in our experiments. Experimental results demonstrate that the improved algorithm achieved detection frame rates exceeding 100 fps on both datasets. Furthermore, the enhanced model achieved an mAP of 81% on the GC10-DET dataset and 80.1% on the NEU-DET dataset. Compared to the original YOLOv7-tiny algorithm, this represents an increase in mAP of nearly 11% and 9.2%, respectively. Moreover, when compared to other novel algorithms, our improved model demonstrated enhanced detection accuracy and significantly improved detection speed. These results collectively indicate that our proposed enhanced model effectively fulfills the industry's demand for rapid and efficient detection and recognition of metal surface defects., Competing Interests: The authors declare there are no competing interests., (©2024 Chen et al.)

Microsecond molecular dynamics simulations were performed to investigate the methane hydrate formation on smooth and rough metal surface covered with water, light oil, and heavy oil with asphaltenes, respectively. The growth and crystallization of methane hydrates were characterized by the four-body order parameter and the face-saturated incomplete cage analysis. Results highlighted the priority for hydrate growth inside the groove of rough metal surface, from which the hydrate grew upwards to the outside of the groove and then to the water-gas interface. It clearly demonstrated the inhibition effects of light oil on the methane hydrate growth, which was further enhanced by adding asphaltenes. The inhibition mechanisms were elaborated during different stages of hydrate growth by gaining insights into the four-body order parameters at different locations of metal surface. To our knowledge, this was the first molecular-level study on the methane hydrate evolution on metal surface with heavy oil. It provided theoretical supports for evaluating the priority sites for the hydrate formation on pipe wall with concave and convex surfaces that might result from metal corrosion, change of pipe diameter or depositions of solid particles during the multi-phase transportation. [ABSTRACT FROM AUTHOR]

Irradiation of high-energy electrons can produce surface vacancies on the exit surface of thin foils by the sputtering of atoms. Although the sputtering randomly occurs in the area irradiated with an intense electron beam of several hundred nanometers in diameter, characteristic topographic features can appear under irradiation. This paper reviews a novel phenomenon on a self-organization of nanogrooves and nanoholes generated on the exit surface of thin metal foils irradiated with high doses of 360–1250 keV electrons. The phenomenon was discovered firstly for gold irradiated at temperatures about 100 K, which shows the formation of grooves and holes with widths between 1 and 2 nm. Irradiation along [001] produces grooves extending along [100] and [010], irradiation along [011] gives grooves along [100], whereas no clear grooves have been observed for [111] irradiations. By contrast, nanoholes, which may reach depths exceeding 20 nm, develop mainly along the beam direction. The formation of the nanostructures depends on the irradiation temperatures, exhibiting an existence of a critical temperature at about 240 K, above which the width significantly increases, and the density decreases. Nanostructures formed for silver, copper, nickel, and iron were also investigated. The self-organized process was discussed in terms of irradiation-induced effects.

The design of anti-reflective coatings for metal surfaces of Al, Ti, N,i Cr is proposed. The coatings have the form of alternating layers of dielectric/metal/dielectric with the number of cells up to15. The method of calculation of such coatings is proposed. We have calculated the coatings of the type [HfO2/Cr/HfO2]15, [ZrO2/Ti/Al2O3]15, [ZrO2/Cr/ZrO2]15. It is shown that the proposed interference coatings provide reduction of the residual reflectance of the metal several times (from 3.5 to 6.0) in a wide spectral range (300-1000 nm). The proposed coatings can be recommended as anti-reflective coatings for energy saving solar systems and batteries, and photovoltaic cells.

There is need to determine the wear rates of disc mill hammer used for grinding groundnut cake, a major plant protein in fish feed. This surface wear damage characterised by scoring, cutting, deep grooving and gouging on a metal surface leads to high costs of production. The hammer wear rate was carried out using disc mill for different combinations of processing conditions: disc speed of 2,175, 3,900 and 4,350 rpm; screen size of 1.0, 2.0 and 3.0 mm; moisture contents of 12, 14 and 16% w.b. at 300 operating hours. Response Surface Method was used to optimize the operating variables. The wear rate was found to increase as the moisture content of the groundnut cake decreased. Quadratic models developed for the four responses (tip length loss, width loss, thickness loss and absolute mass) studied indicated the optimum conditions at disc speed 3,262.50 rpm, screen size 2.0 mm and moisture content 14% w.b. The study established that experimental data and model predictions agreed well.

The work investigates some tribological parameters of the surfaces of friction pairs made of steel, with mineral layers. A comparative study of the friction coefficient of 18CrNiMo7-6 steel specimens with and without a mineral coating is performed. The lowest friction coefficient value was achieved for samples with mineral coating, without HFC hard surfacing before creating a mineral layer, and it was approximately 15% lower in comparison with the samples with HFC hard surfacing, but without mineral coating. The friction coefficient in the temperature range of 30..140 °C, with constant displacement speed of samples with mineral coatings is practically unchanged for every sample type, the variation range does not exceed 0.02, as contrasted with the friction coefficient of the samples with hard surfacing, but without mineral coating. The value of the coefficient of volumetric wear of an aluminum oxide toroid during rotation with disks with some types of mineral coatings was at the level of detection limit (less than 1.2x10-9 mm3/Nm), which is significantly less than the coefficient of volume wear of a toroid during rotation with disks without coating (4-7)X10-8 mm3/Nm. [ABSTRACT FROM AUTHOR]

This study reports the relevance of alkyl substituents on the chemisorption of alkylaromatics on metal surfaces, namely Pt(1 0 0) and Ni(1 0 0). Density functional theory (DFT) based calculations are used to calculate the chemisorption energies of benzene, methylbenzene (toluene), 1,2-, 1,3-, and 1,4-dimethylbenzenes (xylenes), and 1,3,5-trimethylbenzene (mesitylene), and ethylbenzene on Pt and Ni. Removal of hydrogens from the adsorbed benzene substantially reduces the stability of the adsorbed species on both Pt(1 0 0) and Ni(1 0 0) surfaces. Methyl substituents on the benzene ring, i.e., toluene, xylenes and mesitylene, result in exothermic dissociation of a C H bond from each methyl substituent on both Pt and Ni surfaces. This leads to formation of CH 2 groups connected to the aromatic ring, resulting in a π-conjugated system stacked over the metal surface, and expanding over the entire molecule. The energy gain associated with dehydrogenation of each hydrogen from the methyl groups cumulates, so that complete π-conjugated configuration by dehydrogenation of the methyl groups of xylenes and mesitylene is expected. Further removal of hydrogen from aromatic rings of the π-conjugated system is always endothermic on both surfaces. The study is extended to ethylbenzene on the same surface, and results suggest that dehydrogenation of methylene group followed by dehydrogenation of the methyl group, leading to formation of adsorbed styrene is favored. These results may justify more explicitly the essential role of cofed (or recycled) hydrogen to produce alkylaromatics in practical applications, enabling to recover sp 3 alkyl substituents to desorb the products. Although the actual catalysis involves more complexities on other types of metal sites, such as edges and kinks, the current study already demonstrates one significant aspect of the hydrogenation-dehydrogenation catalysis on metal surfaces. [ABSTRACT FROM AUTHOR]

By using the semi-classical open orbit theory, the effect of the laser polarization direction on the detached electron flux of H− in electric field near metal surface has been studied. The result suggests that the azimuth angle has little effect on the electron flux distribution. We find that the influence of polarization angle θL on electron flux distribution is more obvious than the influence of the azimuth angle . In other words, the effect of the polarization angle θL on electron flux distribution is more important. We hope that our study will provide a new understanding of the electron flux distribution of negative ion in external field and surfaces, and can be used to guide the future experiment research on the negative ion photo-detachment microscopy. [ABSTRACT FROM AUTHOR]

The design and development of a plastic optical fiber (POF) macrobend temperature sensor is presented. The sensor has a linear response versus temperature at a fixed bend radius, with a sensitivity of . The sensor system used a dummy fiber-optic sensor for reference purposes having a resolution below 0.3 °C. A comprehensive experimental analysis was carried out to provide insight into the effect of different surrounding media on practical macro-bend POF sensor implementation. Experimental results are successfully compared with bend loss calculations.

The field research results of the austenitic steel wear under the droplet impact erosion are presented. The stages of the erosive wear origin and development are considered. The leading part of the side cracks in the forming and growth of the erosion fracture craters, as specified in Marshall-Lawn model, is shown by the high-resolution scanning electron microscopy technique.

In this research, the life expectancy of ball bearings in industrial applications is estimated based on known parameters. The overall mathematical calculation of such behavior is based on the theory of Lundberg and Palmgren. The proposed life estimation equation however lacks certain points to make it qualified as universal. A firm conclusion therefore could not be obtained on the basis of this equation alone, particularly when different operating conditions are involved. One such example is the life of ball bearings while operating in clean lubricant environment, which is approximately up to 20 times longer than the calculated life based on the previously prescribed equations. On the other hand, active life under contaminated lubricants is nearly close to one-tenth of the calculated life originally thought to be correct .

Lasers are used in industry in a large variety of applications. These applications can mainly be divided into two categories: one specifically being materials processing. Material processing includes cutting, drilling, welding, etc., and generally involves the use of high-powered lasers. Laser applications such as rust/dye removal and surface cleaning are not yet very common for the industry. This paper reports experiments on industrial applications of a low-power fiber laser (20 W). Experimental studies showed that a low-power fiber laser could be used for a variety of applications such as surface cleaning of metals and rust-dye removal. Optimum laser parameters were also determined for these applications. [ABSTRACT FROM AUTHOR]

In order to quantify chemical quenching effect of metal wall surfaces, a methane-air premixed flame formed in narrow quartz plate channels is investigated. Stainless steel321 (SUS321) and Inconel600 are chosen as the surface materials for their high oxidation/heat resistivity. Thin films of SUS321 and Inconel600 are deposited on the quartz plates using a vacuum arc plasma gun to realize equivalent thermal boundary condition with different surface reactions. In addition, two types of alumina thin films are prepared using atomic layer deposition (ALD) and sputtering techniques to explore the surface roughness effect. Microscopic OH-PLIF and numerical simulation with detailed chemistry are employed to examine the near-wall flame structures. When the wall temperature T w ≧ 1073 K, OH concentrations near the SUS321/Inconel600 surfaces are significantly lower than that near the quartz surface. Based on the PLIF data, the initial sticking coefficient S 0 associated with radical adsorption is estimated to be 0.1 and 0.01 for the SUS321/Inconel600 and quartz surfaces, respectively, indicating stronger radical quenching effect on these metal surfaces. On the other hand, OH profile near the sputtered-alumina surface, which has larger roughness, is in good accordance with that near the ALD-alumina surface. This implies that the radical adsorption is independent on surface roughness. [ABSTRACT FROM AUTHOR]

The manipulation of a single atom by a tip is a well-studied and effective means to fabricate supported nanostructures. However, in practice, atom-by-atom manipulation adds complexities and difficulties. We propose a method for obtaining different structures of supported nanocluster by placing the tip at a certain height above the cluster. Pt-supported nanoclusters can be changed to a three-dimensional structure. Analysis of nearest neighbor bonds is used to explain the change. [ABSTRACT FROM AUTHOR]

Metal matrix composite materials have wide and various applications in modern industry. Some of them require the deposition of a ceramic coating. This paper reports a new approach to formation of surface metal composites based on silicon carbide using an overlaying technology. The latter is based on the application of an arc which is formed between the metal surface acting as an anode and C-welding rod acting as a cathode. When the arc is started the cathode is heated and releases a stream of electrons of a temperature of ca 4000 K bombarding the anode. Partial melting of the surface layer and formation of a coating of silicon carbide is achieved by adjusting the advance distance between the surface and the electrode as well as the current applied. The metal melt covers the reinforcing phase particles and a solid structure is obtained. The realization of the carbothermal reduction requires the deposition of a coating of a silicon dioxide on the metal surface. Its location and size, as well as the border between carbide layer and the metal surface are determined by microscopic analysis. [ABSTRACT FROM AUTHOR]

In fabrication of nano- and quantum devices, it is sometimes critical to position individual dopants at certain sites precisely to obtain the specific or enhanced functionalities. With first-principles simulations, we propose a method for substitutional doping of individual atom at a certain position on a stepped metal surface by single-atom manipulation. A selected atom at the step of Al (111) surface could be extracted vertically with an Al trimer-apex tip, and then the dopant atom will be positioned to this site. The details of the entire process including potential energy curves are given, which suggests the reliability of the proposed single-atom doping method. [ABSTRACT FROM AUTHOR]

• Computational and theoretical approaches are necessary to substantiate the experimental results. • Phytochemicals in a plant extract can be utilized to define and classify how plant-based inhibitors control corrosion. • DFT calculations supported the donor–acceptor adsorption and inhibition mechanism for plant extract. • Fukui analysis evidenced the adsorption-rich site of the atoms in the phytochemicals. • Surface orientation and adsorption of inhibitor molecules onto a metal surface can be studied using MD and MC simulations. The most significant component of metallic corrosion control in aqueous conditions is examining the adsorption mechanisms of inhibitor molecules at the metal-electrolyte interface. The use of computational techniques for metal corrosion inhibition research has progressively increased due to time, economic, and environmental constraints, and the inadequacy of experimental techniques to provide meaningful insight into metal surface-inhibitor interaction. Even though there is a wealth of research that employs computational evaluation, there is a lack of a comprehensive review, specifically to investigate the adsorption behavior of phytochemicals on metal substrates. There is still uncertainty regarding the techniques and steps required in Molecular Dynamics (MD) modeling for corrosion systems. The fundamental principles of MD simulation, adsorption mechanism, quantum calculations as well as summaries of major publications in anticorrosive studies are discussed in this paper. To substantiate the experimental results, this review discovered that computational and theoretical approaches are necessary. [ABSTRACT FROM AUTHOR]

Understanding Extracellular Polymeric Substances (EPS) interaction on a well-defined chromium surface is of importance especially for biocorrosion processes. Adsorption of EPS extracted from Pseudoalteromonas NCIMB 2021 on Cr surfaces was investigated using in situ quartz crystal microbalance (QCM) and X-ray photoelectron spectroscopy (XPS). We show that EPS adsorption is an irreversible process. The amount of adsorbed EPS increases with increasing EPS concentration in solution. For low EPS concentration, the surface is only partially covered by EPS, whereas a continuous organic film of around 15 nm is formed at the surface for high EPS concentrations. An in-depth structuration of this organic layer is evidenced with a strong enrichment of proteins in the inner part and of polysaccharides in the outer part. Adhesion of Pseudoalteromonas NCIMB 2021 has been tested on Cr surfaces covered or not by EPS extracted from Pseudoalteromonas NCIMB 2021. EPS conditioning with a 15 nm film inhibits bacterial adhesion on Cr, showing that this organic film has efficient anti-adhesive properties. [Display omitted] • Irreversible adsorption of EPS extracted from Pseudoalteromonas on Cr surfaces. • Chemical composition of EPS not maintained after adsorption on the Cr surface. • In-depth structuration of the film: proteins inside and polysaccharides outside. • EPS conditioning with a 15 nm film inhibits bacterial adhesion on Cr. [ABSTRACT FROM AUTHOR]