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In order to study the effect of laser-excited surface waves on the transmission of surface rectangular defects, the transmission information of surface waves is used to achieve the quantitative calculation of defect depth. In this paper, the finite element method is employed to analyse the transmission process of laser-excited surface waves on rectangular defects. Subsequently, the influence of the front and back edges of rectangular defects on the transmission of surface waves is examined, and the formation mechanisms of feature points in the time-domain signals at detection points are elucidated. Ultimately, the calculation formula for defect depth is derived on the basis of the propagation path of each component after transmission. The experimental results are consistent with the simulation results, and the relative error of the surface rectangular defect depth calculation is within 9%. The validation of the proposed method on homogeneous isotropic metallic materials demonstrates the effectiveness and applicability of the proposed method. These results will provide a reliable method for characterising the depth of rectangular defects on surfaces by laser ultrasonic technology. [ABSTRACT FROM AUTHOR]

The optical properties of quantum dots are significantly influenced by photoactivation. However, the progress in the development of photoactivation carbon dot (CDs) is still stagnant. Herein, a method for preparing photoactivated CDs/NiFe2O4 by introducing affluent defects onto the surface of CDs through modification with NiFe2O4, extremely filling the gap of photoactivated CDs is proposed. Interestingly, a fantastic fluorescence detection strategy in a unique “OFF‐ON‐OFF” mode, combined with Ag+‐assisted fluorescence enhancement, to establish a dual‐channel regulation mechanism for ultra‐sensitive detection of Hg2+ is first proposed. By increasing surface traps and non‐radiative recombination, defect‐induced manipulation on the surface of CDs creates favorable conditions for enhancing photoactivation capacity. Unexpectedly, the CDs/NiFe2O4 shows a 25‐fold enhancement in fluorescence intensity after exposure to UV‐lamp. Compared to CDs/NiFe2O4, Ag+‐assisted photoactivation CDs/NiFe2O4 exhibits superior selectivity and sensitivity for Hg2+ detection, with low detection limits of 0.7 nM. Moreover, TiO2@CDs/NiFe2O4 catalyst is successfully prepared by anchoring CDs/NiFe2O4 onto the surface of TiO2, which demonstrates efficient degradation of tetracycline within 90 min under visible light irradiation, with a degradation rate of 98.3%. This work first provides a novel defects modification strategy for developing highly active photoactivated CDs technology that is significant for future environmentally sensitive applications. [ABSTRACT FROM AUTHOR]

Grain boundaries (GBs) and surface defects within perovskite films are inherent challenges that affect the photovoltaic performance of perovskite solar cells  (PSCs. In this work, Nylon 11 (N11) is utilized, a long‐chain polymer, for post‐treating the GBs and surface defects within FAPbI3 films. The multifunctional groups of N11 exhibit unique passivation abilities, enabling self‐regulation and selective correction of reverse‐charged defects. Post‐treating with N11 results in high‐quality FAPbI3 films characterized by tight GBs and low surface defect density. Despite fabrication under full open‐air conditions, the N11 post‐treatment significantly enhances the power conversion efficiency (PCE) value of FAPbI3 PSCs, increasing it from the reference value of 21.89% to 23.54%. Importantly, the long alkyl chain present in N11 significantly enhances the humidity stability of the PSCs. Unencapsulated PSCs treated with N11 maintain 89% of their initial PCE after exposure to air with 30% relative humidity (RH) for 1000 h, demonstrating resilience to elevated humidity levels. This work highlights the substantial improvement in the photovoltaic performance of PSCs achieved through the post‐treatment with N11. [ABSTRACT FROM AUTHOR]

Ball burnishing (BB) is a surface finishing process that involves moving the tool while pressing it against the workpiece under rolling contact. We investigated the effects of BB on the fatigue strength of an aluminum alloy with a semicircular slit simulating an initial surface defect. After performing BB under a typical condition, compressive residual stress was induced to a depth of 0.4 mm, with a reduction in surface roughness by 81%. Consequently, the fatigue strength increased by 22%. BB prevented the fatigue strength reduction caused by the 0.1 mm deep slit. The result indicated that BB could render the below 0.1 mm deep surface defect harmless in terms of fatigue strength. The maximum depth of surface defect that can be rendered harmless is 0.18 mm according to fracture mechanics, which is in agreement with the experimental results. Highlights: Ball burnishing (BB) can introduce large compressive residual stress along with smoothing.BB can render a 0.1 mm deep slit harmless in terms of fatigue strength.Defect size rendered harmless can be estimated based on fracture mechanics.Compressive residual stress is the main factor rendering the surface defect harmless. [ABSTRACT FROM AUTHOR]

The surface of superalloy precision castings might exhibit defects after forming, posing a significant risk to their service life, necessitating inspection during post-process. Radiographic inspection, with its extensive research in automation, can achieve efficient and accurate detection of defects. However, it is limited in surface defects detection due to limited sensitivity to non-volumetric defects and high cost. In contrast, fluorescent penetrant inspection (FPI) is highly efficient for surface defect inspection due to its low cost, high sensitivity, and speed. However, manual examination introduces variability in the results, impacting the consistency and reliability of the inspection process. Automation is needed to ensure consistency and reliability of inspection. The implementation of an automated defect identification system based on FPI using convolutional neural networks (CNNs) was systematically investigated. Among the CNN models tested, MobileNetV2 exhibited exceptional performance, achieving a remarkable recall rate of 0.992 and an accuracy of 0.992. Additionally, the effect of class imbalance on model performance was carefully examined. Furthermore, the features extracted by the model were visualized using Grad-CAM to reveal the attention of the CNN model to the fluorescent display features of defects. This study underscores the strong capability of deep learning architectures in identifying defects of precision casting components, paving the way for the automation of the entire FPI process. [ABSTRACT FROM AUTHOR]

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Copyright of Steel Pipe is the property of Steel Pipe Magazine and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Steam reforming for bio-oil and its derivatives is a highly promising method for hydrogen production. The development of low-price, highly active and stable catalysts is crucial for this technology. The Ni/CeO 2 with three morphologies were prepared in this work. The obvious differences in surface defects and oxygen vacancy concentrations of catalysts with different morphologies were confirmed by Raman and XPS. The Ni/CeO 2 -R with the highest surface defects and oxygen vacancy concentrations possessed the greatest catalytic performance. The highest hydrogen yield (72.01 %) and carbon conversion rate (86.61 %) were achieved over Ni/CeO 2 -R at 600 °C with the complete conversation of acetic acid. In addition, Ni/CeO 2 -R possessed the lowest apparent activation energy, the highest hydrogen production rate, and turnover frequency within 400–600 °C. More importantly, Ni/CeO 2 -R maintained stability during a 15-h durability test. This work would provide references for the design of catalysts for SRAA in the future. [Display omitted] • CeO 2 with specific morphologies were prepared using a simple hydrothermal method. • The change in morphologies enhances the oxygen vacancy concentrations of catalysts. • The change in CeO 2 morphologies enhances the activity and stability of catalysts. • The catalytic performance of Ni/CeO 2 -R is higher than that of common oxide supports. [ABSTRACT FROM AUTHOR]

Hot-rolled strip steel is a material widely used in production activities and daily life. However, the appearance of surface defects during its production process is inevitable. To address this issue, we introduce a new detection method using Gold-Yolo to detect surface defects on hot-rolled strip steel. Our method effectively balances accuracy and real-time performance while detecting four common types of surface defects, achieving an average accuracy rate of 82,2 % for detecting individual types of surface defects. Experimental data prove that our method excels in classifying and locating surface defects on hot-rolled steel strip, demonstrating broad application prospects and promotional value. [ABSTRACT FROM AUTHOR]

In response to the inevitable surface defects in the manufacturing process of hot-rolled steel, this paper proposes an improved steel surface defect detection model based on YOLOv7. In the Extended Efficient Large Aggregation Network (E-ELAN), the model replaces conventional convolution with Omni-Dimensional Dynamic Convolution (ODConv) to enhance the network's sensitivity to feature extraction using a combination of various attention mechanisms. Additionally, the detection head in the head section is replaced with an Efficient Decoupled Detection Head, enhancing the model's capability to classify and locate small defects. The proposed model is tested on the public dataset NEU-DET, achieving a high mAP of 76,5 %. This effectively enhances the model's ability to detect surface defects in steel while maintaining a fast detection speed. [ABSTRACT FROM AUTHOR]

The quality control of steel products' surface is of utmost importance, where several inspection techniques and technologies have been proposed over the last few years. Traditional manual inspection procedures face several limitations and often fall short in ensuring flawlessness. Vision-based strategies for automatic steel surface inspection have emerged as powerful and effective tools to solve various industrial-related problems, including product quality control. Therefore, the current study aims to improve the recognition rate of steel surface defects classification systems by introducing a novel classifier combination approach. The proposed system utilizes two distinct feature sets, namely LCCMSP and DCP, which were carefully selected based on a comprehensive comparative study of 19 state-of-the-art texture descriptors, considering both accuracy and time consumption. These generated features are individually fed to two classifiers, SVM and RF, leading to the creation of four base classifiers. In the final step, the Bayesian fusion rule is applied to integrate the outputs of these classifiers, ultimately providing the definitive classification decision. To evaluate the proposed system, two steel surface defects datasets, NEU-CLS and X-SDD, are utilized. The experimental results demonstrate that the proposed combination approach surpasses classical combination methods achieving remarkable outcomes compared to existing steel surface defects classification approaches. This highlights the effectiveness and superiority of the proposed system in accurately identifying and classifying steel surface defects while maintaining fast execution time. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy of surface defect detection of high temperature casting slab, an improved YOLOv5s surface defect detection algorithm is proposed. Firstly, Swin Transformer network structure is added to enhance the ability of feature extraction.Secondly, a coordinate attention mechanism is introduced to increase the sensitivity of position and direction information. Finally, a target detection layer is added to better realize feature fusion and enhance the generalization ability of the network. The improved algorithm has performed ablation experiments on the data set, which shows the effectiveness of the algorithm.

[Objectives]Aiming at the low detection accuracy of apple surface defects in the process of non-destructive testing, an apple surface defect detection method based on DSCS-YOLO was proposed. [Methods]Firstly, in order to improve the network's ability to extract detailed features of surface defects, a deep and shallow feature selection module DSCS based on Dense and SE modules was designed. The C3 module in Backbone was replaced by DSCS, which strengthened the learning of important features and weakened redundant features on the basis of retaining superficial information of surface defects. In order to solve the problem of parameter coupling caused by excessive output information of Backbone and Neck, the Decoupled Head principle was used to make hierarchical prediction of Head layer. At the same time, the ELU activation function was used to improve the original Decoupled Head, simplify the terminal structure, and make the network training easier. Finally, to solve the difficulty of labeling surface defects, Wise-IoU loss function was used to replace CIoU loss function, which provided nonlinear gain for labeling of different qualities and realized dynamic focused learning of the network. [Results]DSCS-YOLO improved the detection of small targets, and the mean average precision of the apple surface defect test set reached 90.9%, which was 4.5%, 1.9%, 6.3% and 16.3% higher than that of YOLOv3-tiny, YOLOv5s, YOLOX-s and SSD, respectively, showing the best detection effect. At the same time, the number of model parameters was 9.54 M, and the inference time was only 2.8 ms. The detection speed was fast, which could meet the needs of practical applications. [Conclusions]The improved DSCS-YOLO improved the accuracy of YOLOv5s algorithm and realized the accurate identification of apple surface defects. [ABSTRACT FROM AUTHOR]

A mathematical model of defects of the inner surface of a ferromagnetic plate is presented. The model compares crack-type defects with wide-opening corrosion defects. [ABSTRACT FROM AUTHOR]

The corrosion steels phenomenon is one of the main problems in the oil industry, such as in buried transmission pipelines used for high gas pressure for long distances. Steels are protected from the external soil corrosion through a bituminous coating, whose action is coupled with a cathodic protection system, which aims to maintain steel in its protection field and thus to avoid any corrosion risk. However, steels in service may experience external surface defects like corrosion pitting and cracking due to electrochemical or mechanical interactions of bare steel with an aggressive soil solution after steel protection failure. These are concerning phenomena and are the major threats of the pipeline transmission system's reliability and ecological safety. Corrosion mechanisms are varied and can be evaluated by different methods, such as electrochemical measurements, which are influenced by various factors like temperature, pH, soil characteristics, resistivity, water content, and as well mechanical stresses. Corrosion results from simulated artificial soil solutions showed that steel is sensitive to corrosion by soil. Surface defects detection was carried out using an ultrasonic non-destructive method such as C-Scan Emission testing and the time of flight diffraction technique (TOFD) ultrasonic non-contact testing method. After propagation of the ultrasonic waves, the diffracted ultrasonic reflected wave occurring at the edges of the defects appears due to the presence of a corrosion defect by generating defect echoes. The C-Scan ultrasonic image shows surface reflection, including corrosion defects on interfaces with varying acoustic impedances. The cross-transverse speed ultrasonic propagation through the plate including defect is modified, revealing more surface defects, and cross-transverse speed is shown to increase ultrasonic detection presents some advantages, such as precision and speed of detection without alteration to the structure. This method can be used in the industrial context as an intelligent industrial robotics technique. [ABSTRACT FROM AUTHOR]

In the present work, the influences of surface defects on the motion of magnetic vortices in a mesoscopic type-II superconductor with randomly distributed pinning centers are considered using the time-dependent Ginzburg-Landau equations. Two kinds of surface defects are located in the boundary: first, the pinning centers. and second, the geometric defects simultaneously with pinning centers. In the simulation, the magnetization curves, vorticity, and the density of superconducting electrons for both different contents of pinning centers and various geometric defects are analyzed. For the pinning centers as surface defects, the maximum magnetization values as a function of the contents exponentially decrease, and the field H 1 where the first vortex penetrates and the field H 2 where the complete transition from superconducting to a normal state in the system occurs is reduced. For the geometric defects as surface defects, the density of superconducting electrons and the magnetization curves depend on both size and form of them. In addition, a threshold on the size of geometric defects in which the motion of vortices and vorticity changes is presented. [ABSTRACT FROM AUTHOR]

AbstractDesigning structural grillages is an innovative solution to repair material surface defects. The grillages have advantages as lightweight and directionally enhanced, but their stability and stress concentration under dynamic loads may pose significant risks. Three analytical models of grillages above a semicircular defect are constructed. The analytical solution of the frequency-domain wave field around the semicircular defect under SH wave incidence is solved. The complex variable function method is introduced to simplify the wave field expression. The accuracy is verified by comparing it with classic examples. Applying the Modified Time-Frequency Transform to obtain the time-domain wave field. Based on the Multipoint Transient Input Method (MTIM), the transient surface displacement response of the grillage above the defect is derived, mainly discussing the superposition behavior of input waves in the grillage. MTIM is based on the analytical solution of local time-domain wave field and takes the time histories of the surface characteristic points as the wave input. It has been validated as an effective method for studying the dynamic response of structures. The numerical results show that grillage form, incident angle, and wave velocity ratio are important factors affecting the structural responses of grillage above the defect. This research aims to reveal the propagation characteristics of waves in grillages in filling material defects, providing theoretical guidance for the design and application of grillages in this field. [ABSTRACT FROM AUTHOR]

Small steel bars (SSBs) are common steel products, and their surface defects pose a significant quality issue. Despite numerous studies conducted to detect and predict such defects, identifying the underlying causes, or 'quality factors,' remains a challenge. Most of the existing methods focused on validation of known factors based on the theories of physical properties and material composition; thus, they lack exploring new quality factors. This study proposes a two-step method for identifying these quality factors, considering two common issues that often arise when analyzing operational data in SSB manufacturing processes: 'merged measurement' and 'variation due to operation dates.' These issues are resulted because the rolling process is lack of measurement capability and exposed to external environment. They make it difficult to use various analytical methods such as statistical analysis and data mining methods. The proposed method sequentially employs statistical difference testing and k-means clustering, thereby providing a practical heuristic for overcoming the aforementioned difficulties. The effectiveness of the proposed method was demonstrated through a case study that highlighted its ability to effectively identify the quality factors critical to surface defects on SSBs. [ABSTRACT FROM AUTHOR]

In response to the inevitable surface defects in the manufacturing process of hot-rolled steel, this paper proposes an improved steel surface defect detection model based on YOLOv7. In the Extended Efficient Large Aggregation Network (E-ELAN), the model replaces conventional convolution with Omni-Dimensional Dynamic Convolution (ODConv) to enhance the network’s sensitivity to feature extraction using a combination of various attention mechanisms. Additionally, the detection head in the head section is replaced with an Efficient Decoupled Detection Head, enhancing the model’s capability to classify and locate small defects. The proposed model is tested on the public dataset NEU-DET, achieving a high mAP of 76,5 %. This effectively enhances the model’s ability to detect surface defects in steel while maintaining a fast detection speed.

Hot-rolled strip steel is a material widely used in production activities and daily life. However, the appearance of surface defects during its production process is inevitable. To address this issue, we introduce a new detection method using Gold-Yolo to detect surface defects on hot-rolled strip steel. Our method effectively balances accuracy and real-time performance while detecting four common types of surface defects, achieving an average accuracy rate of 82,2 % for detecting individual types of surface defects. Experimental data prove that our method excels in classifying and locating surface defects on hot-rolled steel strip, demonstrating broad application prospects and promotional value.

Surface defects which are caused by diamond fly-cutting can degrade the optical characteristics of KDP crystals by inducing the laser damage and resultantly shortening the service life when working in the condition of extremely high-power laser irradiations. The ball-end milling repair can totally remove the defects and correspondingly enhance the ability of the KDP crystals to resist laser damage. However, because of the existence of pull-milling, push-milling, down-milling, and up-milling in the complex machining trajectory, it is of great challenge for soft-brittle KDP crystals to achieve full plastic domain milling of repair points for obtaining ultra-smooth repaired surface. In this study, theoretical models of the maximum uncut chip thickness (UCT) considering the defect are constructed in the various milling modes. The cutting force and surface morphology affected by milling modes on the defective surface are simulated using a three-dimensional (3D) finite element model. Combined with the simulations, micro-milling tests are employed to confirm the influence of milling modes on the material removal mechanisms for defective KDP crystals by cutting force, surface morphology, surface roughness and specific cutting energy (SCE). It is found that defects could reduce the maximum UCT to distinct degrees in the various milling modes. Defects have a greater influence on the maximum UCT in the push-milling mode than that in the pull-milling mode, while the influence is the same in the up-milling and down-milling modes. Interestingly, the ductile removal mode occurs on the both defect-free and defective surfaces in the pull-milling mode when the feed per tooth (f z) is 0.5 µm/z. In contrast, the brittle-ductile transition caused by surface defects occurs in the other three milling modes. Furthermore, defects would lead to severer size effect in the all milling modes. When the f z is 0.1 µm/z, for the defective surface, the SCE is larger and the ploughing effect is more significant in the push-milling mode compared to the pull-milling mode. More importantly, as a result of the difficulty of chip discharge, the SCE is greater in the up-milling mode than in the down-milling mode on the defective surface. This work reveals the influence mechanism of milling modes on the material removal process for defective KDP crystals and offers a theoretical foundation and technical value for improving the repaired surface quality of functional KDP crystals served as the optical devices. [Display omitted] • Effect of milling modes on cutting mechanisms of defective KDP crystal was explored. • The models of maximum uncut chip thickness considering defects were established. • Surface defects could cause the brittle-ductile transition and severe size effect. • Pull-milling was the easiest to obtain ultra-smooth surface of defective KDP crystal. [ABSTRACT FROM AUTHOR]

Surface defect detection in industrial processes is crucial for ensuring product quality and reducing material waste. Automated defect identification using deep learning techniques has become a vital aspect of the automated surface defect detection field. However, achieving accurate and automatic defect segmentation remains a significant challenge, especially for fine precision segmentation required in high-quality products. The traditional approaches for defect segmentation have several limitations, such as difficulty in preserving fine details and contextual information, leading to poor segmentation performance. To overcome these limitations, new segmentation algorithms that can preserve fine precision and contextual information need to be evaluated. Therefore, there is a need for novel segmentation algorithms that can accurately identify and segment defects in industrial processes, incorporating multi-scale contextual information, preserving fine details, and handling complex and subtle defects. In this paper, we propose a novel approach for steel defect segmentation called multi-scale cross-patch attention with dilated convolution (MCPAD-UNet). This approach employs a subsampled module that achieves the same dimensionality reduction as max-pooling while preserving the fine precision of the features. Additionally, MCPAD-UNet utilizes a cross-patch attention module with dilated convolution, simultaneously collecting channel–spatial data and integrating relevant multi-scale features to reduce the semantic gap and enhance detailed information. To prevent overfitting, we apply dropout after each hybrid dilated convolution block. Extensive testing on the public Severstal: Steel Defect Detection dataset demonstrates the effectiveness of our approach, achieving Dice scores of 95.3%, outperforming the competition's overall score by 5.2%. Our proposed method has the potential to significantly improve defect detection in industrial processes, thereby reducing material waste and improving product quality. [ABSTRACT FROM AUTHOR]

The surface defect of navel orange is one of the significant factors that affects its price. At present, most of surface defect detection algorithms for navel orange have disadvantages of slow speed, massive calculation and low efficiency, making it difficult to meet the needs of automated detection. This article proposes an improved image matching method on navel orange surface defect detection which combines wavelet transform (WT) and speeded up robust features (SURF) based on compressed sensing (CS). Firstly, do some pre-treatment on the navel orange images such as de-noising, compression and so on, then decompose the image by wavelet transform based on compressed sensing technology, and obtain the low frequency sub-image and extract SURF features of the image, next compare the extracted SURF feature with feature library, search for the maximum matching value of the similarity measurement values, and output the recognition results. The algorithm ensures better recognition accuracy and efficiency, and achieves rapid identification of navel orange defects. [ABSTRACT FROM AUTHOR]

Rational defect regulation is an effective way to enhance the performance of photoelectrocatalytic (PEC) water splitting. In this paper, we firstly constructed internal and surface defects to modulate the crystal structure of CdS, and investigated the synergistic impact of 0D (0‐dimensional) internal and surface defects regulation coupled with pyroelectric effects for optimizing the photoelectrocatalytic properties of CdS. It was found that the synergistic impact had a significant enhancement effect on the carrier separation and transfer, and the current density reached 3.93 mA/cm2 at 1.23 V vs. RHE increased by 16.38 times, meanwhile, the stability of the photoelectrodes was greatly promoted. The advantages and intrinsic relationships are also described: the formation of 0D dual‐type of defects alters the atomic arrangement in the crystal, optimizes the energy band structure, reduces carrier recombination, and increases carrier density. What's more, the introduction of defects induces electron redistribution and changes the state of dipoles inside the crystal, thus increasing built‐in electric field and generating more thermally generated carriers to optimize the pyroelectric effect. This work demonstrates the feasibility of defect modulation for optimizing pyroelectric performance and photocatalytic performance and bringing new light to PEC water splitting. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The application of copper-clad steel having the advantages of both copper and steel as a grounding material in substation engineering is increasing gradually. The integrity of copper coating is the key indicator to determine its service life. Once the steel matrix is exposed due to the destroy of copper coating, the corrosion of the steel underneath will be accelerated and then the service life of copper-clad steel will be shortened. Based on the color development reaction for determination of iron ions, an indicator composed of aqueous acetic acid solution, fatty alcohol polyoxyethylene ether (AEO), and o-phenanthroline was prepared for rapid detection of copper coating failure in copper-clad steel on the field. The indicator has high sensitivity, distinct color change, good response to various types of defects, and environmental friendliness. The failure of copper coating on copper-clad steel can be determined rapidly by visual check. [ABSTRACT FROM AUTHOR]

The issues of classification and characterization of surface operational defects according to the results of ultrasonic, eddy current, and visual inspection methods of nondestructive testing are considered. At the same time, the visual inspection method was realized with the use of a television inspection camera equipped with a computer vision function and a laser triangulation sensor. The paper presents a dataset containing 5760 images of pipelines with and without pitting corrosion. A convolutional neural network (CNN) is presented that was applied to classify the images obtained from the TV inspection camera into images without corrosion and images with pitting corrosion. The paper presents a dataset containing 269 measurements of planar and volumetric surface defects. A model for surface defect sizing based on gradient boosting is presented. The paper develops an algorithm for classification and sizing of surface defects in complex diagnostics in which the obtained models are applied, and determines the accuracy of this algorithm in the RMSE metric, which was calculated within the studied test dataset and amounted to 0.011 mm. [ABSTRACT FROM AUTHOR]

Hypo-peritectic steels are widely used in various industrial fields because of their high strength, high toughness, high processability, high weldability, and low material cost. However, surface defects are liable to occur during continuous casting, which includes depression, longitudinal cracks, deep oscillation marks, and severe level fluctuation with slag entrapment. The high-efficiency production of hypo-peritectic steels by continuous casting is still a great challenge due to the limited understanding of the mechanism of peritectic solidification. This work reviews the definition and classification of hypo-peritectic steels and introduces the formation tendency of common surface defects related to peritectic solidification. New achievements in the mechanism of peritectic reaction and transformation have been listed. Finally, countermeasures to avoiding surface defects of hypo-peritectic steels duiring continuous casting are summarized. Enlightening certain points in the continuous casting of hypo-peritectic steels and the development of new techniques to overcome the present problems will be a great aid to researchers. [ABSTRACT FROM AUTHOR]

In this work, magnetic responses from artificial corrosion defects were investigated using a differential fluxgate probe. Different rates of corrosion defects were prepared based on the electrolytic corrosion process on the surface of a carbon steel plate by manipulating the corrosion period. Then, the differential fluxgate probe, consisting of two miniature fluxgates arranged in a planar configuration and a circular excitation coil, was used to measure the real and imaginary components of magnetic responses from the corrosion defects in the range of 5 to 510 Hz. The real differential magnetic response from the probe showed that the magnetic signal change was proportionally increased with the corrosion period, revealing the increased depth of the corroded region with the electrolytic corrosion period. The magnetic response induced by an excitation field from 10 to 110 Hz could be utilized to enable a sensitive depth estimation of a corrosion defect on high-permeability steel at a sub-millimeter resolution. [ABSTRACT FROM AUTHOR]

Hot-rolled steel strips are a commonly used product in both production and daily life. However, the manufacturing process inevitably leads to the occurrence of surface defects. To solve this problem, Our method uses YOLOV8 and squeeze-and-excitation (SE) attention mechanism to detect surface defects in hot-rolled steel strips. Our method balances accuracy and real-time performance, while detecting four common surface defects. The method has an average accuracy of 90,9 % and a maximum accuracy of 98,5 % for detecting a single category of surface defects. Experimental results confirm good performance of our proposed method in classifying and localizing surface defects in hot-rolled steel strips, and has the potential for broad application and promotion.

The surface anomaly detection of train parts is one of the key technologies to ensure the safe operation of a metro system. Its core often lies in image processing. However, some anomalies do not exhibit significant changes in color, shape, and texture features, making detection difficult on 2D images. 3D point clouds add depth information on top of 2D images. This can help reflect the surface features of components more precisely and contribute to accurate anomaly detection. This paper addresses the common feature that most metro vehicle underbody components are rigid structures, combined with the 3D point cloud processing technology, and induces a general anomaly detection method. This method adopts point cloud preprocessing, point cloud registration, etc., and can detect changes in the depth information of the surface of subway underbody rigid structure components. However, it doesn't perform well when processing parts with high-ablaze reflective surfaces and mesh surfaces. Therefore, the paper uses the High Voltage Distribution Box (HVB) joint area and the hollow reactor oblique area as examples to improve this method. For the HVB joint area, regional segmentation is first used to obtain pure joint and wire harness regions; then, the statistical method is used to complete the detection of the former, and the general detection method is used to complete the detection of the latter. For the hollow reactor inclined surface area, plane filtering is used to adjust the process of the general detection method, eliminating the interference of noise points behind the grid plane and avoiding the problem of poor point cloud registration caused by plane filtering. Finally, using actual data from a metro company as a test set, it was verified that the new method, while ensuring a detection rate not lower than the original method, eliminates over 90% of false alarms, significantly reducing false detection rates. This offers theoretical guidance for the practical application of related anomaly detection systems.

Ductile cast iron pipes (DCIPs) are widely utilized in water and gas supply due to their exceptional mechanical and material properties. Quality control during production is paramount for optimal performance, with surface defect detection being of utmost importance. Currently, production sites primarily rely on manual visual inspection, which presents several challenges, including high labor intensity, low efficiency, and subjectivity in defect judgment. 2D detection algorithms based on single images struggle to effectively detect small defects such as pores, pinholes, and scratches, resulting in high missed detection rates and vulnerability to interference from oil and water stains. To address these issues, this paper proposes a symmetrical twin-light photometric stereo (TLPS) detection algorithm based on photometric stereo theory, and image correction algorithm for mitigating the effects of ambient light. A visual inspection prototype system was designed and successfully implemented at a production site. The system comprises eight visual inspection units, each equipped with a high-speed line scan camera and two bar light sources for image acquisition of moving DCIP. Under the Lambertian model and parallel light source assumption, the gradient expression in the texture direction of the DCIP surface is derived based on light source symmetry. Production experiment results demonstrate that the gradient map obtained through the proposed TLPS effectively enhances defect contrast and accurately detects and locates micro defects such as pores, pinholes, and scratches on cast pipe surfaces. This verifies the effectiveness of the TLPS algorithm, addresses the shortcomings of traditional 2D detection algorithms, and lays the foundation for subsequent online detection of DCIP surface defects.

Recently, hybrid injection molding—a type of overmolding technology in which a short carbon fiber-reinforced thermoplastic is injection molded over a compression-molded carbon fiber-reinforced thermoplastic (CFRTP) sheet—has been introduced. A heat-and-cool hybrid injection molding technique has also been introduced for practical use. The technique yields high-quality molded products. This is achieved through the heating of the mold cavity surface to a temperature higher than the melting point of the base polymer impregnated into the carbon fiber fabric. However, few experimental analyses of the molding phenomena in heat-and-cool hybrid injection molding have been reported. In particular, the effect of the processing conditions on the transfer of the mold cavity surface shape to the CFRTP sheet has not been clarified in detail. Therefore, it has been impossible to take extensive measures when defects are generated in molded products. In this study, a mold is designed and fabricated for use with far-infrared radiation heating, a variotherm technology that is suitable for the experimental analysis of the heat-and-cool hybrid injection molding phenomenon. In particular, a mold is designed and fabricated to continuously perform the following three processes using only an injection molding machine: (1) the radiation heating of both the CFRTP sheet and the mold cavity surface using a far-infrared radiation heater, (2) the compression molding of the CFRTP sheet, and (3) the injection molding of the melt. The effects of the heating conditions of the mold and the injection molding process conditions on the appearance characteristics of the molded products are clarified using this mold and a far-infrared radiation heater. [ABSTRACT FROM AUTHOR]

In the current industrial revolution, advanced technologies and methods can be effectively utilized for the detection and verification of defects in high-speed steel filament production. This paper introduces an innovative methodology for the precise detection and verification of micro surface defects found in steel filaments through the application of the Eddy current principle. Permanent magnets are employed to generate a magnetic field with a high frequency surrounding a coil of sensors positioned at the filament's output end. The sensor's capacity to detect defects is validated through a meticulous rewinding process, followed by a thorough analysis involving scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS). Artificial defects were intentionally introduced into a sample, and their amplitudes were monitored to establish a threshold value. The amplitude signal of these created defect was identified at approximately 10% FSH, which corresponds to a crack depth of about 20 µm. In the experimental production of 182 samples covering 38 km, the defect ratio was notably high, standing at 26.37%. These defects appeared randomly along the length of the samples. The verification results underscore the exceptional precision achieved in the detection of micro surface defects within steel filaments. These defects were primarily characterized by longitudinal scratches and inclusions containing physical tungsten carbide. [ABSTRACT FROM AUTHOR]

Fe-6.5wt%Si alloy possesses excellent electromagnetic properties, including high resistivity, low iron loss, high magnetic permeability, and almost zero magnetostriction coefficient, making it an ideal core material for low-noise and low-iron-loss applications. However, the higher silicon content promotes the formation of ordered phases B2 and D03, which significantly increases the material's brittleness. Traditional rolling processes are inadequate for producing Fe-6.5wt%Si alloy thin ribbons. Planar Flow Casting, on the other hand, has a high cooling rate, which can suppress the formation and growth of ordered phases in high-silicon steel, reduce the material's brittleness, and produce Fe-6.5wt%Si alloy thin ribbons. However, Fe-6.5wt%Si alloy ribbons produced by Planar Flow Casting have gaps on the as-cast surface and micro-roughness defects on the free surface at small scales. Simulation results indicate that the gap on the as-cast surface is caused by the formation of air-pocket due to the entrapment of gas into the puddle. The presence of air-pocket can affect the heat transfer between the melt and the cooling roll, consequently influencing the fluid velocity and causing fluctuations on the free surface, and even leading to fracture of the thin ribbon. The wetting behavior of the melt on the cooling roll surface influences the entrainment of air-pocket, and favorable wetting conditions can reduce the entrainment of air-pocket, thereby improving the surface quality of the alloy thin ribbon. [ABSTRACT FROM AUTHOR]

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]

The aircraft engine is a core component of an airplane, and its critical components work in harsh environments, making it susceptible to a variety of surface defects. To achieve efficient and accurate defect detection, this paper establishes a dataset of surface defects on aircraft engine components and proposes an optimized object detection algorithm based on YOLOv5 according to the features of these defects. By adding a dual-path routing attention mechanism in the Biformer model, the detection accuracy is improved; by replacing the C3 module with C3-Faster based on the FasterNet network, robustness is enhanced, accuracy is maintained, and lightweight modeling is achieved. The NWD detection metric is introduced, and the normalized Gaussian Wasserstein distance is used to enhance the detection accuracy of small targets. The lightweight upsampling operator CARAFE is added to expand the model's receptive field, reorganize local information features, and enhance content awareness performance. The experimental results show that, compared with the original YOLOv5 model, the improved YOLOv5 model's overall average precision on the aircraft engine component surface defect dataset is improved by 10.6%, the parameter quantity is reduced by 11.7%, and the weight volume is reduced by 11.3%. The detection performance is higher than mainstream object detection algorithms such as SSD, RetinaNet, FCOS, YOLOv3, YOLOv4, and YOLOv7. Moreover, the detection performance on the public dataset (NEU-DET) has also been improved, providing a new method for the rapid defect detection of aircraft engines and having high application value in various practical detection scenarios. [ABSTRACT FROM AUTHOR]

Fatigue of engineering materials is caused by the gradual degradation of materials under cyclic loading, from the evolution of internal and surface defects. It is quite necessary to combine internal and surface defects together in fatigue life prediction for materials. In this paper, an integrated method was proposed to predict fatigue life based on internal and surface defects. Based on five-stage damage testing, internal defects were obtained by x-ray CT and reconstructed. A simplified internal defects classification method is proposed. Surface defects were observed by microscope, then extracted by MATLAB, and reconstructed in SolidWorks. Then, an integrated defect model combining both internal and surface defects was proposed by using Python script to ABAQUS software. The model was verified by experiments. Finally, a regression fatigue life prediction method was obtained by combining surface roughness and stress concentration coefficient. It is shown that the proposed integrated defect model can achieve life prediction effectively. [ABSTRACT FROM AUTHOR]

In the present study, Neodymium (Nd), Cadmium (Cd), and the various molar ratios of Nd: Cd doped copper oxide nanocomposites (CuO NCs) were prepared by the co-precipitation method. The as-synthesized Nd, Cd, and Nd:Cd doped CuO NCs [Cu 1-x + y Nd x Cd y O, (x:y = 0.006:0.00, 0.00:0.006, 0.005:0.001, 0.004:0.002, 0.003:0.003, M)] were characterized through various instrumentation techniques such as TGA, UV–Vis–NIR, FTIR, Raman, FL, XRD, ZP, FE-SEM with EDAX elemental mapping, HR-TEM and XPS analyses. Further, the antibacterial activity of doped CuO NCs was tested against Staphylococcus aureus and Escherichia coli. An equal molar ratio of Nd and Cd doped CuO NCs showed excellent antibacterial activity mainly due to the synergistic effect of sufficient Nd3+, Cd2+, and Cu2+ ions releasing ability. Interestingly, the doping effect enhances surface defects and decreases the ability to scavenge free radicals compared to pure CuO nanomaterials. At the same time, the cytotoxicity of NCs was evaluated on the human lung epithelial L132 cell line. Evidently, 75 μg/ml concentration of Nd:Cd doped CuO NCs samples shows 80% viability, which confirms their negligible cytotoxic effect. The Nd:Cd doped CuO NCs (94:3:3) had a high aspect ratio shape, remarkable ion-releasing ability, and biocompatibility while being thermally stable. Because of these qualities, they are well suited for treating bacterial infections in the biomedical area. • Nd and Cd doped CuO NCs was successfully synthesized by co-precipitation method. • The equal molar ratio of Nd and Cd excellently substituted the CuO matrix and extends growth direction [0 1 0] plane. • CuO NCs showed ROS-mediated antibacterial activity in S. aureus and E. coli via releasing Nd3+ and Cd2+ ions. • Cytocompatibility of CuO NCs against L132 cell line opens-up the possibilities for further pre-clinical investigations. [ABSTRACT FROM AUTHOR]

The corrosion steels phenomenon is one of the main problems in the oil industry, such as in buried transmission pipelines used for high gas pressure for long distances. Steels are protected from the external soil corrosion through a bituminous coating, whose action is coupled with a cathodic protection system, which aims to maintain steel in its protection field and thus to avoid any corrosion risk. However, steels in service may experience external surface defects like corrosion pitting and cracking due to electrochemical or mechanical interactions of bare steel with an aggressive soil solution after steel protection failure. These are concerning phenomena and are the major threats of the pipeline transmission system’s reliability and ecological safety. Corrosion mechanisms are varied and can be evaluated by different methods, such as electrochemical measurements, which are influenced by various factors like temperature, pH, soil characteristics, resistivity, water content, and as well mechanical stresses. Corrosion results from simulated artificial soil solutions showed that steel is sensitive to corrosion by soil. Surface defects detection was carried out using an ultrasonic non-destructive method such as C-Scan Emission testing and the time of flight diffraction technique (TOFD) ultrasonic non-contact testing method. After propagation of the ultrasonic waves, the diffracted ultrasonic reflected wave occurring at the edges of the defects appears due to the presence of a corrosion defect by generating defect echoes. The C-Scan ultrasonic image shows surface reflection, including corrosion defects on interfaces with varying acoustic impedances. The cross-transverse speed ultrasonic propagation through the plate including defect is modified, revealing more surface defects, and cross-transverse speed is shown to increase ultrasonic detection presents some advantages, such as precision and speed of detection without alteration to the structure. This method can be used in the industrial context as an intelligent industrial robotics technique.

Objective: Improve the detection accuracy and efficiency of apple surface defects. Methods: An detection method for apple surface defects was established based on an improved convolutional neural network (CNN) and data augmentation method. Firstly, the classical CNN was improved to detect apple surface defects. Then, using the conditional generation adversarial network, the image data of surface defect free and defective apples was augmented with synthetic apple images to improve the detection performance of the improved CNN for apple surface defects. Finally, by pruning the CNN model, the detection accuracy, detection speed and energy saving limits of apple surface defects were balanced reasonably to improve the practicability of the proposed method. Results: When 2 048 interpretive neurons were selected in the interpretation layer of the improved CNN, the average detection accuracy was the highest among the interpretive neuron number situations. Additionally, the diversity of the apple image data sets was enhanced with the synthetic apple images produced by the conditional generation adversarial network. In addition, the accuracy of the proposed method for detecting apple surface defects increased continuously with the increase of the proportion of the enhanced images in the test data set. When the ratio of the pruned model size to the original model size decreased from 100% to 50%, the detection efficiency of apple surface defects was doubled with 6.96% detection accuracy decreasing. Conclusion: This method is expected to realize the automatic defect detection in apple production and processing, and provide a reference for the developing of other fruit surface defect detection methods.

The defect-induced laser damage on mechanically machined fused silica optical surfaces under intense laser irradiation severely threatens the laser damage resistance of optical components. The lack of a quantitative relationship between the feature information of surface defects and the laser-induced damage thresholds (LIDTs) of optical surfaces poses a challenge to effectively evaluate the influence of the surface defects on the LIDTs of optical surfaces. Herein, the photoluminescence (PL) properties of the areas where laser damage tends to occur in surface-defect zones are explored. The PL induced by the point defects at the atomic scale in these areas is found to be obviously higher than that of other positions. The PL envelope area (S) at the position with the highest PL intensity among the whole surface defect is found to be inversely proportional to the LIDT (Φ) of the optical surface for the first time (SΦ = N 0). It is beneficial to quantitatively characterize the influence of the surface defect and provide new insights in elucidating the defect-induced laser damage mechanisms. Besides, we found that there is a minimum LIDT for the mechanically machined optical surfaces with various surface defects. On basis of this, the maximum influence of the surface defects with different species on the LIDTs of optical surfaces could be determined. Based on the constructed relationship (SΦ = N 0) and the developed MD model, two causes of the minimum LIDT are determined. The PL envelope area of the surface defect would slightly affect the LIDT when the PL envelope area is large enough, which is the first cause. The mechanical force could compact the optical surface, increasing the densities of Si and O atoms. However, the densities of Si and O atoms become stable when the mechanical force is large enough, causing the highest densities of point defects, which is the second cause. [ABSTRACT FROM AUTHOR]

A 3D printer produces parts from CAD models. The printout must be checked for defects. We present a surface defect detection algorithm that constructs a 3D mesh of the printout by multi-view scanning and compares it to the part CAD model. It computes and displays the distance from each vertex of the printout mesh to the closest point on the part model. These distances comprise the global deviation of the printout from the model. When the error is within a threshold, the algorithm compares the curvature at each printout vertex to that at the closest part point. Qualitative differences in curvature indicate local defects, such as blobs, cracks, surface roughness, and faded embossing and engraving. The results are grouped into local defect regions that are displayed. We demonstrate the algorithm on metal printing and on PLA 3D printing. [ABSTRACT FROM AUTHOR]

When detecting surface defects in the industrial cutting environment, the defects are easily contaminated and covered by many interference factors (such as chips and coolant residue) that exist on the machined surface. These interfering factors hinder the sustainable detection of surface defects. Furthermore, addressing the challenge of detecting surface defects in the presence of interference factors has proven to be a difficult problem in the current detection field. To solve this problem, a sustainable detection method for surface defects is proposed. The method is divided into two steps: one is the identification and removal of interference factors; the other is the detection of surface defects. First, a new FPN-DepResUnet model is constructed by modifying the Unet model from three aspects. The FPN-DepResUnet model is used to identify the interference factors in the image. Compared to the Unet model, the MAP of the FPN-DepResUnet model is increased by 5.77%, reaching 94.82%. The interfering factors are then removed using the RFR-net model. The RFR-net model performs point-to-point repair of interference regions. The repair process is performed by finding high-quality pixels similar to the interference region from the rest of the image. The negative effects of the interfering factors are removed by combining the FPN-DepResUnet model with the RFR-net model. On this basis, the SAM-Mask RCNN model is proposed for efficient defect detection of clean surface images. Compared with the Mask RCNN model, the MAP of the proposed SAM-Mask RCNN model increased by 2.00%, reaching 94.62%. Further, the inspection results can be fed back with a variety of surface defect information including defect types, the number of pixels in the different defect regions, and the proportion of different defect regions in the entire image. This enables predictive maintenance and control of the machined surface quality during machining. [ABSTRACT FROM AUTHOR]

Gating system design is an essential element in casting that significantly affects the melt's molten metal flow behaviour, heat transfer, and solidification. Improper gating system leads to pouring-related defects like cold shut, misrun, etc. Submersible pump-bearing block castings produced in one of the leading foundries are rejected due to a cold shut defect. It was identified that the improper gating system is the probable cause for rejection. The bearing block and the existing gating system were modelled in 3D modelling software, and the molten metal flow behaviour of casting was simulated with casting simulation software. Based on the simulation results, the existing gating system was redesigned following established empirical relations. The component with the revised gating system was analysed for flow behaviour through simulation with the software. With the revised gating system, elimination of cold shut is observed, and the revised gating design is recommended to the industry for implementation. The effectiveness of the revised gating system is also validated with pump diffuser casing, which has similar geometrical and material characteristics. The simulation results and experimental validation showed a reduction in flow and surface defects. Appropriate design of the gating system and balanced arrangement of cavities in the gating system are noted to reduce the flow and surface-related defects. It is observed that with the revised gating systems, the percentage of rejection is reduced from 28.36% to 2.34% and 23.43% to 2.45% for the bearing block and pump diffuser casing respectively. [ABSTRACT FROM AUTHOR]