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In mechanical engineering, monitoring steel surface defects is crucial for ensuring the quality of industrial products, as these defects account for over 90% of flaws in steel items. Traditional manual inspection methods are time-consuming and may overlook some defects. To address these challenges, this study introduces an automated deep learning (DL) model for continuous monitoring of steel surface defects using real-world images from the Industrial Machine Tool Component Surface Defect (IMTCSD) dataset, which includes 1,104 three-channel images, 394 of which are categorized as exhibiting "pitting" damage. This study evaluated several Convolutional Neural Network (CNN) classifiers: EfficientNetB3, ResNet-50, and MobileNetV2, to determine the most effective model for defect detection. EfficientNetB3 is distinguished by its scalable architecture that adapts efficiently across various image dimensions, making it ideal for high-accuracy applications on limited computational resources. ResNet-50 uses residual connections to maintain performance in deeper networks by facilitating smooth gradient flow, yet it requires more computational power. MobileNetV2, designed for real-time applications on devices with limited resources, uses lightweight depthwise separable convolutions. The performance of these models was assessed using accuracy, recall, precision, specificity, F1-score, and AUC metrics. EfficientNetB3 emerged as the best performing model, achieving an accuracy of 0.981, specificity of 0.975, recall of 0.987, precision of 0.975, and an F1-score of 0.982. This model proved effective in detecting defects even on dirty surfaces, demonstrating its potential to significantly enhance quality control in industrial settings. [ABSTRACT FROM AUTHOR]

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The main objective of this research is to create and improve three different object identification techniques for identifying surface flaws and categorising dimensions in steel that has been fabricated. RetinaNet, YOLOv3, and Faster R-CNN are the selected modalities in the experiment. The main goal is to evaluate these modalities' ability to detect and classify defects on steel surfaces in terms of accuracy, precision, recall, and F1 score. This assessment makes use of a varied collection of steel surface photos that show different kinds and sizes of faults. Training, validation, and testing sets make up the dataset's partitioning. The training set is used to train and optimise the three modalities, while the testing and validation sets are used to evaluate their performance. According to the study's findings, all three methods provide excellent of 0.92. RetinaNet comes in second with an F1 score of 0.89, followed by YOLOv3 with an F1 score of 0.87, while the Faster R-CNN modality obtains the greatest overall performance with an F1 score. [ABSTRACT FROM AUTHOR]

The success of machine learning (ML) models in object or pattern recognition naturally leads to ML being employed in the classification of the microstructure of steel surfaces. Light optical microscopy (LOM) is the traditional imaging process in this field. However, the increasing use of ML to extract or relate more aspects of the aforementioned materials and the limitations of LOM motivated us to provide an improvement to the established image acquisition process. In essence, we perform style transfer from LOM to scanning electron microscopy (SEM) combined with “intelligent” upscaling. This is achieved by employing an ML model trained on a multimodal dataset to generate an SEM-like image from the corresponding LOM image. This transformation, in our opinion, which is corroborated by a detailed analysis of the source, target and prediction, successfully pushes the limits of LOM in the case of steel surfaces. The expected consequence is the improvement of the precise characterization of advanced multiphase steels’ structure based on these transformed LOM images.

The success of machine learning (ML) models in object or pattern recognition naturally leads to ML being employed in the classification of the microstructure of steel surfaces. Light optical microscopy (LOM) is the traditional imaging process in this field. However, the increasing use of ML to extract or relate more aspects of the aforementioned materials and the limitations of LOM motivated us to provide an improvement to the established image acquisition process. In essence, we perform style transfer from LOM to scanning electron microscopy (SEM) combined with "intelligent" upscaling. This is achieved by employing an ML model trained on a multimodal dataset to generate an SEM-like image from the corresponding LOM image. This transformation, in our opinion, which is corroborated by a detailed analysis of the source, target and prediction, successfully pushes the limits of LOM in the case of steel surfaces. The expected consequence is the improvement of the precise characterization of advanced multiphase steels' structure based on these transformed LOM images. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Ground Improvement is the property of Journal of Ground Improvement Editorial Office 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.)

In steel production, defect detection is crucial for preventing safety risks, and improving the accuracy of steel defect detection in industrial environments remains challenging due to the variable types of defects, cluttered backgrounds, low contrast, and noise interference. Therefore, this paper introduces a steel surface defect detection model, DBCW-YOLO, based on YOLOv5. Firstly, a new feature fusion strategy is proposed to optimize the feature map fusion pair model using the BiFPN method to fuse information at multiple scales, and CARAFE up-sampling is introduced to expand the sensory field of the network and make more effective use of the surrounding information. Secondly, the WIoU uses a dynamic non-monotonic focusing mechanism introduced in the loss function part to optimize the loss function and solve the problem of accuracy degradation due to sample inhomogeneity. This approach improves the learning ability of small target steel defects and accelerates network convergence. Finally, we use the dynamic heads in the network prediction phase. This improves the scale-aware, spatial-aware, and task-aware performance of the algorithm. Experimental results on the NEU-DET dataset show that the average detection accuracy is 81.1, which is about (YOLOv5) 6% higher than the original model and satisfies real-time detection. Therefore, DBCW-YOLO has good overall performance in the steel surface defect detection task. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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.)

An enhanced steel surface defect detection algorithm based on YOLOv8 was introduced to enhance the accuracy of small target detection. This algorithm incorporates an attention-free mechanism to calculate attention-weight, aiding in the extraction of specific feature regions. Additionally, improvements were made to the SPPF module to expand the receptive field and enhance target detection optimization. Experimental evaluations on the NEU-DET dataset demonstrated significant enhancements over the original YOLOv8 algorithm. The improved algorithm exhibited a 9.3 percentage point increase in precision, a 10 percentage point increase in recall, a 4.6 percentage point increase in mAP@0.5, and a remarkable 21.2 percentage point increase in mAP@0.5:0.95.Significant progress has also been made in analyzing the surface data of aluminum sheets. The enhanced algorithm has shown a 6% increase in precision compared to the original YOLOv8 algorithm. Additionally, recall has improved by 3.2%, mAP@0.5 has increased by 4.1%, and mAP@0.5:0.95 has seen a notable rise of 17.4%.

In order to improve the problem of insufficient measurement scale and accuracy in the process of fracture toughness testing by indentation method, a method for assessing the fracture toughness of steel based on Berkovich indentation strain energy density (SED) is proposed based on the critical indentation energy (CIE) method. The fracture toughness of nine steels is evaluated by considering both elastic and plastic energies and correcting the critical total pressure depth. The results show that the relative error of the toughness measured by considering only the plastic energy is found to be in the range of 5% to 20%, whereas the relative error of the toughness measured by considering both the elastic and plastic energies is found to be within 5%. At the same time, determining the fitting range for the critical total indentation depth requires avoiding the region where the elastic modulus of the material rises near the surface. These improve the accuracy of evaluating the fracture toughness of steel based on the CIE method using the Berkovich indenter. [ABSTRACT FROM AUTHOR]

Slagging and fouling of boilers are typical technological problems in the combustion of solid fuels (especially coal and biomass). This study proposes a novel approach based on laser texturing of the near-surface layer of steel used to make heating surfaces of coal-fired boilers. This approach is aimed at improving the resistance of metal surfaces to slagging. The formation of slag deposits on the surfaces of AISI 310 S steel modified by laser radiation has been experimentally studied. The formation of slag melt was analyzed on the surface of modified and polished steel plates during heating to 1400 °C. High-temperature heating and interaction with slag led to a change in the chemical composition of the steel. A significant increase in the chromium content (up to 47%) of the steel surface and the transformation of its texture with the formation of grains up to 2.5 µm in size were established. Laser surface treatment significantly increased the resistance of the steel to such changes and also reduced the grain size to 1.5 µm. As a result of laser modification of surfaces, the temperature of slag formation shifts to higher values (higher by 60–75 °C compared to the unmodified polished surface). The anisotropic texture (cauliflower type) contributed to a decrease in the size of the slag spreading front, and also had by self-cleaning properties from the mineral melt. The latter allows to increase the reliability and energy efficiency of boiler equipment. [Display omitted] [ABSTRACT FROM AUTHOR]

In steel production, defect detection is crucial for preventing safety risks, and improving the accuracy of steel defect detection in industrial environments remains challenging due to the variable types of defects, cluttered backgrounds, low contrast, and noise interference. Therefore, this paper introduces a steel surface defect detection model, DBCW-YOLO, based on YOLOv5. Firstly, a new feature fusion strategy is proposed to optimize the feature map fusion pair model using the BiFPN method to fuse information at multiple scales, and CARAFE up-sampling is introduced to expand the sensory field of the network and make more effective use of the surrounding information. Secondly, the WIoU uses a dynamic non-monotonic focusing mechanism introduced in the loss function part to optimize the loss function and solve the problem of accuracy degradation due to sample inhomogeneity. This approach improves the learning ability of small target steel defects and accelerates network convergence. Finally, we use the dynamic heads in the network prediction phase. This improves the scale-aware, spatial-aware, and task-aware performance of the algorithm. Experimental results on the NEU-DET dataset show that the average detection accuracy is 81.1, which is about (YOLOv5) 6% higher than the original model and satisfies real-time detection. Therefore, DBCW-YOLO has good overall performance in the steel surface defect detection task.

In order to improve the efficiency of small target detection of steel surface defects, an improved Yolov7- tiny steel surface defect detection algorithm is proposed. The activation function of the feature extraction network is changed to SiLU to improve the feature extraction capability. The tensor splicing operation of the feature fusion network is combined with the weighted bidirectional feature pyramid BiFPN, and the nearest interpolation of the upper sampling part is replaced with the lightweight operator CARAFE to improve the feature fusion ability. Finally, the multi-head self-attention mechanism MHSA and SPD convolution building blocks are introduced at the output end to improve the detection performance of the output end for small targets of steel surface defects. The ablation and contrast experiments are carried out on the NEU-DET dataset. Compared with the original Yolov7-tiny algorithm, the improved algorithm has increased the mAP by 11.7 percentage points, the precision by 3.3 percentage points, and the FPS value reaches 192. The results show that the improved algorithm can effectively improve the detection efficiency of small targets of steel surface defects. Comparative experiments on the VOC2012 dataset show that the improved algorithm is universal. [ABSTRACT FROM AUTHOR]

To enhance the precision of detecting defects on steel plate surfaces and diminish the incidences of false detection and leakage, the ESI-YOLOv8 algorithm is introduced. This algorithm introduces a novel EP module and integrates the large separation convolutional attention module and the spatial pyramid pooling module to propose the SPPF-LSKA module. Additionally, the original CIOU loss function is replaced with the INNER-CIOU loss function. The EP module minimizes redundant computations and model parameters to optimize efficiency and simultaneously increases the multi-scale fusion mechanism to expand the sensory field. The SPPF-LSKA module reduces computational complexity, accelerates model operation speed, and improves detection accuracy. Additionally, the INNER-CIOU loss function can improve detection speed and model accuracy by controlling the scale size of the auxiliary border.The results of the experiment indicate that, following the improvements made, the algorithm’s detection accuracy has increased to 78%, which is 3.7% higher than the original YOLOv8. Furthermore, the model parameters were reduced, and the verification was conducted using the CoCo dataset, resulting in an average accuracy of 77.8%. In conclusion, the algorithm has demonstrated its ability to perform steel plate surface defect detection with efficiency and accuracy.

Steel is an important raw material of fluid components. The technological level limitation leads to the surface faults of the steel, thus the key to improving fluid components quality is to diagnose the faults in steel production. The complex shape and small size of steel surface faults result in the low accuracy of the diagnosis, and the large size of the network leads to poor real-time performance. Therefore, aiming at the problems, an improved YOLOV5 is proposed. Firstly, to reduce the feature information loss, coordinate attention is used to improve YOLOV5, thus the diagnosis ability can be improved. Secondly, to further reduce the loss, a new connection is constructed in YOLOV5, and the detection ability can also be further improved. Thirdly, to improve the real-time performance of the fault diagnosis, YOLOV5 is improved by the lightweight method ShuffleNetV2, and its size can be reduced. Lastly, to further improve the accuracy, the cosine annealing with warm restarts algorithm is used to optimize YOLOV5. The dataset of NEU-DET is verified and testified. The results show that improved YOLOV5 can diagnose steel surface faults with high efficiency and accuracy. [ABSTRACT FROM AUTHOR]

Features of structure organization during pulsed laser melting of steel surface layers are studied. Based on analyzing crystallization process rate kinetics it is established that temperature gradients and thermal stresses developing in irradiated metal areas during melting contribute to convective mixing of liquid at a rate of 103 cm/sec, and also, despite an extremely short laser pulse time (10–3 sec) they contribute to partial or complete dissolution of the original structure carbides. As a result, the chemical composition of melted metal changes, martensitic transformation points are lowered and a significant amount of textured metastable residual austenite (40–60%) is fixed that has a very fine structure (dendrite cross-section 5–9 nm). This has a favorable effect on irradiated material surface layer quality and mechanical properties. It is established by experiment that with a change in laser processing regime it becomes possible to achieve a wide range of cooling rates during crystallization, i.e., to control the degree of carbide dissolution, structural characteristics, and melted metal area properties. It is demonstrated that in order to achieve the maximum possible degree of hardening for irradiated steels having different chemical composition, it is necessary to dissolve 30–60% of carbides in the initial metal. As a result of this after laser irradiation material heat resistance increases by 50–100°C, and wear resistance increases by a factor of 2–4. [ABSTRACT FROM AUTHOR]

Pipeline steels are among the most common engineering materials and are used extensively in various industries. Therefore, it is essential that these materials are hydrophobic and corrosion resistant. Herein, a simple method to prepare a superhydrophobic coating on X65 steel is proposed. Firstly, the black oxide coating (BO) was grown in situ on X65 steel surface, utilizing its morphology advantage to build the micro-nano structure, where they showed a water contact angle (WCA) of 158°. After fluorination, the WCA of the coating increased to 163°. The prepared superhydrophobic samples were exposed to air and salt spray test for a 30-day durability study, where the long-lasting superhydrophobicity of the coating demonstrated its wide applicability. In addition, the coating also exhibited excellent corrosion resistance and self-cleaning property. This work provides a simple and low-cost technique for designing superhydrophobic coatings on steels. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence technology and the popularity of intelligent production projects, intelligent inspection systems have gradually become a hot topic in the industrial field. As a fundamental problem in the field of computer vision, how to achieve object detection in the industry while taking into account the accuracy and real-time detection is an important challenge in the development of intelligent detection systems. The detection of defects on steel surfaces is an important application of object detection in the industry. Correct and fast detection of surface defects can greatly improve productivity and product quality. To this end, this paper introduces the MSFT-YOLO model, which is improved based on the one-stage detector. The MSFT-YOLO model is proposed for the industrial scenario in which the image background interference is great, the defect category is easily confused, the defect scale changes a great deal, and the detection results of small defects are poor. By adding the TRANS module, which is designed based on Transformer, to the backbone and detection headers, the features can be combined with global information. The fusion of features at different scales by combining multi-scale feature fusion structures enhances the dynamic adjustment of the detector to objects at different scales. To further improve the performance of MSFT-YOLO, we also introduce plenty of effective strategies, such as data augmentation and multi-step training methods. The test results on the NEU-DET dataset show that MSPF-YOLO can achieve real-time detection, and the average detection accuracy of MSFT-YOLO is 75.2, improving about 7% compared to the baseline model (YOLOv5) and 18% compared to Faster R-CNN, which is advantageous and inspiring. [ABSTRACT FROM AUTHOR]

Owing to the high hardness and hydrogen passivation of carbon bonds, hydrogenated diamond‐like carbon (a‐C:H) film has shown promising potential to achieve ultra‐low friction and wear on steel surfaces. Here, a‐C:H film was successfully deposited on 9Cr18Mo steel via programmable high power pulse magnetron sputtering and potential application for industrial was evaluated. The a‐C:H films against different mating materials of GCr15 steel balls, Al2O3, Si3N4, ZrO2, and a‐C:H‐coated GCr15 balls all showed ultra‐low friction under a normal load of 5 N in a dry ambient air environment. Among them, self‐mating tribo‐system a‐C:H films on steel surfaces and a‐C:H‐coated steel balls achieve best friction performance; the principal reason is that both contacting surfaces coated with a‐C:H film have the lower electron affinities compared with other tribo‐systems. However, the differences of coefficient of friction (COF) for uncoated‐GCr15, Al2O3, ZrO2, Si3N4, and a‐C:H(GCr15) balls can be attributed to different sizes of clustering in wear debris. This work provides new insights on synthesis and industry application of the a‐C:H films with ultra‐low friction properties. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence technology and the popularity of intelligent production projects, intelligent inspection systems have gradually become a hot topic in the industrial field. As a fundamental problem in the field of computer vision, how to achieve object detection in the industry while taking into account the accuracy and real-time detection is an important challenge in the development of intelligent detection systems. The detection of defects on steel surfaces is an important application of object detection in the industry. Correct and fast detection of surface defects can greatly improve productivity and product quality. To this end, this paper introduces the MSFT-YOLO model, which is improved based on the one-stage detector. The MSFT-YOLO model is proposed for the industrial scenario in which the image background interference is great, the defect category is easily confused, the defect scale changes a great deal, and the detection results of small defects are poor. By adding the TRANS module, which is designed based on Transformer, to the backbone and detection headers, the features can be combined with global information. The fusion of features at different scales by combining multi-scale feature fusion structures enhances the dynamic adjustment of the detector to objects at different scales. To further improve the performance of MSFT-YOLO, we also introduce plenty of effective strategies, such as data augmentation and multi-step training methods. The test results on the NEU-DET dataset show that MSPF-YOLO can achieve real-time detection, and the average detection accuracy of MSFT-YOLO is 75.2, improving about 7% compared to the baseline model (YOLOv5) and 18% compared to Faster R-CNN, which is advantageous and inspiring.

Computer vision is augmented in various manufacturing industries to perform automated inspection operations accurately and efficiently. It has been observed that the performance of vision-based inspection approaches degrades considerably upon utilizing images captured under shop-floor conditions. This work proposes utilizing Histogram Equalization and adversarial training through Neural Structure Learning (NSL) for developing a robust vision-based Surface Defect Classification framework. A novel deep neural network architecture obtains adversarial samples in the extracted feature space instead of obtaining the same in the original input image space. The architecture can be easily integrated and employed with various machine learning models. A commonly employed steel surface defect dataset (NEU) with practical relevance to industrial cases is selected for the model training and experimental studies. The robustness of the proposed approach is evaluated over the Extended Diversity Enhanced (ENEU) dataset derived by simulating image acquisition variations similar to shop floor conditions. The results reveal that the proposed approach enhances the recognition accuracy of the baseline method from 87.7% to 92.4% over ENEU. The prediction accuracy of the proposed approach is considerably better than the traditional methods and deep learning competitors over ENEU. The qualitative and quantitative comparison of results obtained using the present approach with methods reported in the literature demonstrates the effectiveness of adversarial training in improving the generalization abilities of machine learning models. [ABSTRACT FROM AUTHOR]

[Display omitted] • The ignition delay and burnout times of OCWF can be controlled by surface roughness. • To change the combustion characteristics, steel is processed by 100-μm abrasives. • Laser surface processing allows controlling the ignition and combustion characteristics. • Laser processing of steel intensifies the fuel droplet combustion on its surface. The ignition and combustion of single organic coal-water fuel (OCWF) droplets made of coal (30%), waste engine oil (35%), and water (35%) during conductive heating on the surfaces of the machine steel modified with abrasive materials and laser radiation were studied experimentally. The experimental conditions (material and temperature of the heating surfaces) corresponded to the operating conditions of modern furnace chambers of boilers at thermal power plants. It is found that the ignition delay and burnout times during conductive heating of OCWF droplets on steel surfaces can be controlled by changing the surface roughness parameters. On the example of 7-mg OCWF droplets with a radius of about 1.2 mm, it is shown that for processing of heating surfaces, it is necessary to use the abrasive materials with an average grit size of 100 μm to decrease the ignition and burnout times. The abrasive materials with an average grit size of more than 100 μm have an insignificant effect on the ignition delay and burnout times of OCWF droplets. However, such processing significantly increases the deposition of solid combustion products (ash deposits) with an increase in the surface roughness. Laser processing of steel surfaces is of particular interest in the implementation of passive ways of controlling the ignition and combustion characteristics of OCWF droplets. This way of changing the surface texture allows controlling the ignition delay and burnout times in a wide range of the heating source temperatures, as well as reducing the intensity of the solid product deposition on the heating surface during the OCWF combustion. The positive effect from laser processing of heating surfaces to intensify the fuel droplet combustion consists in increasing the velocity of vapor flows from combustible fuel components due to the droplet dispersion. This, in turn, intensifies the formation of a combustible vapour-gas mixture and increases the velocities of flame propagation together with the dispersion products of fuel droplets along the textured surface. [ABSTRACT FROM AUTHOR]

[Display omitted] • MicroCoat® coated AISI 304 steel topography was modified by nanosecond laser pulses at variable incident laser fluence. • The water-wetting angles continuously decreased from 100° to 10° on the patterned surfaces. • The autonomous directional water flow was demonstrated on the laser-structured patterns of various shapes. • Temporal tracking of the contact angle after the laser structuring demonstrated gradual surface hydrophobization. A series of hybrid textures with variable topography and chemical composition were obtained on a hydrophilic steel (AISI 304) substrate with a hydrophobic MicroCoat® coating at the increasing incident nanosecond laser fluence from 1.4 J/cm2 to 5.6 J/cm2. The microtopography of the textured surface was studied by laser microscopy and scanning electron microscopy (SEM), while the chemical composition of the surface was determined by IR spectroscopy. The contact angles of water wetting in the textured areas varied versus laser fluence from about 100° to 10°, which was explained by both ablative removal of the hydrophobic polymer coating and the increase in roughness of the hydrophilic steel surface. The time dependence of the contact angle on the different textures, which demonstrated hydrophobization at different time scales depending on the texturing conditions, was analyzed. Autonomous directional flow was observed on laser-patterned wetting-gradient structures of various shapes, and the flow velocity of water droplets on these structures were measured. It was shown that the velocity of water drop spreading on the gradient structures depends on the difference in contact angles on adjacent spots. The various modules fabricated can be harnessed in chemical and biomedical microfluidic devices. [ABSTRACT FROM AUTHOR]

• Stachys byzantine -(SB) extract was applied as green inhibitor for steel in HCl media. • SB-zinc cations mixture provided the corrosion inhibition of about 92%. • The mix of SB and Zn caused a protective layer creation on the surface. The presence of bio-active compounds in Stachys byzantina (SB) extract has made it a powerful source of green inhibitors in controlling steel corrosion. In this study, it has been attempted to create a highly durable corrosion protective film on the surface of the metal with SB and divalent zinc (II) cations and investigated them by Electrochemical Impedance Spectroscopy (EIS) and Potentiodynamic polarization (PP) tests, and surface analysis methods. EIS results show that after 24 h of placing the steel samples in 3.5% NaCl solution containing 700 SB − 300 ZN, the synergistic corrosion inhibition behavior was about 92%. Besides, the results of the PP demonstrated a significant reduction of the i corr. Furthermore, surface analyses such as Field Emission Scanning Electron Microscopy/Energy Dispersive X-ray Spectroscopy (FE-SEM/EDS) and Atomic Force Microscopy (AFM) images illustrated the low metal surface degradation, which indicates that the mix of SB and Zn caused a protective layer creation on the surface, especially for SB 700 - Zn 300. Also, new compounds of graphitic materials' structure were made using SB extract which is a natural-based chemical, and investigated by Fourier-Transform Infrared Spectroscopy (FTIR) test, Grazing Incidence X-ray Diffraction (GIXRD) technique, Ultraviolet–visible spectroscopy (UV–Vis) analysis, and Raman spectroscopy. [ABSTRACT FROM AUTHOR]