Fe60/Cr3C2 composite deposition coatings were fabricated on 24CrNiMo substrate used for high-speed rail brake discs via laser direct deposition．The microstructure and properties of these coatings were analyzed using optical microscopy (OM), X-ray diffraction (XRD), scanning electron microscopy (SEM), microhardness testing, abrasive wear testing, and high-temperature oxidation analysis. The results showed that the coating is mainly composed of -Fe, -Fe, Cr7C3, CrFeB, WC, Fe2B, CrB, and (Cr,Fe)7C3, etc. When the Cr3C2 content reaches 15%, new phases Cr3C2 and Cr23C6 are detected in the coating. The coating with 15% Cr3C2 exhibits typical radial structures which constitute of long rod-shaped and polygon block radiating from the center of approximately elliptical Cr3C2 aggregates to the periphery，with the long rod-shaped and polygon block structures mainly composed of (Cr, Fe)7C3 and a small amount of Cr23C6 compounds. The microhardness of the coating with Cr3C2 is significantly improved, among which the hardness change of the coating with 9% Cr3C2 is stable, with an average hardness of 741.1 HV and the highest wear resistance. The oxidation weight gain of the coating decreases with the increase of Cr3C2 content, and the high-temperature oxidation resistance of the coating is improved. A dense Cr2O3 oxide film is formed in the coating with 15% Cr3C2, which has the best high-temperature oxidation resistance. However, the thermal fatigue resistance of coatings with Cr3C2 addition remains unchanged.

This study investigates the influence of different layers and welding passes on the macroscopic morphology, microstructure, mechanical properties, and corrosion resistance of multi-layer and multi-pass fillet welds in the context of repairing cladding defects at the corners of spent fuel pools in nuclear power plants. The results show that two-layer two-pass and three-layer five-pass welding can effectively increase the thickness of the fillet weld to 3.6 mm and 5.7 mm, respectively. Meanwhile, two-layer three-pass and three-layer six-pass welding can increase the size of the weld foot to 6 mm and 9 mm, respectively, based on the increased thickness. In terms of microstructure, ferrite is distributed in the weld with varying morphologies: skeleton-shaped or worm-like ferrite is predominantly found in the weld center, while tree-like ferrite is mainly present on the weld surface. Additionally, ribbon-like ferrite is observed at the junction of the bottom plate and the vertical plate. As the welding sequence increases, coarse grains are refined, and fine tissues undergo secondary growth, resulting in differences in the size and growth direction of the weld tissues. Regarding mechanical properties, the measured microhardness of the fillet welds exceeds that of the base material. In terms of corrosion resistance, two-layer two-pass and three-layer five-pass welds exhibit severe corrosion after pitting tests, with uniform corrosion and oxidation zones appearing on the surface. In contrast, two-layer three-pass and three-layer six-pass welds demonstrate good corrosion resistance, with no obvious pitting pits. These findings provide guidance for multi-layer and multi-pass fillet welding.

The shrink fit interface between the axle and wheel hub is susceptible to microslip damage due to combined torsional and tensile loads, leading to fretting fatigue and premature failure during assembly and service. In this study, a Fe314 alloy coating was applied to EA1N axle steel using laser cladding with pre-strain. The microstructure and microhardness of the repair layer were investigated, and a comparative analysis was conducted on the torsional fretting fatigue properties of axles with varying repair depths. The results indicate that the repaired axle steel exhibits a strong metallurgical bond without defects such as blowholes and cracks. The microhardness and elastic modulus increase progressively from the cladding surface to the substrate region. The torsional fretting fatigue life significantly improves with increasing repair depth. Fretting cracks in an "X" shape originate from the substrate zone without cladding. Furthermore, the damage degree on the cladded side is less severe than in the substrate zone, with the cladded side primarily exhibiting slight delamination and plastic deformation.

In laser processing, controlling the shape of the cutting profile, particularly the taper, is often essential. Understanding the interaction characteristics between the laser and the material, as well as the effects of processing parameters on the profile, is crucial. This study calculates the relationship between kerf angle and fundamental laser beam features during picosecond laser processing. Based on these results, two primary dicing modes—vibration mirror scanning and helical cutting—are examined. The influence of laser processing parameters on kerf angle in these modes is analyzed in conjunction with practical applications, enabling an understanding of the post-processing profile shape and estimation of relevant geometric parameters. Additionally, the paper discusses kerf angle control in other similar laser processing methods, analyzing their respective advantages and limitations to guide the selection of appropriate techniques for specific requirements.

The Ni60 coating was prepared on the surface of TC4 substrate using laser cladding technology to improve its hardness and wear resistance. Scanning electron microscopy, XRD, microhardness tester, and wear tester were used to analyze the effect of lap ratio on the coating microstructure, hardness and wear resistance. The results show that as the lap ratio increases from 30% to 50%, the grain size in the remelted zone initially decreases and then increases. At a lap ratio of 40%, the grain size in the remelted zone is minimal, with a dense microstructure. The non-remelted zone′s lower part consists of columnar and dendritic crystals, while the upper part comprises equiaxial and cytosine crystals. The coating is mainly composed of -Ni、Cr23C6、Cr7C3、TiC、-Ti and other phases. When the lap ratio is 40%, at which the average hardness of the coating reaches 855 HV, which is 2.4 times of that of the substrate, and the wear loss is 5.05 mg, which is 32% of that of the substrate, indicating that the Ni60 coating can improve the hardness and wear resistance of TC4 alloy.

This study investigated the influence of laser peening impact on the strength and corrosion resistance of 6005A-T6 aluminum alloy. The results indicated that under laser peening impact, a hardened layer formed on the surface of the alloy, causing the grains to transition from coarse elongated structures to fine elongated structures. Three impact passes reduced the average equivalent circle diameter from 19.2 m to 16.8 m, decreased the volume fraction of low-angle grain boundaries, and increased dislocation density. The treatment also fragmented coarse second-phase particles, which effectively pinned dislocations and promoted further grain refinement. These dual effects contributed to the enhancement of both the strength and corrosion resistance of the aluminum alloy after laser peening treatment: after 3 impacts, the yield strength increased from 173 MPa to 231 MPa, representing a 33% development, while the corrosion current density decreased from 1.59×10-6 A/cm2 to 6.75×10-9 A/cm2.

The stability of power towers, which are crucial supports for transmission lines, can be compromised by factors such as geological subsidence, leading to potential tilting and even collapse if not promptly detected. Traditional manual inspection methods are inefficient and insufficient to meet current demands. In order to solve the above problems, this paper proposes a tilt estimation algorithm for power pole tower based on unmanned aerial vehicle (UAV) laser point cloud. The method first uses the semantic segmentation network MFNet-S to extract the categories of the towers in the power scene. Subsequently, the density clustering density-based spatial clustering of applications with noise (DBSCAN) is used to cluster the categories of the towers to obtain a single tower. After obtaining the tower, the tower is vertically segmented, and the bounding box of each layer of the point cloud and its center point are calculated. After obtaining the set of coordinates of the center points, random sample consensus (RANSAC) straight-line fitting is applied to determine the tower′s tilting vector. Finally, the tilt of the tower is calculated according to the formula derived in this paper. Through the experiments on the untilted original tower and the manually placed tilted tower, the experimental results show that the algorithm proposed in this paper has a higher accuracy of tilt calculation, with an average error of only 0.15% and 0.52%, which is better than the 0.24% and 6.59% of the comparison method.

To address the limitations of the PointPillars algorithm, including insufficient accuracy and issues with missed detections and false alarms, an enhanced 3D small object detection algorithm based on PointPillars is proposed： A lightweight attention mechanism, ECA, is integrated into the backbone network to enhance the network′s feature representation capabilities, thereby improving detection accuracy. Additionally, to achieve better performance in deep models, we replace the ReLU activation function with the Hardswish activation function, effectively mitigating the gradient vanishing problem. This improved framework is validated on the KITTI dataset. Experimental results demonstrate a substantial enhancement in the algorithm′s ability to detect small objects. Under the bird′s-eye view, the average mean average precision (mAP) for medium difficulty targets (MmAP) reaches 71.31%, representing a 5.62 percentage point improvement compared to the original algorithm. Furthermore, the proposed model exhibits superior performance in detecting small and occluded objects compared to other mainstream models.

As the monitoring field of substation is wide and the high-voltage equipment in the station is uneven and scattered, the 3D-BoNet example segmentation model has a large deviation for the point cloud segmentation of high-voltage electrical equipment in the station. In this paper, a 3D target instance segmentation model 3DPowerSegNet for substation scene based on improved 3D-BONET is proposed. Firstly, the Groupsift feature extraction module is proposed to carry out convolution from multiple directions to capture more key features and insert them into the backbone network to enhance the ability to extract local features. Secondly, in the sampling stage under point cloud, the set abstraction module is improved to achieve normalization by expanding the query radius to increase the sensitivity field. Then, the inverted residual feature module (IRF) is proposed to obtain richer feature details by expanding the channel, and alleviate the problems of gradient disappearance and model overfitting. Finally, the feature propagation module is improved in the up-sampling stage to reduce information loss during data processing. The comprehensive experimental results on the self-built substation scene data set and the public data set show that the 3DPowerSegNet model can accurately extract the point cloud features of the target object in sparse point cloud environment, and the point cloud segmentation accuracy reaches 63.87%, 71.80% and 52.20%, compared with the original 3D-BoNet model. mAP increases by 2.80%, 4.51% and 6.97%, respectively.

Digital close-up photogrammetry technology can save time for construction project managers by eliminating the need to visit field sites for power line foundation quality inspection. However, existing projection density algorithms for power line foundation point cloud classification suffer from low accuracy. This paper proposes a multi-view grid-based point cloud classification algorithm for power line foundation. Firstly, the elevation difference features of the grid are constructed to coarsely classify the point clouds; then the plane point clouds and elevation point clouds obtained from the coarse classification are projected to the XOZ and XOY planes and divided into grids; finally, according to the elevation relationship between different types of point clouds in the point cloud, the seed grids for various types of point clouds are selected for neighborhood boundary tracking, and the boundary grids are distinguished according to the projected density of the grids, so as to realize the classification of the point clouds. The performance of the algorithm is evaluated by manually discriminating and counting the number of various types of point clouds as the real value, and the results show that the classification accuracy of the algorithm is better than that of the classification algorithm based on the projection density. The maximum measurement error of the length of the two groups of power line foundation is 2.137 mm, and the maximum measurement error of the width is 2.714 mm, which does not exceed the specification requirement of ±10 mm, and the maximum measurement error of the spacing of the same group of ground bolts is 3.124 mm, which does not exceed the specification requirement of ±3.6 mm. this method can realize the quality inspection of the power line foundation, and the measurement precision meets the construction and acceptance specification requirements.

This paper proposes a laser SLAM algorithm based on double-layer global pose map optimization to address the low accuracy and efficiency of traditional laser SLAM loop detection and the long optimization time of large-scale loop back-ends. This algorithm improves the LINK3D descriptor and calculates landmarks accordingly, achieving pre optimization of global keyframes for hypersubgraphs through joint constraints of landmarks and pose. Secondly, the pose factor graph is further optimized according to the pre optimization results to reduce the number of iterations and improve the efficiency and accuracy of the back-end optimization. Experiments on multiple sequences in the KITTI dataset show that the SLAM algorithm proposed in this paper reduces the backend pose optimization time by an average of 17.2% compared to traditional graph optimization methods. For large-scale scenes with many loops, the SLAM algorithm reduces the average translation error by 13.2% and the average rotation error by 13.3%, thereby verifying its effectiveness.

To address the challenges of inner contour priority constraints, multi-component nesting, and suboptimal local convergence and efficiency in existing artificial immune algorithms, this study proposes a laser cutting path optimization method combining multi-objective clustering and adaptive artificial immunity. This method redefines the path optimization problem as a generalized traveling salesman problem with priority constraints. After establishing the relationships among multiple nested components, a multi-objective clustering algorithm is utilized to optimize the objective function and enhance the initialization and clone proliferation of the antibody population. During the crossover and mutation processes of the artificial immunity algorithm, self-cycling crossover and adaptive mutation operators are introduced to further refine the optimization. Experimental results indicate that the proposed algorithm achieves an average error of 0.24% compared to the optimal solution for generalized traveler datasets. In laser cutting tests, the optimized algorithm reduces convergence iterations by 67.68% compared to the standard artificial immune algorithm. Additionally, the null shift path is decreased by 10.31%, 8.21%, and 4.81% respectively compared to the artificial immunity algorithm, the artificial immunity-ant colony hybrid algorithm, and the immunity particle swarm algorithm, significantly enhancing laser cutting efficiency.

As offshore wind energy expands into deeper waters, LiDAR (Light Detection and Ranging) is increasingly applied for regional wind resource assessment. To quantitatively analyze the wind characterization using LiDAR, this study deployed devices on the deck of an operational offshore wind farm′s substation and compared them with wind speeds and directions at the hub heights of wind turbines located at various distances. Wind turbine data were obtained through the Supervisory Control and Data Acquisition (SCADA) system. Pearson and Spearman correlation coefficients were used to analyze wind speed data at different locations, revealing a minimum correlation coefficient of 0.794 within a 5.416 km range, indicating significant correlation between wind speeds in the region. Moreover, both correlation metrics showed a decreasing trend with increasing distance. Throughout the experimental period, predominant wind directions were consistent across different locations. Analysis combined with wind turbine power curves showed thatthe maximum theoretical cumulative electricity generation error between different locations within the experimental range compared to LiDAR position was 11.256%. These results demonstrate that LiDAR provides crucial guidance for turbine siting within the experimental range, but enhancing wind resource assessments requires an increase in the number of measurement points to cover a broader area more accurately, thereby reducing assessment errors due to characterization range limitations.

In the non-horizon environment, the laser beam propagation is affected by the scattering medium such as air molecules and dust particles, which causes the scattering phenomenon. Traditional methods are difficult to effectively distinguish scattering effects from color changes at the edges of real objects when processing non view laser images, which reduces recognition accuracy. Therefore, a method for automatic recognition of salient region targets has been proposed. In the Lab color space, a reasonable color change threshold is set to reduce the interference of scattering effects on image quality. By combining the advantages of local and global saliency maps, the salient regions in the image are accurately identified, and diverse features such as color, texture, and shape are extracted. Based on this, a support vector machine (SVM) classifier is trained. After training, the support vector machine (SVM) classifier classifies the feature vectors and accurately assigns them category labels, constructing an efficient set of feature vectors, to identify salient region targets in non view laser images. Experimental results demonstrate that the target boundaries identified by this method are clear and complete in shape. The frame rate of the image processing reaches approximately 65.8 frames per second (FPS), which significantly enhances the efficiency of non-view laser image analysis technology.

To address the negative impact of moving samples on image quality and sample integrity in traditional light-sheet microscopy, a three-dimensional light-sheet imaging system incorporating a liquid zoom lens and galvanometer mirror is designed and constructed. Imaging experiments are performed on opaquestandard matte ceramic spheres, 3D printed translucent polylactic acid (PLA) samples, fluorescent microspheres, and apple pollen. The light-sheet microscopy three-dimensional imaging system is used to obtain images of different sections of the samples. By adjusting the galvanometer mirror and liquid zoom lens, a series of changing images of the samples were obtained. The feasibility of using scanning galvanometer mirror and liquid zoom lens to achieve 3D imaging of immovable samples is verified. The images of the sections are reconstructed in three dimensions to achieve the three-dimensional imaging of the samples. Extract, normalize, and fit the brightness information of fluorescent microsphere images.The experimental results of fluorescent microspheres and apple pollen show that the system has a lateral resolution of 10 m and an axial resolution of 19 m.

To study the clinical efficacy and safety of ultrapulsed fiber thulium laser and holmium laser in the treatment of ureteral stones. A total of 150 patients undergoing unilateral ureteral calculi surgery in our hospital from January to September 2023 were randomly divided into control group, T1 group and T2 group, and the general data of the three groups were recorded. The control group was treated with holmium laser, the T1 group was treated with ultra-pulsed fiber thulium laser with low-energy and high-frequency lithotripsy, and the T2 group was treated with ultra-pulsed fiber thulium laser with the same energy and frequency as the holmium laser group. The perioperative data of the three groups were recorded after surgery. There was no significant difference in the general information of the three groups. The mean operation time was 14.30 min±3.90 min in the control group and 11.10 min±4.10 min in the T1 group, and the difference was statistically significant (P<0.001), and the average displacement distance in the T1 group was 1.83 cm±0.45 cm, and the mean displacement distance in the control group was 2.58 cm±0.60 cm, with a statistically significant difference (P<0.001). The 2-week clearance rate of ureteral stones in the three groups was 86% in the control group, 98% in the T1 group, and 92% in the T2 group, respectively, and the stone clearance rate in the control group was lower than that in the T1 group, and the difference was statistically significant (P=0.027, P<0.05). There was no significant difference in complication rates between the three groups after surgery. Ultrapulsed fiber thulium laser is safe and effective in ureteral lithotripsy, and the stone displacement and effectiveness are better than holmium laser lithotripsy when using low-energy and high-frequency mode lithotripsy.