Selective Laser Melting (SLM) technology was employed to conduct single-pass monolayer scanning experiments on cobalt tungsten carbide alloy powder under, exploring various combinations of process parameters. Analysis of the single-pass width variation under different parameters, particularly focusing on lap rate and energy density, elucidated the formation mechanism of monolayer surface morphology. The effects of laser power, scanning speed, and scanning pitch on roughness were investigated using the Super Depth of Field 3D Microscope System. Additionally, the cross-sectional morphology was examined via Scanning Electron Microscope (SEM), with elemental distribution analyzed using SEM's attached energy spectrometer. Results revealed that laser power and scanning speed predominantly influenced monolayer width through line energy density, with higher line energy density resulting in wider monolayer width. In the process of monolayer formation, the lap rate emerged as a pivotal factor in monolayer morphology, with higher lap rates correlating with lower overall surface roughness. Various process parameters affect the lap rate of the monolayer, thereby impacting its morphology. Laser power and scanning speed influence single-channel width, consequently affecting the lap rate within the same scanning spacing. Additionally, a certain combination of laser power and scanning speed directly affects the lap rate of the monolayer when the laser scanning spacing is fixed. Agglomeration of tungsten (W) elements within the monolayer cross-section was observed, potentially impacting surface quality. Optimal surface quality, with a roughness of 6.05 μm, was achieved at a laser power of 420 W, scanning speed of 500 mm·s-1, scanning spacing of 125 μm, and powder layer thickness of 40 μm.

Laser melting deposition (LMD) has the potential to achieve high-performance manufacturing of complex components, however, its process is affected by multiple factors, and ensuring the stability and consistency of the component formation process can be challenging. Existing control schemes include physical model-based and data-driven approaches. Considering the difficulty in establishing accurate mechanistic models to describe the complex LMD process, data-driven methods are employed for controller design. For the layer-by-layer processing principle of multi-layer LMD process, this study proposes a controller based on the Model-Free Adaptive Iterative Learning Control (MFAILC) algorithm. As an intelligent control strategy, MFAILC has the ability to control complex and time-varying systems with repetitive running characteristics and has been applied in various fields. Multiple experiments were conducted on a home-built LMD system, and the thermal imaging camera integrated with the system was used to collect and extract the temperature of the melt pool. To tune the controller parameters, a neural network model based on the experimental dataset was constructed to simulate the process. The designed MFAILC controller is validated by printing experiments based on typical annular thin-walled specimens. Compared with the experiments under constant parameters, the range of deposition height and width of the annular thin-walled structure printed using the MFAILC controller was reduced by 28.6% and 15.4%, respectively, and the MAE was reduced by 31.5% and 48.8%, respectively, and the RMSE was reduced by 33.4% and 35.0%, respectively. The experimental results and comparative analysis show that the designed MFAILC controller can effectively improve the stability of the process and the quality attributes of manufactured parts.

With the increasing demand for innovative design and manufacturing of parts in the manufacturing field, emerging more complex design inspirations, which caused traditional processes cannot to meet the manufacturing needs of the complex features. The integral forming of complex parts can be achieved by additive manufacturing (AM) technology as a new manufacturing process, which with a high degree of freedom of manufacturing, and the material utilization rate is greatly improved compared with the traditional subtractive process. This paper introduces the airworthiness verification criteria for laser additive manufacturing technology and examines the quality control and airworthiness verification concepts within the AM process. It analyzes the airworthiness development process for materials and structural properties in additive manufacturing, presents the current key methods in additive manufacturing product design and airworthiness verification, and outlines the development direction and path for metal additive manufacturing airworthiness technology. The aim is to facilitate broader application of metal additive manufacturing in civil aircraft manufacturing.

To enhance the wear and corrosion resistance of crankshafts, two types of cladding layers, Stellite6 and Stellite6+5% B4C, were applied to the surface of 42CrMo steel using a laser cladding preset method. The cross-sectional morphology of the cladding layers was examined using an industrial microscope, while their microstructure and phase composition were characterized by SEM, EDS, and XRD analyses. The hardness, abrasion resistance, and corrosion resistance of the cladding layers were evaluated using a microhardness tester, a multifunctional friction grinder, and an electrochemical workstation. The results show that both types of cladding layers have good metallurgical bonding with the substrate, and the dilution rates of Stellite6 and Stellite6+5% B4C cladding layers are 23% and 47%, respectively. Under the combined effect of fine grain strengthening, solid solution strengthening, and second phase strengthening, the hardness of the Stellite6+5%B4C cladding layer reaches 895.73 HV, which is 3.38 times that of the substrate. The addition of B4C reduces the friction coefficient from 0.55 to 0.46, significantly reduces the wear depth and width, and reduces the wear amount by 84%. The Stellite6+5% B4C cladding layer has the highest self corrosion potential, the lowest self corrosion current, and the best corrosion resistance.

To enhance the quality of TC4 titanium alloy cladding coating, a hybrid multi-objective optimization model integrating response surface methodology (RSM) and the non-dominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ) was developed. The model focused on optimizing the laser power, feeding speed, and scanning speed as factor, with the objectives of minimizing the dilution rate, maximizing the aspect ratio, and enhancing microhardness. The regression model of the relationship between input and response based on RSM was constructed, and the reliability of the variance analysis model was used. Then NSGA-Ⅱ algorithm was used to realize the multi-objective optimization of the process. The results show that the optimal combination of process parameters is as follows: laser power 2 250 W, scanning speed 19 mm/s, powder feeding rate 1.3 r/min. The optimization resulted in substantial improvements, with a 13.2% reduction in the dilution rate, a 7.1% increase in the aspect ratio, and a 6.4% enhancement in microhardness.

To address the challenges related to poor wear resistance, high friction coefficient, inadequate grinding performance, and low hardness of TC4 titanium alloy, this study aims to enhance its applicability. Composite coatings were fabricated on TC4 substrate using varying proportions of Ni60 powder and Ni/MoS2 powder. The impact of different powder ratios on coating quality was assessed through analyses of coating morphology, microhardness, and friction reduction performance. Experiments show that the macroscopic morphology of the prepared composite coating is well formed, but its cross-sectional morphology changes from gentle to excessive to convex at the junction of the cladding layer and the substrate with the increase of Ni/MoS2 content, and defects such as porosity and cracks appear. Coating microhardness and Ni/MoS2 addition is positive correlation, friction coefficient and Ni/MoS2 addition of the first decline and then rise trend, comprehensive comparison, when the addition of Ni/MoS2 mass fraction of 0.2 can obtain a more excellent composite coating, the microhardness of the coating can reach 950—1 000 HV, about 2.8 times the TC4 substrate, and the coating friction coefficient is reduced to 1/3 of the friction coefficient of TC4 substrate. These findings demonstrate that the proposed experimental approach effectively mitigates the low hardness and poor wear resistance issues of TC4 titanium alloy in practical applications.

This study conducted laser-MAG hybrid welding on 12 mm thick AH36 high-strength steel without beveling. The influence of the distance between the laser and the wire on weld bead formation was investigated, and high-speed photography was used to observe and analyze the behavior of droplet transfer and molten pool flow during the welding process. The results show that as the distance between the laser and the wire increases, the coupling effect of the two heat sources gradually weakens, the molten pool in the keyhole area gradually separates from the arc action area, the keyhole becomes unstable, and the flow behavior of the molten metal fluctuates significantly. When DLA (Distance between Laser and Arc) is 4 mm, the best weld bead formation is achieved; when DLA is 0, 2 mm, the weld has defects such as spatter and undercut; when DLA is 6, 8, 10 mm, the weld formation has defects such as depression, porosity, and hump. As DLA increases from 0 mm to 10 mm, the frequency of short-circuit transition first decreases and then increases, and the laser′s hindrance effect on the jet transition gradually weakens until it disappears.

The laser deep fusion welding test of 20mm thick 05Cr17Ni4Cu4Nb/HR-2 dissimilar stainless steel was carried out by using different defocus, and the microstructure of the welded joint was analyzed by optical microscope, scanning electron microscope and energy spectrum. The mechanical properties of welded joints were studied by microhardness tester, electronic universal material tester and metal pendulum impact tester. The test results show that the weld is well formed and no welding defects occur under different technological parameters. The microstructures of weld seams are all δ ferrite+austenite, but the morphology and quantity of δ ferrite change. With the increase of defocusing amount, the grain in weld zone is gradually refined, and the microhardness of weld zone is improved, which is higher than that of HR-2 base metal. In the three-point bending test, the bending positions all appear on the HR-2 base metal side, indicating that the welded joint has good plasticity. When defocusing is 10mm, vermicular ferrite in weld zone improves weld toughness and the impact toughness reaches the maximum.

Conformal antenna has the advantages of low profile, low radar scattering cross-sectional area, and can satisfy arbitrary aperture installation, making it one of the key development trends of advanced weapon equipment antenna in the future. However, there are still many shortcomings in the current antenna fabrication process such as small antenna front curvature and assembly problem of antenna front and conformal structure, which restricts the development of conformal antenna. In this paper, a novel integrated manufacturing process of curved conformal antenna with multi-layer interconnected circuits has been researched by combining the advantages of 3D printing technology, laser technology and electroplating technology. The line accuracy, bonding strength and electrical performance of the manufactured conformal antenna sample is tested. The results show that the antenna conductive lines have high precision, good adhesion and good weldability. The resistivity of the copper layer is measured at 1.5×10-6 Ω·cm, and both the voltage standing wave ratio and gain of the antenna element meet the design specifications, fulfilling practical application needs. The outcomes of this research significantly contribute to the development of curved multilayer circuit forming processes and the advancement of conformal antenna technology.

This study investigates the laser drilling of theroplastic materials, focusing on the performance of four different lasers: ultmviolet nanosecond, green nanosecond, CO, continuous, and fiber nanosecond lasers. The ultraviolet nanosecond laser was identified as the most suitable for achieving optimal drilling quality. A systematic analysis was conducted to examine the influence of scanning times and speeds on the drilling quality. The results show that scanning times have obvious effects on diameter and taper of hole, and scanning speeds have obvious effects on diameter, tape, roundness and heat-affected zone of hole. For processing 0. 3 mm through-holes in a 1 mm thick theroplastic, the optimal laser parameters were detennined to be an average power of 10 W, a repetition rate of 60 kHz, zero defocus, 120 processing tines, and a scanning speed of 300 mm/s.

Based on the WOS database, this paper collects a total of 3621 relevant literatures in the field of artificial intelligence and laser technology from 1989 to 2022. The visualization software DDA and CiteSpace were used to conduct bibliometric analysis of the literatures distribution characteristics, hot topics, keywords clustering, emerging trends and highly cited papers. The results show that the number of published papers in China ranks first in the world, and the total number of citations ranks second in the world. However, the frequency of cited articles is obviously low, lower than that of the global average mean. The overall quality of papers need to be further improved. Machine learning, deep learning, convolutional neural network, laser radar, artificial neural networks, additive manufacturing, classification, point cloud, laser-induced breakdown spectroscopy and feature extraction have become the hot keywords in this field. Including life health, laser radar, additive manufacturing and elemental analysis of four major applications. Among them, keywords such as deep learning, additive manufacturing, 3D display, point cloud, laser powder bed fusion and convolutional neural networks have become research trends in recent years.

Abstract Mobile laser scanning systems encounter difficulties in precisely locating the center of conventional targets within highway tunnels due to high vehicle speeds and complex environments. This study designed a planar target suitable for highway tunnel scanning, and proposed a robust target center positioning method based on this planar target. Firstly, combined filtering, euclidean clustering segmentation, and shape constraints are used to accurately extract monomer target, then, the time distribution characteristics of the target point cloud are used to extract the target boundary, and then, the standard boundary template with a center point is matched with the extracted boundary to achieve robust template matching to locate the center of the target, finally, the reliability of the proposed method is verified in a 3.5 km long highway tunnel, and its accuracy is compared with the traditional centroid method. The experimental analysis results show that the target in this paper has strong adaptability to the environment, compared to the centroid method, the method in this paper achieves accurate center positioning of incomplete targets, when the target is complete, the center positioning accuracy of both the method and the centroid method is 1-3 cm, when the target is incomplete, the center positioning accuracy of the method in this paper is improved by 1-4 cm compared to the centroid method.

The extensive coverage and complex terrain of power transmission lines pose significant challenges for traditional manual inspection methods, necessitating more efficient approaches to meet contemporary power industry demands. In recent years, the use of unmanned aerial vehicles (UAVs) equipped with LiDAR for power inspection has received widespread attention. However, the point cloud data obtained from inspections are massive, requiring more intelligent point cloud automation processing methods. Therefore, this paper proposes an automatic semantic segmentation method based on deep learning networks for LiDAR point clouds of power corridors to extract targets such as power lines and trees for hazard analysis. For vegetation and power line distance measurement in safety hazard detection, a projection-based power line fitting algorithm is designed. Additionally, a projection-based tree height detection algorithm is proposed for tree height measurement. Experiments conducted on LiDAR point cloud data provided by power grid companies demonstrate that the proposed semantic segmentation algorithm achieves an average accuracy of 84.92% across six different power corridor scenarios, with key point clouds like power lines, vegetation, and poles exceeding 90% accuracy. The tree height measurement error is confined within ±0.5 meters, and the distance detection algorithm accurately identifies danger points based on predefined safety margins.

In view of the fact that most of the existing tree height estimation models are based on a single parameter and tree height, the R2 goodness of fit of the model is not high and the accuracy is relatively rough. In this paper, a multi-parameter combined tree height estimation method based on multiple linear model is proposed. In this method, the height of Metasequoia glyptostroboides is estimated by multiple linear model with different parameter combinations, which improves the accuracy of tree height estimation and solves the problem of low goodness of fit of tree height estimated by single parameter model. In addition，in this paper a multi-time series Metasequoia street tree dataset is introduced and tested it through this method. The experimental results show that the goodness of fit between tree height and actual tree height obtained by the combination of diameter at breast height and subbranch height in multi-parameter combination is 0.984. Compared to the single parameter models, the goodness of fit increases by 0.3 to 0.4. Based on the parameters of individual tree structures, the accuracy of tree height estimation improves by approximately 30% to 40%, and the estimated growth trend aligns with that of mature Metasequoia forests.

3D laser scanning technology, while effective for acquiring point cloud data, often encounters limitations due to factors such as the measured object, measuring instrument, and environment, leading to incomplete data and holes within the dataset. Utilizing such incomplete data directly can compromise the quality and precision of subsequent modeling and reconstruction processes. Consequently, hole repair is a critical step. This paper presents an enhanced radial basis function (RBF) algorithm for repairing point cloud data, which improves upon the accuracy and speed of the original method, making it particularly suitable for repairing convex models. This algorithm implemented in Visaual Studio 2019, repairs point cloud holes by detecting hole boundaries, initializing grids, least squares grids, establishing implicit surfaces, and adjusting points to implicit surfaces. Experiments show that this method can fill the holes on the surface well, and the repair position remains smooth with the original area, which can be applied to the repair of indoor irregular objects, caves and tunnels.

To mitigate the impact of multi-scale noise present in 3D point cloud data acquired through 3D laser scanning on subsequent processing stages, a denoising approach leveraging normal vector direction information for feature classification has been developed. Firstly, the algorithm removes the large-scale noise in the point cloud model by statistical filtering combined with radius filtering, and then uses the principal component analysis method to obtain the normal vector information of the point cloud, constructs the histogram of normal orientations through the angle between the local neighborhood normal vectors, divides the point cloud into a plane area with less feature details and a feature area with rich feature details. For different regions, bilateral filtering and adaptive guided filtering are used to denoise small-scale noise in point clouds. Experimental results show that the proposed method can effectively remove the multi-scale noise of the point cloud model, and also maintain the feature details of the model well.

This study investigates and analyzes an ultra-wideband water-based metamaterial absorber. The unit cell consists of a shell, a water layer, and a metal backplane, with the shell fabricated from photosensitive resin and the water layer comprising an ‘X′ shaped body and four spheres. Designed for the millimeter wave band, simulation results indicate that the absorber′s performance exceeds 90% absorption within the frequency range of 22.7 GHz to 63.9 GHz when illuminated by vertically incident electromagnetic waves. Perfect absorption is achieved within the frequency range of 39.5 GHz to 44.4 GHz. The absorber exhibits polarization insensitivity and wide-angle incidence characteristics. Additionally, equivalent dielectric theory is applied to analyze the equivalent impedance of the absorbing material. Given the unique absorption frequency band, the proposed ultra-wideband water-based metamaterial absorber holds significant potential for applications in millimeter wave communication systems.

Blood is a prevalent carrier of biological information at crime scenes, often present in various forms and intermixed with environmental substances. Deciphering the age of bloodstains can offer crucial clues and investigative directions for revealing the truth behind crimes and discovering evidence. Spectral analysis technology, renowned for its rapid and non-destructive testing capabilities, has emerged as a classic technique in the analysis of bloodstains. This paper delineates the biochemical conversion processes and influential factors associated with bloodstains, with a particular emphasis on advancements in modern spectral analysis technology for studying bloodstain aging patterns. The aim is to offer concrete references and insights for the application of these techniques in forensic science research.

Objectives: Toassess the safety and efficacy of 1 470 nm laser en bloc resection (ERBT) compared to transurethral resection of bladder tumor (TURBT) in the treatment of primary multiple non-muscle invasive bladder cancers (NMIBC). Methods: Clinical datafrom 57 patients with multiple primary NMIBC treated at the Department of Urology, Shantou Central Hospital from October 2020 to July 2022 were analyzed. Of these, 25 patients underwent 1 470 nm laser en bloc resection, and 32 underwent TURBT. Both groups received identical postoperative maintenance bladder irrigation chemotherapy. Perioperative clinical characteristics, specimen quality, perioperative complications, and recurrence rates were recorded and compared. Results: There were no differences with the preoperative characteristics between the patients in the two groups. The 1 470 nm laser group was superior than the TURBT group in terms of the postoperative bladder irrigation time（0.88±0.14 vs 1.24±0.53）, obturator nerve reflection（4% vs 21.82%）and bladder perforation（0 vs 15.64%）, with statistically significant differences (P<0.05). Detrusor was found in 88% of the 1 470 nm laser group and 46.87% of the pathological specimens in the TURBT group (P<0.05). After a mean follow-up of 10.8 (10-18) months, the recurrence rates outside the wound margin were 12% in laser group and 12.5% in TURBT group, and the in situ recurrence rates were 8% and 9.37% respectively. The recurrence rate did not differ significantly between two groups. Conclusions: The study indicates that 1 470 nm laser en bloc resection is a viable, safe, and effective alternative treatment for primary multiple non-muscle-invasive bladder tumors.