This study investigates the use of laser-arc hybrid welding to improve the forming quality and mechanical properties of joints in 6 mm thick low carbon steel and medium carbon steel rolled plates. The effects of various process parameters on the welding quality were analyzed, and hardness and tensile tests were conducted on samples with optimized parameters to elucidate the reasons for enhanced mechanical properties. The experimental results show that the arc heat input mainly affects the droplet transition form during welding. With the gradual increase of arc heat input, the droplet transfer changes from unstable short circuit transfer to stable droplet transfer, and the quality of weld formation is significantly improved. The depth of the weld is mainly affected by the laser heat input. The weld is considered to have the best quality when the laser heat input reaches 247.5 kJ/m, and completely penetrate. During the welding process, recrystallization occurs in the welded joint area due to the high temperature thermal cycle, resulting in a large number of fine subcrystals and dislocations, which is the fundamental reason for the improvement of mechanical properties of the welded joint.

Fiber and semiconductor composite laser welding has been widely used in the field of laser precision joining due to its ability to give full play to the unique advantages of fiber laser and semiconductor laser heat source. However, the laser process multi-parameters coupled with each other, the impact on the actual welding quality has not been clear, how to accurately predict the weld formation and process optimization is the key to improve the welding quality. Therefore, for the power battery 3003 aluminum alloy, this paper carries out fiber and semiconductor composite laser welding process test, systematic research on the effect of different energy ratios on the weld morphology of the law, and to elaborate the formation mechanism. The results show that the fiber laser mainly changes the keyhole absorption efficiency of laser energy, directly affecting the depth of fusion, while the semiconductor laser mainly determines the surface fusion width and internal effective fusion width. Meanwhile, a weld shape prediction model based on gradient boosted regression tree GBRT is constructed using laser process parameters as multidimensional input vectors, and its root mean square error (RMSE) is lower than 20%, with high prediction accuracy and strong generalization ability. Finally, the constructed prediction model realizes the optimization of laser welding process, and good welding quality can be obtained when the fiber laser power is 0.61.0 kW and the semiconductor laser power is 1.62.0 kW. This paper provides key theoretical support and guidance basis for fiber and semiconductor composite laser welding quality prediction and process optimization.

Laser direct deposition remanufacturing technology has the advantages of high energy density, controllable thickness of forming layer and good bonding performance, so it has become one of the effective ways of remanufacturing, but there are some problems in the forming process, such as spheroidizing effect, powder adhesion and pore defects, at the same time, subsequent processing is needed to meet the assembly requirements. Laser polishing has the characteristics of non-contact, green environmental protection and no material loss in the machining process, so it is one of the effective methods to improve the surface quality of adding materials. Based on this, the key processes of laser adding and polishing collaborative remanufacturing are studied for the damage of thin-walled parts, and the effects of optical increasing manufacturing and laser polishing process parameters on the microstructure and properties of remanufacturing are explored. The primary and secondary order of the influence of laser augmentation process parameters on dilution rate is powder feeding rate, laser power and scanning speed, and the factor that has the greatest influence on microhardness is laser power. In the optimization of laser polishing process, the effects of laser energy density and laser polishing scanning spacing on the microstructure and properties of laser reinforcements-polishing collaborative remanufacturing samples were studied, and the optimal parameter combination of collaborative remanufacturing was obtained. Compared to unpolished samples, optimized collaborative remanufacturing resulted in finer cellular and equiaxed crystal structures, with surface roughness reduced by 93%, porosity decreased by 62%, and microhardness increased by over 20%. The friction and wear properties have also been improved.

TiB2/18Ni300 composites with different mass fractions of TiB2 were prepared by selective laser melting technique, and the effects of different mass fractions of TiB2 on the surface morphology, microstructure, EDS spectra, XRD phase structure and mechanical properties of the specimens prepared were investigated. The results showed that with the increase of the TiB2 mass fraction, top surface of the specimens roughened, the spheroidization effect was enhanced, and the surface porosity enlarged. Meanwhile, the cellular crystal-type solidified structure gradually changed to columnar crystals, in which Ti elements were uniformly distributed. The main crystalline phases of the specimens were -Fe martensite, with a gradually increasingly intensified diffraction peak. Moreover, the Vickers hardness and tensile strength of the specimens showed a change trend of increasing at first and then decreasing, and the ductility gradually decreased. The specimen containing 0.5% TiB2 had the greatest hardness and tensile strength, which were 409 HV and 1485.8 MPa, 15.78% and 53.1% higher than those of the 18Ni300 specimen without TiB2 powder addition, respectively. Different from the plastic fracture of the 18Ni300 specimen, the fracture mode of the TiB2/18Ni300 specimen was plastic and brittle fractures.

Selective laser melting (SLM) is significantly influenced by the matching of process parameters, such as laser power, scanning speed, and laser overlap ratio, which directly affect the forming quality. This paper established the finite element method to simulate multi-layer multichannel for SLM process parameters, especially laser overlap rate, including thermal coupling model correction, and laser parameters setting, scanning path design. The relationship between the temperature field and thermal stress accumulation was obtained. According to the statistical analysis, laser overlap rate from 20% to 30% range is advisable. Then, the simulation results were verified by experimental samples with the same size. The four groups of samples respectively reflect the influence of different parameter changes on the residual stress, which verifies the effectiveness and scalability of the simulation. Finally, the actual laser overlap rate was discussed and the action value of the designed laser overlap rate was modified.

The surface of AZ31 magnesium alloy was treated by laser melting under pure argon atmosphere, and the microstructure, structure, and corrosion resistance of the ultra-thin AZ31 magnesium alloy surface melting layer prepared by low-power laser surface melting were studied through optical microscopy, scanning electron microscopy, and energy spectrum analyzer. The results indicated that the laser-melted layer exhibited a dense microstructure with grain sizes smaller than 20 m. The surface microhardness increased from 5158 HV to 7478 HV after treatment. After laser melting treatment, the solid solubility of Al element in the microstructure of the melting layer increases, and there is a sensitivity to solidification cracks during the laser melting process. After laser melting and solidification treatment, the corrosion potential of magnesium alloy increased by 0.285 V compared to the matrix structure, and the corrosion current density decreased by one order of magnitude compared to the matrix magnesium alloy, resulting in a certain degree of improvement in corrosion resistance.

This study investigates the effects of laser scribing process parameters on the surface quality and magnetic properties of 0.27 mm thick grain-oriented silicon steel. Experiments were conducted using industrial laser scribing equipment to determine the optimal parameterization for balancing surface quality and magnetic properties. The results show that laser scribing damages the insulation coating on the surface of grain-oriented steel and this damage is deepened with the increase of laser power. When the laser power is 4.5 kW, the average depth of groove exceeds the total thickness of insulation coating, resulting in the exposure of substrate. With the increase of notch spacing, iron lose reduction ratio increases first and then decreases, so the best result could not be obtained if the notch spacing is too large or too small. The magnetic induction of grain oriented silicon steel reduces overall after laser scribing, and the reduction expands with greater laser power. However, the reduction of magnetic insulation can be controlled within 0.01 T when the laser power is in the range of 3.54.3 kW. Within the scope of the process examined in this study, the optimal laser scribing parameters are laser power of 4.3 kW and notch spacing of 5 mm, which can reduce the iron loss of grain-oriented silicon steel by 13.43%, with hardly noticeable damage of insulation coating.

In order to study the effects of laser parameters and repeated scanning times on laser cleaning, based on the principle of thermal ablation effect, combined with Fourier′s law of heat transfer and conservation of energy, the model of laser cleaning zinc oxide yellow primer on 6061 aluminium alloy surface was established by using simulation software. The principle of laser paint removal was discussed, and the effects of changes in laser power and scanning times on the temperature field, cooling rate, ablation depth, and flatness of the paint layer were analyzed. The results show that when the repetition rate is 2.5 kHz and the spot overlap rate remains unchanged at 60%, the temperature and ablation depth of the paint layer increase with the increase of laser power, and the cooling rate of the paint layer near the substrate is slower when the temperature is lower. When the laser power exceeds 50 W, the surface paint layer can be completely removed, while when the laser power exceeds 60 W, the substrate will be slightly damaged. When the laser power is 30 W and other parameters remain unchanged, the surface temperature and ablation depth of the paint layer will increase with the increase of scanning times, and the surface flatness will gradually improve. When the number of scans is 3, the paint layer is completely removed and the substrate surface is smooth and undamaged. Through multiple simulation analyses, the variation patterns of cleaning effects with laser power and scanning frequency were determined. These findings provide a reference for theoretical analysis of laser removal of aluminum alloy paint layers and the selection of process parameters.

The fabrication of polymethyl methacrylate (PMMA) gradient wetting surfaces using femtosecond lasers is significant for controlling droplets in microfluidic channels. In this paper, parallel microgroove structures are fabricated on PMMA surfaces using femtosecond laser. Gradient wetting surfaces with different properties are obtained by designing laser energy density parameters. Contact angle measurements, laser confocal microscopy, energy spectrum analysis, and scanning electron microscopy are used to measure the contact angle, roughness, chemical element composition, and surface morphology of PMMA surfaces, respectively. A high-speed camera is used to observe the spreading trend of droplets on gradient wetting surfaces. The results show that as the laser energy density increases, the surface contact angle first increases and then decreases. The surface roughness parameters, root-mean-square height and arithmetic mean height, both exhibit an upward trend. The content of carbon on the surface decreases while the content of oxygen increases. The larger the gradient difference of the gradient wetting surface, the faster the droplet spreading speed. This study on the reliable control of surface wettability by femtosecond laser provides a theoretical reference for the fabrication of PMMA microfluidic channels.

The laser cleaning processing characteristics and evaluation methods of thermal barrier coating on aeroengine guide blades were studied. The relationship between laser processing parameters such as laser average power, cleaning speed, laser incidence angle, defocus and cleaning quality was discussed. The key factors affecting cleaning quality in laser cleaning processing were invested. The quality evaluation standards of laser cleaning was established based on traditional cleaning methods. The results show that the thermal barrier coating on the surface of turbine guide vane could be effectively removed by nanosecond pulse laser cleaning technology. Through the four dimensions of macro surface cleanliness, macro surface condition, micro surface cleanliness and micro surface condition, the laser cleaning quality of thermal barrier coating on blade surface could be effectively evaluated. Defocus, cleaning speed and incident angle were the three key factors affecting the laser cleaning quality of thermal barrier coating on blade surface. The optimized laser cleaning processing parameters of thermal barrier coating on blade surface were as follows: the average laser power was 340 W, the defocus was 2 mm, the cleaning speed was 3 mm/s, the linear spot width was 8 mm, the incident angle was 90°, and the galvanometer speed was 1 500 mm/s.

CrFeNiCoMnMo0.5 and CrFeNiCoMn high-entropy alloy (HEA) coatings were successfully prepared on the surface of automotive steel using laser coating technology. The influence of Mo addition on the microstructure, phase composition, crystallographic properties, nanoindentation characteristics, and wear behavior of the coatings was systematically investigated. The results revealed that the CrFeNiCoMnMo0.5 HEA coating comprised an FCC phase and a phase. The incorporation of Mo increased lattice distortion within the FCC phase, enhancing its solid solution strengthening effect. Consequently, the nano-hardness of the CrFeNiCoMnMo0.5 coating was significantly improved, reaching 7.81 GPa± 0.13 GPa, which was markedly higher than that of the CrFeNiCoMn coating. Additionally, the CrFeNiCoMnMo0.5 coating exhibited higher H/E and H3/E2 ratios, signifying exceptional resistance to plastic deformation. The enhanced nano-hardness resulted in superior wear resistance, with an average friction coefficient of 0.48 and a specific wear rate of 2.307×10-6 mm3/(N·m). The primary wear mechanisms were identified as abrasive wear, adhesive wear, and surface fatigue wear. This study underscores the critical role of Mo in improving the mechanical and wear properties of high-entropy alloy coatings for automotive applications.

Laser thermal effects can cut glass by inducing stress, but high temperatures may lead to burns or thermal cracks. This paper employs ABAQUS to study temperature distribution and heat time-integral flux (HTL) influence during laser induced thermal-crack propagation (LITP), examining how HTL impacts ablation, surface morphology, roughness, and straightness to establish a threshold for undamaged cuts. The results indicate that: when the HTL is lower than 14.5 J/cm3, the laser energy is not sufficient to cut the glass effectively; in the interval of 14.5307.2 J/cm3, the cut cross-section and the surface of the glass samples are smooth, straight, and damage-free; when the HTL is higher than 307.2 J/cm3, the cut cross-section starts to show obvious thermal damage, ablation, and chipping of the cut edges.

Aiming at the problems of missing identification of pole columns and misclassification of pole tops caused by partial missing of vehicle-mounted laser point cloud, a progressive pole target identification method considering spatial features is proposed. The method effectively solves the problem of accurate identification and classification of intermittent pole targets in the point cloud by using a cascaded random forest model and spatial relationship features. Firstly, the spatial relation features between continuous arcs in the vertical direction are obtained by the arc morphology features of the top model of the cascade structure in the multi-scale node slices, and the accurate identification of intermittent poles is achieved by combining the arc morphology features; secondly, based on the pole identification results of the cascade model, the top clusters of the vertical pole clusters are obtained, and the spatial features of poles and pole tops are obtained by the spatially relationally constrained ESF shape features combined with PCA dimensional features using the cascade model to achieve accurate classification of pole tops. The experimental results show that the recognition accuracy of this method reaches 96.56% and 94.51% in two sets of experimental data, which can effectively deal with the recognition and classification of pole targets in complex road scenes and has strong stability.

To address the issues of low automation and segmentation errors caused by parameters in power line point cloud extraction, this paper proposes a power line extraction method based on an improved spatial density clustering algorithm, combined with the distribution characteristics of airborne LiDAR point cloud data. Firstly, the proposed method completed the rough extraction of power line point cloud through the improved elevation filtering algorithm. Then, the optimal parameters of spatial density clustering were obtained by the distance-density method and the mathematical expectation calculation method, avoiding the complicated manual parameter adjustment process. Experimental results show that compared with the spatial density clustering algorithm, the proposed algorithm has significantly improved efficiency, reduced the power line extraction time by about 60%, and realized the automatic and efficient extraction of single power line point cloud.

To address the issue of point cloud noise affecting the accuracy of external parameter calibration for different LiDARs, an adaptive joint calibration method for LiDAR and camera based on DBSCAN is proposed. In this method, firstly, pass-through filtering is used to pre-process point clouds to obtain checkerboard corner points, secondly, KDTree is constructed to obtain the density threshold within the radius of corner points. Finally, based on the obtained density threshold, DBSCAN algorithm is used to cluster the complete checkerboard point clouds, thus avoiding the problem of time consuming and precision reduction caused by the inconsistency of different LiDAR external parameter calibration methods. The experimental results show that the efficiency and accuracy of the method are improved, the calibration efficiency is increased by 4-6 times, and the reprojection error is reduced by about 29.8%. The resulting external parameters are suitable for engineering applications.

To address surface defects such as flying edges and grooves that commonly occur during friction stir welding, an embedded system for image processing of friction stir welds has been designed. First, GigE industrial cameras and line lasers were used to capture images of the weld seam. Approximate polygons of laser strips were fitted by contrast enhancement, iterative method threshold segmentation, edge fitting, etc. The dimensions of flying edges and grooves were obtained by methods such as Shi-Tomasi corner point inspection. Finally, the fuzzy PID control algorithm was used to regulate the amount of downward pressure, and experiments are conducted to verify the feasibility of the system. The results showed that the method can quickly and accurately detect and eliminate flying edges and grooves, and is highly adaptable to meet industrial requirements.

In order to detect bio-molecules, an arrayed micro-cantilever sensing system was designed. The system utilizes the optical lever principle and silicon-based micro-cantilevers to detect bio-molecules. This study integrates a temperature control system with a reaction chamber, achieving system integration, and utilizes a micro-moving platform to move the light spot between the arrayed micro-cantilevers. After constructing the detection platform, the system was calibrated, and its stability and accuracy were evaluated. Experimental results demonstrate that the actual resolution of the sensor can reach 0.03 m, and the accuracy of the entire sensing system, measured using the thermal effect of bimetallic micro-cantilevers, is 96.1%. The achieved resolution and accuracy of the entire sensing system have been confirmed to meet the intended objectives through the utilization of bimetallic micro-cantilevers′ thermal effect.

LiDAR point cloud data acquisition is often affected by reflective objects such as glass and mirrors, which produce specular reflections and introduce mirror reflection noise. Due to the similarities between mirror reflection noise and the corresponding physical point clouds, traditional denoising methods struggle to effectively remove such noise points. To address this challenge, this paper proposes a method for identifying and removing mirror reflection noise based on the similarity and difference between the two. First, the scene point cloud data is divided into several point cloud blocks, and the mirror symmetry plane is extracted through the random sampling consensus algorithm, and then the Euclidean distance from the point cloud block to the mirror symmetry plane is judged to preliminarily screen out the mirror reflection noise, and the 3D point cloud registration is performed The similarity detection with the two-dimensional depth image accurately identifies the mirror reflection noise, and finally the point cloud convex hull is used to calculate the density value combined with Euclidean clustering to remove mirror reflection noise. The experimental results show that this paper can accurately identify the mirror reflection noise and remove it effectively.

This paper presents a new algorithm for center extraction of line structured light. Traditional segmentation algorithms often suffer from issues such as holes, discontinuity, and inaccuracy in image segmentation. To address these limitations, the EAST deep learning model is employed to accurately and efficiently extract the region of interest of laser stripes. This approach mitigates the susceptibility of traditional algorithms to noise interference and improves segmentation efficiency. In addition, the gray barycenter method is used to initially extract the detected effective light strip area, and finally the optimization algorithm is used to perform secondary optimization on the initial center line to realize the accurate extraction of the sub-pixel coordinates of the light strip center. The results show that the algorithm can more accurately extract the center of the line-structured light strip, which ensures the accuracy and stability of the strip center extraction.

LiDAR intensity data can provide good spectral separation, which is an ideal data source applied to shield tunnel disease detection. In this paper, we take shield tunnel point cloud data as the research object and propose an intensity correction model based on a natural homogeneous target by analyzing the influencing factors of tunnel intensity information. And based on the corrected intensity data and the generated tunnel surface intensity image to achieve the detection of water seepage area. The experiments show that the model proposed in this paper can effectively eliminate the intensity deviation caused by distance and incidence angle and performs well in the coefficient of variation ratio, and the corrected intensity information of similar materials tends to be consistent. The actual water seepage area extracted based on the corrected intensity and the generated intensity image at 610640 m is 4.17 m2, accounting for 1.05% of the total area of the interval, and 36 water leakage areas are correctly detected in this section of the tunnel, indicating that the water seepage disease areas can be effectively identified.

The rapid advancement of science and technology has led to the widespread application of optimization algorithms in various fields, including healthcare, industry, and commerce, due to their efficient information processing capabilities. The state evolution of fiber lasers is a complex process influenced by multiple interacting factors, necessitating precise parameter adjustments to achieve optimal mode-locking states. Integrating optimization algorithms into mode-locked fiber lasers has emerged as a key research area, addressing the stability challenges associated with mode-locked fiber lasers. This article firstly introduces the mode-locked fiber lasers based on optimization algorithms, and explains the various optimization algorithms used in mode-locked fiber lasers. Secondly, the combination of fiber lasers and optimization algorithms was analyzed to optimize the mode locking state of lasers, including the time taken for the system to achieve mode locking, robust control of the mode locking state, different mode locking states, soliton pulsation, soliton dynamics, and pulse prediction. Finally, the problems faced by optimization algorithms in mode-locked fiber lasers were summarized, and their future development was prospected.