The selective laser melting (SLM) process of AlSi10Mg alloy is prone to pore defects and surface spheroidization, which can be mitigated through laser remelting treatment. This study focuses on three common pore defects—lack of fusion, gas holes, and keyholes—in AlSi10Mg alloy produced by SLM. The impact of remelting power on the density, surface roughness, and melt pool depth of the samples was examined to determine the optimal remelting power. The results show that the un-melted AlSi10Mg alloy power is remelted using laser remelting treatment, the lake of fusion defect can be eliminated, the keyhole and gas hole defects can be decreased, and the optimal remelting power is different for different pore defects. In addition, the laser remelting treatment can improve the surface morphology of the samples, eliminate the surface spheroidization phenomena and reduce the surface roughness.

Additive manufacturing(3D printing), as an advanced forming method, has natural advantages in the manufacture of complex porous structures. In order to study the process-performance rules of 3D printing formed porous structure,In this paper,the porous structure is formed based on the Ti-6Al-4V alloy (TC4) material and the 3D printing method of selective laser melting (SLM), through orthogonal experimental design, the SLM selective laser melting process parameters including laser power, scanning speed, and lap distance were selected as the experimental factors, and the range and variance analysis were used to study the effects of different process factors on metal selective laser melting (SLM) 3D printing. The influence law of mechanical properties of porous samples and the degree of influence of related factors on different performance indexes. Finally, the linear regression equation relationship between relevant factors and performance indicators is obtained by using the linear regression equation fitting method, and the theoretical performance value of the optimal performance combination is predicted through the regression relationship equation and compared with the actual test value. The theoretical value and the actual value have the good correspondence proves the feasibility and accuracy of establishing the functional mapping relationship between factor conditions and performance for predicting the performance of SLM-formed porous structures by means of orthogonal experiments

Selective laser melting (SLM) of metal materials often results in irregular grains structures and high thermal stress, issues that can be addressed through heat treatment to achieve a more uniform microstructure and reduced residual stress. Therefore, the effects of three different heat treatment processes on the microstructure and mechanical properties of selective laser melting FeSiB/316L stainless steel were studied in this paper. It was found that after heat treatment at 400 ℃ for 2h, the internal grains were refined, resulting in an increase in dislocation density. Its tensile strength increases and the elongation did not change much. However, after heat treatment at 700 ℃ for 2 h, the grain size becomes larger, resulting in a decrease in dislocation density. The tensile strength decreased, but the elongation increased by 28% compared with the unheated FeSiB/316L composite sample. After heat treatment at 1 050 ℃ for 2 h, the structure of molten pool boundary and columnar crystal disappeared, which reduced the deformation resistance. The microhardness and tensile strength decreased by 42%, and the elongation increased by 2.5 times.

During the preparation of W18Cr4V cladding layers, laser-induced defects such as holes, unfused particles, and cracks can hinder the extensive application of laser cladding technology in manufacturing and processing. This study aims to identify optimal parameters for W18Cr4V cladding suitable for grinding by conducting an orthogonal experiment with four factors at four levels. The cladding efficiency, aspect ratio and surface roughness after grinding were used as the performance characterization indexes of the cladding layer to study the laser cladding technology. The performance data of cladding layer were obtained by orthogonal experiment method, and the signal-to-noise ratio method was used to process the performance data. Then range analysis method was used to observe the main and secondary effects of laser cladding process parameters on the properties of the cladding layer, and analyze the most influential factors. Finally, the three performance indicators are transformed into a single grey correlation degree by grey correlation analysis for optimization. The results show that optimal comprehensive performance is achieved with laser power set at 1 900 W, scanning speed at 8 mm/s, gas flow at 10 L/min, and defocus at 9 mm, yielding a cladding efficiency of 15.613 mm3/s, an aspect ratio of 0.641, and a surface roughness of 0.734 m.

In this paper, the lap welding process of 0.2 mm thick 430 stainless steel is investigated by using a pulsed laser welder, The macroscopic morphology and microstructure of the joint weld cross-section were observed and analyzed, and the effects of different laser powers on the mechanical properties of the joint weld were investigated. The results show that when the welding speed is 10 mm/s, the defocus amount is 0 mm, and the laser power is 130 W, the two plates realize the fusion connection, the weld surface is well formed, and the joint tensile force reaches 2 714 N. The joint molten core area is mainly composed of coarse columnar crystals of ferrite and a small amount of martensite, and it grows perpendicularly to the fusion line towards the center of the weld. The heat-affected zone is narrow at the top and wide at the bottom and consists of coarse equiaxial crystals of ferrite and nodular carbide precipitates. The weld width and depth increase with the increase of laser power. The microhardness of the welded joint is distributed in W type, and there was obvious softening in the heat-affected zone, which was 36% lower than that of the base material. Joint tensile strength increases with the laser power, when the base material achieves complete penetration of the joint, continuing to increase the laser power on the mechanical properties of the joint has little effect, the tensile specimen fracture location occurred in the heat-affected zone near the performance of plastic fracture.

This study examines the effects of laser power and welding current on the microstructure and mechanical properties of 40Mn medium carbon steel welded joints. A 6mm-thick 40Mn steel was employed as the test material. The laser power and welding current parameters were optimized to analyze the resulting microstructure using optical microscopy (OM) and scanning electron microscopy (SEM). The findings indicate that as laser power increases, the weld penetration phenomenon diminishes, porosity tendency reduces, and the tensile strength of the welded joint improves. Similarly, an increase in welding current enhances the droplet transition mode, decreases porosity tendency, and augments the tensile strength of the welded joint. The microstructure of the 40Mn steel joint obtained through laser-arc hybrid welding is characterized by the presence of acicular ferrite, trostenite, upper bainite, and lower bainite. These observations provide insights into the optimization of welding parameters for improving the quality of laser-arc hybrid welded joints in medium carbon steels.

This study investigates the effects of process parameters, including welding current, laser power, and welding speed, on the weld formation in laser-MAG hybrid backing welding of 12 mm thick Q345B test plates. A series of experiments were conducted to determine the optimal parameters for achieving full penetration backing welds. The results show that full penetration can be achieved with laser power set between 4.0 to 4.5 kW, welding current ranging from 200 A to 280 A, and a welding speed of 1.0 m/min. Under these conditions, the weld formation was satisfactory, with no detectable defects such as pores or cracks. It was observed that a higher welding current resulted in a fuller weld formation. The welding process demonstrated adaptability to mismatch conditions, accommodating gaps up to 1 mm and staggering of 1.5 mm. The tensile strength of the welded joints exceeded that of the base metal, and the bending performance was satisfactory, fulfilling the criteria for engineering applications.

Femtosecond laser precision correction of tooth surfaces has become a new method for complex gear machining. In this paper, the meshing method is used to divide the tooth surface of the face gear into 5 × 9 grid points, and the femtosecond laser incidence angle of each node is measured through the three-dimensional model of tooth surface theory.By analyzing the distribution of laser energy density acting on the tooth surface, the quantitative relationship between the femtosecond laser incidence angle and defocus amount on the tooth surface is determined, the femtosecond laser ablation model of the tooth surface is established, and the variation patterns of the depth of the ablation pit and laser incidence angle are simulated and analyzed.Through experiments, it is found that the maximum relative error between the simulated tooth surface and the experimental tooth surface after ablation is 6.53%, which is within a reasonable range. This indicates that the femtosecond laser ablation model is feasible and provides a technical basis for the femtosecond precision correction of face gear material 18Cr2Ni4WA.

Laser technology is widely employed for chamfering phone screens and engraving telescope prisms. This study explores the use of high repetition frequency picosecond lasers to induce fractures in modified glass through a slit shaping method. The study demonstrates that an oblique cutting orientation facilitates precise crack propagation. The results indicate that the glass piece is 1.26 m rough after separating and is sliced at a significant angle of 35° with the horizontal plane. In this study, the effects of laser pulse energy, pulse number, and scanning speed on the surface roughness and edge collapse of quartz glass are investigated. This novel cutting technique can resolve the issue of splitting and beveling prism glass, and it has a promising future in the area of laser processing translucent materials.

This paper presents a laser cleaning vision inspection system, leveraging computer vision technology, designed to intelligently detect and manage rust stains on the metal surfaces of outdoor disconnect switches in substations. The system employs the HSV color conversion method to establish color thresholds based on rust color characteristics, facilitating the initial extraction of rust-affected areas. Subsequent processing involves Gaussian filtering and morphological image operations—expansion and erosion—to accurately identify rust regions and ascertain their location and dimensions. This information is then fed into a pulse laser controller to execute the laser cleaning of rusted areas on the workpiece surface. The system was implemented in a substation in Ulanqab, demonstrating effective rust removal from the metal surfaces of the contact finger workpieces of the disconnect switches. The system achieved a rust area recognition accuracy of 93.10% under the HSV color space, thereby accomplishing the design of a machine vision-based laser cleaning system for rust on disconnect switches.

Leveraging the potential of convolutional neural network (CNN) in image processing, a novel method based on GASF-CNN was introduced for detection kerosene content in gasoline. Competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA）were adopted to select the key variables, and Gram-angle and field (GASF) was used to encode the selected variables, which were then input into CNN. Experimental results revealed that using variables selected by CARS-SPA enhanced the model's performance. The root mean square error (ERMS) of GASF-CNN on the training set and the test set was 0.620 and 0.739, respectively. The coefficient of determination (R2) on the training set and the test set was 0.988 and 0.983, respectively. However, the ERMS on the training set and the test set of 1D-CNN, support vector regression (SVM) and partial least squares regression (PLSR) are 0.702, 0.898, 1.500, 1.290, 1.490 and 1.320, respectively; the R2 on the training set and test set are 0.985, 0.975, 0.932, 0.952, 0.932 and 0.949, respectively. The amalgamation of GASF-CNN and CARS-SPA allows for more precise quantitative detection of kerosene adulteration in gasoline, thereby offering a promising methodology for spectral detection of gasoline adulteration.

Spatial light modulators (SLMs), extensively utilized in the optical field, offer substantial modulation flexibility and the capacity to generate complex wavefronts, necessitating precise wavefront sensing techniques. This study introduces a wavefront sensing technology for SLMs based on Hilbert phase-contrast imaging. This technology utilizes the Hilbert transform to introduce complex signal analysis into wavefront sensing, effectively preserving the high spatial frequency information and realizing the quantitative phase-contrast imaging with high accuracy. The experiments show that the spatial light modulator wavefront sensing technology based on Hilbert phase-contrast imaging can effectively detect the complex wavefront generated by the spatial light modulator. The average wavefront sensing error is less than 1% for 40 different phase regions, achieving a well phase contrast. The proposed technology is expected to be applied in research fields that depend on spatial light modulators, such as scattering imaging, holographic imaging, beam turning, laser shaping, and optical tweezers.

This study investigates the enhancement of shear strength in aramid fiber-reinforced polymer (AFRP) composite joints through surface pretreatment using a 532 nm picosecond laser. The paper first discusses the optimal process window for laser removal of resin from the ARFP surface and analyzes the reasons why laser treatment can enhance the shear strength of the joints from the perspective of mechanical and chemical joining. Subsequently, the etching of notches on the AFRP surface was continued and the effects of notch spacing, number of etchings and scanning angle on the shear strength of the joint were investigated. The results showed that the joint shear strength was improved by 30% compared to the original surface after laser removal of resin from the AFRP surface, and by 75% compared to the original surface after etching the notches. These results offer valuable insights for the repair of AFRP structures and their application in specialized equipment.

Noise and outlier points are inherent in point cloud data collected using vehicle-mounted lidar systems. This study introduces a statistical parameter adaptive filtering (SPAF) technique for denoising lidar point cloud data to mitigate the impact of automotive noise. First, based on the area of interest of various driving conditions, the application condition of filter for point cloud coarse noise reduction, and then use point of statistical features and interval statistics as clustering, calculate the neighborhood of each point cloud point number as well as multiple statistical filter cutoff threshold, and lastly suggested to enhance statistical filtering method to extract the noise reduction. The method completely takes into account the features of the point cloud in various driving settings after theoretical study and experimental verification, requiring no user input parameters. This study compares the radius filtering and bilateral filtering algorithms in the experiment with the suggested method. The results demonstrate that the proposed algorithm achieves an accuracy of up to 94%, outperforming conventional filtering methods.

Under the background of strong noise such as arc light, smoke and a lot of spark splash, conventional laser stripe extraction method based on image processing have the shortcomings of poor flexibility and robustness. In this paper, a method of laser stripe extraction based on full convolution neural network (DB-U-Net) is proposed. The experiments show that by introducing dense residual block (DB) and attention mechanism into the backbone network of the model, the global information extraction ability of the model is improved, and the comprehensive performance AUCPR of the model is increased from 0.891 to 0.924. On this basis, the multi-level deep supervision training mode combined with multi-layer feature cascading output is used to integrate the low-level and high-level feature information, which reduces the information loss caused by multiple up and down-sampling and deep network convolution operation. The comprehensive performance AUCPR of the model is increased from 0.924 to 0.932. By using the proposed model network, the position error of laser stripe centerline extraction after de-noising the strong noise image is up to 0.50 pixel, which proves that the method has high detection accuracy and robustness against the strong noise interference in the welding process.

In the process of remote monitoring, the emission wavelength of the laser listener has a nonlinear shift problem, which results in a large displacement deviation between the received beat signal wavelength and the actual beat signal wavelength. A wavelength offset correction method of laser interceptor based on nonlinear function polynomial fitting is proposed. Through the nonlinear estimation of the laser listener wavelength, the offset of the beat frequency signal is identified, and the nonlinear calibration function model is established by using improved polynomial fitting. The center wavelength of each transmitted wave is obtained by power weighting, the actual beat frequency is obtained according to the relationship between the center wave and the voltage change, and the instantaneous beat frequency is calculated by Hilbert transform. The difference between the two is the beat frequency offset. Correct the offset to complete the wavelength nonlinear calibration of the laser listener. The experimental results show that the proposed calibration method effectively reduces the mean square error value of the received wavelength of the laser listener, and the calibrated wavelength is basically consistent with the actual wavelength of the target object, which accurately realizes the wavelength offset correction of the laser listener and improves the accuracy of the laser listener.

This study examines the interactions between modes in a plasmonic structure consisting of a rectangular cavity edge-coupled to a semi-ring cavity. Analysis of magnetic field distributions reveals that the low-order resonant mode in the semi-ring cavity is strongly coupled with the resonant mode in the rectangular cavity. Additionally, higher-order resonant modes exhibit significant interaction with the waveguide mode and weak coupling to the resonant modes in the rectangular resonator. Understanding these mode coupling mechanisms could offer theoretical insights for the advancement of highly integrated optical switching, sensing, and slow light devices.