Aluminum/steel dissimilar alloys were joined by ultrasonic assisted laser welding-brazing technology, and the forming quality, microstructure and mechanical properties of the joints welded at different ultrasonic vibration frequencies were compared and analyzed. When the ultrasonic vibration is 0 Hz, the spreading wetting ability of liquid aluminum on the surface of stainless steel was so poor that the weld forming quality was decreased, and the biting edge and welding tumor defect were detected. Besides, some porosity defects existed in the fusion welding zone, and the IMC with the shapes of zigzag and fine acicular was found in the brazing interface. A tensile strength of 164 MPa and elongation of 5.5% were obtained, respectively. When the ultrasonic vibration frequency was 60 Hz, the weld forming quality becomes better, the IMC was flat, the thickness of IMC was only 1~3 m, and the tensile strength and elongation after breaking were 192 MPa and 8.4%, respectively.

In view of the disadvantages of small allowable gap and large thermal damage in non-optical contact glass welding, a 50 W infrared picosecond laser, equipped with a 100 mm long focal scanning galvanometer, was employed to investigate the influence of laser scanning number on the welding morphologies in this study. The silica glass welding joint with low heat damage zone was obtained via non-optical contact by quasi-simultaneous welding method, and the allowable gap of welded joint was increased to 5 m. The experimental results demonstrate that the cavities produced by the glass gap exist in the form of bubbles in the molten pool. With the increase of scanning times, the cavities are far away from the original glass interface and the upper and lower glasses are fused to form a strong bonding force. The maximum welding strength was improved to 21.42 MPa under the process parameters with single pulse energy of 24 J，scanning speed of 1 000 mm/s, defocusing amount of -100 m and scanning number of 5 times; meanwhile, it still can reach to 15.79 MPa under such large welding gap of 5 m.

22MnB5 hot-formed steel with aluminum-silicon coating was welded by fiber laser. The effects of different wire feeding speeds on the weld morphology, droplet transition form, microstructure, element content and distribution, and tensile properties were studied. The results showed that the weld formed well and had no obvious defects. The droplet transition is a liquid bridge transition. The weld microstructure after heat treatment at different wire feeding speeds is composed of martensite and -ferrite. With the increase of wire feeding speed, the content of Al and -ferrite in the weld decreases continuously. With the increase of wire feeding speed, the tensile strength of the joint first increases and then decreases.

A visual detection method based on feature extraction is proposed for three types of powder bed defects in laser selective melting equipment. An adaptive brightness correction algorithm is designed to eliminate the influence of lighting on images caused by uneven brightness from the light source. The defect contours are obtained through image preprocessing, and based on the grayscale information of the images, the defect types are differentiated for feature extraction. Stripe defects are identified using the Hough transform. For the other two types of defects, direction gradient histograms (HOG) features, texture features, and shape features are extracted. The feature vectors are then dimensionally reduced and inputted into the AdaBoost ensemble learning algorithm for training to obtain the classification model. Experimental results show that this algorithm can effectively distinguish the three types of powder bed defects with an identification accuracy of 97.29%. The average detection time is less than 500 ms, enabling fast and accurate recognition.

This study investigates the defects in TA15 (Ti-6.5Al-2Zr-Mo-V) components produced by selective laser melting (SLM). TA15 samples were fabricated using varying laser powers and scanning speeds to examine the influence of volume energy density on pore and crack formation. The microscopic morphology of pores at different energy densities was analyzed, and the relationship between energy density and mechanical properties was determined through fracture morphology analysis. Results indicate that specimen density and mechanical properties initially increase with volume energy density before decreasing. The highest relative density (≥99.9%) for SLM-fabricated TA15 specimens was achieved at 100125 J/cm3, corresponding to a tensile strength of 1 189 MPa±22 MPa, yield strength of 871.22 MPa±18 MPa, and elongation of 7.42%±1.2%. At volume energy densities ≤ 100 J/cm3, interconnected small pores form regional irregular pore chains, negatively affecting mechanical properties. Above 125 J/cm3, pores are independently scattered and spherical, suggesting that pore reduction enhances the mechanical properties of SLM-produced TA15 parts, broadening their application potential.

The parameters of laser cladding have a significant impact on the quality of the coating, and appropriate process parameters can help improve coating performance. In this study, response surface methodology was used to establish the statistical relationship between input factors (laser power, powder feed rate, scanning speed) and responses (melting width, melting depth, melting height, dilution rate, microhardness). The experimental results were validated through variance analysis. The results showed that the scanning speed had the most significant influence on the cladding width and height, while the laser power had the most significant influence on the cladding depth. The scanning speed had the most significant impact on hardness, and the powder feed rate had the most significant impact on the dilution rate. Furthermore, the research on the predictive model revealed that the prediction errors for melting width, melting height, melting depth, dilution rate, and hardness were 6.46%, 0.72%, 1.79%, 2.47%, and 0.65%， respectively.

This paper presents a prediction model for the process parameters of laser dressing bronze diamond grinding wheels, utilizing a BP neural network enhanced by the CIGWO algorithm. Based on the topological relationship of the BP neural network model, the number of nodes in the input layer, implicit layer and output layer of the model was determined firstly, and the mapping relationship between the process parameters and the removal amount per unit time of the grinding wheel was constructed, then the Cricle chaotic mapping adaptive weight grey wolf algorithm was used to optimise the established prediction model for the process parameters, and finally the real values of the experimental measurements were compared according to the back propagation training results. The results showed that the CIGWO-BP prediction algorithm achieves a 3.32% higher accuracy than the traditional BP network model, with an average relative error of less than 5.4% between the predicted and actual values. In summary, the optimized model offers a robust approach for predicting the removal efficiency of laser-dressed grinding wheels.

Laser coloring enhances material surface performance and imparts aesthetic appeal or achieves extinction effects. In this study, the single-factor variable method was employed to conduct laser coloring tests on the surface of TC4 alloy, aiming to determine the coloring behavior of titanium alloys and the energy density thresholds for various colors. Four stable coloring effects were selected for SEM and elemental composition analysis to elucidate the mechanism of laser coloring. The results indicate that the cumulative absorbed energy density of titanium alloy materials dictates the color appearance in laser surface coloring, with the color threshold energy density ranging from 1.25 J/mm2 to 20 J/mm2. Further investigation reveals that the surface morphology resulting from the interaction between the laser and titanium alloy materials, along with the extent of oxidation and carbonization, are the underlying factors determining the coloring effect.

This paper presents a prediction method for the laser cleaning thickness of composite coatings using a support vector regression (SVR) model optimized by a particle swarm optimization (PSO) algorithm. Laser cleaning experiments were conducted on aluminum alloy substrates coated with a 20m green epoxy primer and a 40m white polyurethane topcoat. An SVR model was developed to establish the correlation between process parameters and the thickness of paint removal for composite coatings. The PSO algorithm was employed to optimize the SVR model′s penalty coefficient C and kernel function parameter g. The experimental results show that compared with SVR model and BP neural network model, the model is more accurate in predicting the laser cleaning thickness of composite coating. The coefficient of determination of the model is 0.96171, the root mean square error is 1.738, and the average absolute error is 1.516 2. In this study, a prediction model of paint removal thickness with high precision can be obtained, which can effectively predict the thickness of laser paint removal, and lays a foundation for further research on intelligent control of composite paint layer.

To address the issues of debris, noise, and tool loss in traditional mechanical breakage, ultrashort-pulsed laser interior marking technology is employed in this study to achieve green and efficient breakage of electric meters. The laser ablation thresholds of the polymethyl methacrylate (PMMA) and the glass material of the liquid crystal display module are first studied. Afterward, we discuss the mechanisms and characteristics of two typical marking methods. When the laser beam is focused on the surface of the PMMA layer, clear black marks form due to laser-induced carbonization. However, low molecular weight organics generate during the processing, which can result in the generation of gas pollution. By utilizing the spot size variation during propagation and the different ablation threshold of PMMA and glass material, laser marking can be realized on the glass surface by passing through the PMMA layer. Clear marking forms due to the resolidification of the glass material while avoiding the generation of debris and gas pollution. Utilizing this interior marking technology for meter breakage can not only avoid the generation of debris and gas pollution but also enable traceability requirements for the entire life cycle of electric meter, which can be used to replace traditional mechanical breakage methods.

Laser cutting has emerged as a modern machining technology, garnering extensive application due to its benefits of high quality, precision, and efficiency. Cutting path planning is a pivotal step in the machining process, influencing both the quality of the cut and the time expenditure. This review synthesizes the advancements in path planning algorithms for laser cutting. Initially, the paper outlines the features of laser cutting technology and examines the associated constraints. Subsequently, a comprehensive review of the modeling and solution approaches for cutting path planning is provided. The paper concludes by comparing the strengths and limitations of various models and identifying avenues for future research in the field.

In response to the problem of laser cutting processing being unable to blow in special environments, an experimental study was conducted on the impact of 10000 watt level laser loop path, cutting movement speed, focal length, and cutting seam width on the cutting quality of 20 mm thick 20CrNiMo steel under no blowing conditions based on the self-developed 10 000 watt level laser cutting experimental platform. In addition, the optimization relationship between laser power and cutting movement speed were systematically analyzed to obtain better cutting results. The research results indicate that for a 10 000 W power laser, the optimal process parameters are the top-down loop path, cutting movement speed range is 2528mm/s, focal length 0 mm, and cutting seam width 25 mm. For laser powers of 6 000 W, 7 000 W, 8 000 W, 9 000 W, 10 000 W, and 12 000 W, the corresponding optimal cutting movement speeds were 5 mm/s, 5 mm/s, 10 mm/s, 15 mm/s, 25 mm/s, and 25 mm/s, respectively. The research results contribute to providing reference for the research and application of ultra-high power laser in the field of medium and thick steel under non blowing conditions

Laser cutting, characterized by its non-contact, high efficiency, and high precision, offers significant advantages over traditional processing techniques and plays a crucial role in industrial production. To further understand the research and industrial application of laser cutting technology, this study investigates and analyzes global patent data in the field of laser cutting. The innovative development status of the global laser cutting industry is examined from multiple perspectives including trend in patent application, patent competition, technology field distribution, and patent value. The international standing of China's laser cutting industry is clarified, and deficiencies in China's technological innovation and patent protection are identified. This study also provides recommendations for the development of domestic industry technology and patent layout.

The thickness of adhesive significantly impacts the bending resistance and functionality of SIM cards. Traditional measurement methods, such as using a micrometer, are inefficient and can damage the adhesive surface. To enhance the accuracy and efficiency of adhesive thickness measurement, this paper introduces a high-precision method based on laser point cloud technology. Firstly, a line laser profile sensor is used to obtain the depth map of the glue, and missing pixels in the image are repaired. Then, NCC template matching is employed to locate and segment each glue spot, and point cloud reconstruction is performed. Next, statistical filtering is applied to eliminate outliers. Finally, an improved RANSAC algorithm is utilized to fit the best plane equation parameters, and the height information of the top and bottom surfaces of the glue is calculated as the glue thickness. The glue thickness obtained by this method is compared with the measurements from the micrometer, and repeat tests are conducted. The results show that for 10 groups of different glues, the maximum average error is less than ±6 m, and the repeatability is less than 0.3%. The test results validate that this method has good accuracy and stability, meeting the requirements of industrial measurement.

The iterative closest point (ICP) algorithm is a classical and widely used point cloud registration algorithm. However, this algorithm has high requirements for the initial position and is computationally slow. On the other hand, improved methods based on feature extraction suffer from low registration accuracy due to insufficient or unrepresentative feature point quantities. To address this, an improved ICP registration algorithm based on dual-constraint feature extraction is proposed. Firstly, feature points are extracted using the angle between normal vectors and the intrinsic shape signature (ISS), utilizing the complementary nature of these two constraints to obtain more representative feature points. These feature points are then described using the three-dimensional shape context (3DSC) algorithm to obtain an initial point set. Next, the sample consensus initial alignment (SAC-IA) algorithm is integrated with the ICP algorithm to provide an optimized initial pose for ICP. Finally, through separate studies using multiple sets of simulated data and real-world measurements from LiDAR, the experimental results demonstrate that compared to the traditional ICP algorithm, the registration accuracy of different objects has been improved by more than 85% and the computation time has been reduced by more than 40%. The proposed algorithm still maintains excellent registration accuracy and efficiency for large datasets with significant differences in initial positions.

Addressing the issue of low accuracy in single tree segmentation of forest point clouds in complex spatial environments, this study focuses on two sample areas, namely coniferous and broad-leaved forests in Haikou Forest Farm, Xishan District, Kunming City. Utilizing airborne LiDAR point cloud data, a seed-based optimization algorithm is proposed for single tree segmentation. The method combines farthest point sampling and K-nearest neighbor search to identify tree top points, optimizes seed point selection through K-means clustering, and utilizes the Expectation-Maximization algorithm to determine the optimal parameters of a Gaussian Mixture Model, thereby improving its fitting accuracy. Finally, the single tree point cloud segmentation results are obtained based on the optimized Gaussian Mixture Model. Experimental results demonstrate the effectiveness of the proposed method, achieving overall accuracies Oaccu(%) of 89.98% and 90.43% for the two study sites, respectively. The method exhibits good performance in accurately segmenting individual trees, distinguishing between connected tree crowns and pseudo-tree top points, and achieving precise segmentation of forest tree point cloud data.

To facilitate dynamic deformation measurement of multi-camera video extensometer images, this study proposes a tracking matching method for large-field-of-view multi-camera video extensometer images. The method, grounded in digital image correlation and object point reprojection theory, is applicable to material strain measurement under large deformations. Firstly, the global calibration of multiple cameras was performed, and the matrix transformation relationship between the camera coordinate system and the world coordinate system was solved for each camera. Secondly, the image points of the sequence images acquired by the video extensometer were traversed and tracked between the camera images of the video extensometer by applying the object point reprojection theory. Finally, the equation of multi-camera video extensometer strain measurement was derived, and a multi-camera video extensometer strain measurement platform was built. The measurement results were compared with the conventional strain gauge strain measurement method through tensile experiments on two specimens of Q235 mild steel and aluminum alloy. The results show that the method accurately completes dynamic tracking measurement of image points in multi-camera video extensometer images, with a camera calibration reprojection error of less than 0.05 pixel and average strain errors of 0.170 9% and 0.049 9% for the two specimens, respectively. The results show that the dynamic tracking measurement method of multi-camera video elicitor is basically suitable for strain measurement scenarios with large strain.

To address the issue of online monitoring of the effectiveness of laser delayering paint removal in localized or specific areas of aircraft skin, a machine vision-based method for online monitoring of the cleanliness of laser delayering paint removal is proposed. Using an established visual online monitoring platform, images of the laser paint removal process are acquired. First, algorithms such as Canny edge detection, dilation, and perspective transformation are applied to extract localized or specific areas of laser delayering paint removal. Next, the extracted images are converted from the RGB color space to the HSV color space. The OTSU algorithm is then used to segment the V channel image into different color paint layers. Subsequently, the ratio of the areas occupied by these layers is calculated using a binarization algorithm. Finally, the cleanliness of laser delayering paint removal is monitored, and the accuracy of the algorithm is verified. The results show that the machine vision-monitored cleanliness is 39.80%. Experiments demonstrate a 0.42% error between the theoretical and monitored area ratios, indicating high consistency. This method provides methodological guidance and theoretical support for online monitoring of laser paint removal on aircraft skin.

To address the current issues of low-power line-structured light and the difficulty in achieving long-distance detection, this study proposes a biconic Zernike lens composed of aspherical and free-form surfaces. The lens is designed to shape a 5 W laser source into line-structured light. Initial parameters for the fast-axis collimation surface are determined using the extremum optical path law and the aspherical standard equation. By analyzing the slow-axis divergence angle and the mapping relationship to the target plane, combined with Snell's law, the free-form curve equation is derived to establish the initial structure of the slow-axis beam-expanding surface, thereby obtaining the preliminary parameters of the shaping lens. Optical simulation results demonstrate the following performance: The fast-axis divergence angle is compressed from 49° to 0.942 mrad. The slow-axis flat-top field angle reaches 43.6°, with an optical energy utilization rate of 86.4% and uniformity of 96.9%. At a projection distance of 2 000 mm, the system generates uniform line-structured light measuring 1.9 mm × 1 600 mm, delivering an output power of 4.3 W. It increases the projection distance by 3.3~29.4 times compared with the existing line-structured light (light energy of 5~400 mW), and provides an effective method for realizing long-distance detection.

The Yimeng small cotton padded coat, recognized as a traditional attire of the Yimeng area and designated as an Intangible Cultural Heritage by Shandong Province, currently faces an urgent challenge of craft preservation due to the advancing age of its principal artisans and the absence of a structured inheritance system. This study employs digital technology to address this issue, utilizing multi-view close-range images for the reconstruction of the Yimeng coat. The research applies the Alpha-shape algorithm and Poisson disk resampling algorithm to refine point cloud data, yielding a highly accurate and realistic 3D model. Validation confirms that the generated 3D models satisfy the necessary accuracy standards. The proposed 3D reconstruction method offers a cost-effective, accurate, efficient, and visual approach that successfully retains the traditional craft elements such as textures and patterns of the Yimeng coat. Consequently, this method enhances production quality and authenticity in retail, while also providing data that supports digital customization and virtual fitting.

Water LiDAR detection technology offers unique advantages such as being unrestricted by sunlight, enabling continuous day and night observations, and allowing flexible adjustment of vertical and lateral scanning resolution. It can obtain vertical distribution information of underwater optical characteristics, including scattering and attenuation, as well as water quality parameters such as particulate matter and chlorophyl. With the ongoing developments in high repetition rate, high power, and narrow pulse width laser technology, water LiDAR has found extensive applications in hydrological measurement, river and lake surveys. This article introduces the basic structure of water LiDAR, summarizes the research status and existing problems of LiDAR technology in the field of water quality and aquatic optical detection at home and abroad in recent years, and provides prospects. The research believes that the three-dimensional radiation transmission simulation of laser beams and echo signals, calibration verification, and multi-source data fusion will be the focus of future research on water LiDAR. By summarizing and sorting out the application research results of existing water LiDAR and clarifying the problems, it lays an important foundation for the application of water LiDAR in the water conservancy industry and provides a reference basis for a deeper understanding and grasp of the research direction of water LiDAR applications.