In this paper, the selective laser melting (SLM) technology was used to prepare Inconel 625 alloy specimens. Gleeble 3800 thermal simulation experimental machine was used to produce Inconel 625 alloy specimens at 700, 800, 900 and 1 000 ℃. The rheological behavior of the alloy at high temperature was studied by isothermal compression experiments at strain rates of 1, 0.1, 0.01, 0.001 s-1, and the real stress-strain curve was obtained. The constitutive model of SLM Inconel 625 alloy was established by Johnson-Cook model and Modified Johnson-Cook model respectively, and the prediction accuracy of the two models was compared. The results show that the error of the original Johnson-Cook model is AARE 83.53% and the correlation coefficient is 0.77. The error AARE of the Modified Johnson-Cook model is 14.87%, and the correlation coefficient is 0.98. The accuracy of the Modified Johnson-Cook model is significantly higher than that of the original model, and it has higher prediction accuracy. The high temperature rheological behavior of Inconel 625 alloy can be well reacted by additive manufacturing. Finally, microstructural analysis of compressed specimens reveals hardening and recrystallization softening, with temperature and strain rate significantly influencing material microstructure.

To improve the mechanical properties of aluminum alloys, Al-1Cu-1Mg-3Mn-0.8Sc-0.8Zr alloy was designed for selective laser melting (SLM) forming technology with a high cooling rate. In order to establish the best fabrication settings, the effect of SLM forming parameters on the designed alloy was studied and the mechanical properties of as-built alloy were evaluated. The orthogonal experiments revealed that the laser power and scanning speed are the key parameters affecting the densities and hardness of the alloy. According to the study of the effects of different processing parameters on defects, irregular metallurgical holes and cracks in the alloy increased with the decrease of the laser power and the increase of the scanning speed, while high laser power or low scanning speed would lead to large round holes in the alloy. The optimal fabrication parameters for Al-(Cu, Mg, Mn)-(Sc, Zr) alloy was determined as follows: laser power is 330 W, scanning speed is 900 mm/s, and hatch distance 0.12 mm. The alloy has a tensile strength of 349 MPa, a yield strength of 267 MPa, and an elongation of 7%.

Laser melting technology was employed to enhance the surface of 304 stainless steel, aiming to improve its wear resistance. The morphology, microstructure, microhardness, wear morphologies, and wear mechanism of the resulting melting layer were comprehensively analyzed using optical microscopy, microhardness testing, friction and wear testing, scanning electron microscopy, and energy dispersive spectroscopy. The results showed that the melting layer had no defects such as pores and cracks after laser melting, and the microstructure was uniform and dense. Due to the rapid heating and cooling process of laser melting, the microstructure of the melting layer was significantly refined, and the melting layer was mainly composed of dendrites and equiaxed crystals. Fine grain strengthening was the main factor for the hardness improvement of melting layer, and the microhardness of melting layer was about 281 HV, which was about 50% higher than that of 304 stainless steel substrate. With the increase of hardness, the friction coefficient and wear rate of the melted layer decreased to 0.544 and 2.46×10-5 (N·m), respectively. The wear mechanism of melting layer was abrasive wear. Laser melting effectively improved the microhardness and wear resistance of 304 stainless steel.

The purpose of this paper is to investigate the influence of different process parameters on the surface geometric quality characteristics of laser cladding layers and to develop a high accuracy prediction model for the geometric characteristics of single pass laser cladding layers. The surface geometric quality characteristics of the laser cladding layer mainly include the dilution rate and the aspect ratio. The paper designed and completed a full factorial laser cladding experiment of 15-5PH alloy powder coating on the surface of 20Cr13 stainless steel. Based on this experiment, data acquisition of geometric quality characteristics was completed by applying sample processing, tissue observation, and numerical processing. For the analysis of experimental results, this paper conducted single-factor process influence analysis and ternary quadratic regression analysis. Based on the regression model and prediction results, a prediction model and analysis method of geometric quality characteristics based on GWO-DELM intelligent algorithm were constructed. The results of the regression analysis showed that the prediction accuracy of both the dilution rate and the aspect ratio were extremely low and the prediction ability was poor. In order to construct a prediction model of geometric quality characteristics with high accuracy, this paper constructed an analytical model based on the GWO-DELM intelligent algorithm, and trains and tests the factorial experimental data. The evaluation index results were much better than the polynomial and DELM regression models, and the prediction accuracy was better.Conclusion:Based on the experiments of all-factor laser cladding process, the application of GWO-DELM intelligent algorithm can establish the prediction model of surface geometric quality characteristics of single-pass cladding layer with good accuracy. This model and analysis method can provide theoretical support for surface geometric quality control of composite laser cladding layers.

The high entropy alloy coating prepared by laser cladding technology has excellent comprehensive mechanical properties and has become a research hotspot in the field of metal materials in recent years. It is found that adding hard ceramic particles can significantly improve the hardness, wear and corrosion resistance of high entropy alloy coatings prepared by laser cladding technology. In this paper, the strengthening mechanism of hard ceramic particle reinforced laser cladding high entropy alloy coating is summarized. Then, the ceramic particle reinforcement phases commonly used in recent years are reviewed from the aspects of direct addition and in-situ synthesis. The influence of ceramic particle reinforced phases on the microstructure and properties of high entropy alloy coatings are summarized. Finally, the development prospect of laser cladding ceramic particle reinforced high entropy alloy composite coating is prospected.

A TY-1 coating based on iron was developed on the surface of 27SiMn steel through a high-speed laser scanning process. The study examines the interplay between melt pool temperature, cladding layer thickness, and microstructure under varying conditions of laser power, powder feed rate, and scanning speed. Utilizing ANSYS finite element analysis software, the temperature field during the cladding process was simulated. Subsequently, high-speed laser cladding tests were executed under identical parameters, and the micromorphological evolution of the coating was scrutinized using optical microscopy. The results show that the melt temperature is 1 403.1 ℃-2 588.5 ℃, and the coating thickness is 50-244 m; the dense isoaxial crystals and a few cylindrical crystals are gradually formed with the melt temperature under different parameters, and cylindrical or secondary dendrite are easily generated when the melt temperature reaches 2 588.5 ℃. According to the comparative analysis, the power, powder delivery speed and lap rate are 900 W, 13 g/min, and 60% respectively, the powder can be fully melted, the coating surface is smooth, and the microstructure is fine.

In this paper, the hydrogen embrittlement behavior of laser-arc hybrid welded joint of bainite high strength steel was studied by four-point bending loading under in-situ electrochemical hydrogen charging combined with microstructure analysis and fracture characteristics analysis. The result show that the high temperature gradient during laser-arc hybrid welding leads to martensite transformation in the heat-affected zone, and high welding residual stress is formed in the fine grain zone. The martensitic phase's susceptibility to hydrogen embrittlement, coupled with the high diffusivity of hydrogen along grain boundaries and the localized residual stress gradients, facilitates the nucleation and growth of hydrogen-induced cracks. This process is predominantly observed along the martensitic laths and grain boundaries, culminating in a brittle fracture mode.

Femtosecond laser finishing can be used as the final step in the precision machining of face gears. In this paper, the mechanism of the physical process action of the femtosecond laser ablation of the face gear material 18Cr2Ni4WA is studied. Based on the basic double temperature equation, the effect of electron density of states change is investigated, the dynamic energy heat transfer model of the femtosecond laser ablation of the face gear material is established, and the changes of electron temperature and lattice temperature of the femtosecond laser ablation of the face gear material are simulated and analysed. The experiments of femtosecond laser ablated gear material are carried out, and it is found that with the increase of energy density, the radius and depth of the crater are increased, the melt of the crater tends to increase, and there is a better quality of ablation when the energy density is 1.58 J/cm2. With the increase of pulse width, the morphological changes are not significant, but the melt production tends to decrease, and the crater tends to be flat. By studying the ablation law of the femtosecond laser ablation face gear material 18Cr2Ni4WA, it provides a reference for improving the precision machining quality of the face gear.

Icing has caused great harm to human daily life and industrial applications, and superhydrophobic surfaces have attracted special attention because they can be effectively used for anti-icing without consuming external energy. In this paper, we present a method to fabricate three-dimensional porous superhydrophobic surfaces using ethanol-assisted femtosecond laser, and investigate its performance under different laser processing parameters. The prepared three-dimensional porous structure can effectively absorb the sunlight irradiated on the surface of the ultra-thin nickel foil, and its solar irradiation saturation temperature is 1.36 times that of the bare nickel surface, which can effectively improve the photothermal deicing efficiency. In addition, this structure will form a stable gas film on the surface of the material, making it have excellent superhydrophobic properties. This simple and rapid preparation method of three-dimensional porous superhydrophobic surface will show broad application prospects in the fields of anti-icing, optoelectronics, physics and chemistry.

The present study establishes a thermal coupling model for TC4 titanium alloy plates containing prefabricated cracks using COMSOL Multiphysics. The objective is to analyze the relationship between ultrasonic wave propagation characteristics at various detection points and crack information to enhance crack characterization in inspection studies. The influence of detection point location, crack depth, and shape on the crack echo is investigated. The results indicate that with a fixed laser source position, a closer detection point to the crack leads to a shorter peak time and faster amplitude decay in the crack echo. Furthermore, the crack echo peak value increases as crack depth grows, displaying a rapid and linear growth pattern for depths exceeding 1.0 mm. The comparison of crack shape waveforms suggests that its impact on detection accuracy is negligible. By calculating the path and arrival time of the converted transverse wave and crack echo, the crack depth is characterized and located with an accuracy of less than 4%.

This study investigates the intelligent laser processing of polymethylmethacrylate (PMMA) microchannels, examining the influence of laser power density, scanning speed, and scanning passes on channel dimensions utilizing a CO2 laser. A back propagation (BP) neural network model was developed to predict the laser processing parameters. The model was trained with empirical data and further optimized using a particle swarm optimization algorithm. The results showed that the neural network optimization model can control the PMMA microchannel width machining error within 5% and the depth machining error within 12%. The model has good prediction accuracy and will provide a basis for the intelligent selection of laser processing parameters of PMMA microchannel.

In the high-energy laser removal of tree barriers on transmission line channels, excessive laser energy may lead to extreme fire and subsequent fire. To analyze the temperature characteristics and efficiency law when laser irradiates tree barriers, six typical tree types were used as research objects to establish the ablation model. Subsequently, the factors affecting the temperature and efficiency when laser burns tree barriers were studied. The simulation analysis was conducted for the influence of laser power, tree types, and wind speed on the tree burning situation. Finally, the laser burning tests were carried out. The results show that the clearing efficiency increases with laser power. With the increase in tree density, the ablation thickness tends to decrease. Within the density range of 246~963 kg/m3, the ablation thickness is 110~430 nm, and the difference in the time required to clear different kinds of trees will reach four times. With the increase of wind speed, the maximum temperature of the irradiation center when laser burns tree barriers gradually decreases, and the ablation penetration time decreases by 6.42%~12.53% when the wind speed increases from 0.3 m/s to 3 m/s. The wind speed has a slight improvement effect on the efficiency of laser tree barrier removal.

To study the effect of laser shock peening (LSP) on the surface properties and mechanical properties of 316L stainless steel produced by laser selective melting (SLM), the 316L samples were processed by laser shock peening for 2 impacts and 4 impacts, after which the surface roughness of the samples were analyzed by digital microscope. The surface hardness of 316L samples with and without laser shock peening was tested by Vickers hardness tester. The tensile strength and fatigue life of the sample were tested by tensile performance testing and fatigue life testing equipment. The wear resistance of the surface of the sample was tested and analyzed by friction equipment with the help of white light interferometer and digital microscopes. The results show that the surface hardness of 316L samples could be increased by 19% after LSP with 4 impacts. The fatigue life of the sample was significantly increased by laser shock peening, and the fatigue life was increased by 130% after laser shock peening with 2 impacts. The improvement of laser shock peening on the tensile strength and wear resistance of 316L stainless steel samples is not obvious.

In the domain of single-photon laser radar target detection, the current detection speed is relatively sluggish. In order to address this limitation, a Fast Constant False-Alarm Rate (F-CFAR) detection method is proposed in this paper. Firstly, the data format of the single photon lidar signal is redesigned according to the characteristics of the radar echo signal. Then reduced the noise intensity through data processing. Finally, the constant false alarm detection is carried out on the processed signal data. This method can improve the detection speed without excessively reducing the detection performance. Simulation and experiment show that, the proposed method achieves at least an order of magnitude improvement in target detection speed compared to traditional signal detection methods, while maintaining relatively stable detection performance.

ObjectiveFingerprint is an important content of crime scene evidence. Criminal technical personnel on-site detection and extraction of fingerprint is an important content of research. Because the scene fingerprints often appear on the complex background, nondestructive extraction need professional technology. Based on the short wave ultraviolet light distribution test technology research on the complex background of various kinds of potential fingerprint extraction method, combined with the self-defined evaluation criteria, the visual analysis is carried out to provide a reference for the extraction and evaluation of latent fingerprints.MethodsTwelve kinds of common objects at the crime scene were selected as trace carriers, and six types of fingerprints, namely sweat, oil, oil-sweat mixture, occult blood, dust layer reduction, and dust layer addition, were used as the inspection materials. Seventy-two samples were examined by 254 nm shortwave ultraviolet reflectography based on the light distribution test technology. The influencing factors of potential fingerprint enhancement effect were systematically analyzed, and the evaluation criteria were established.ResultsThe experimental results show that for different objects, the short-wave ultraviolet special light distribution test has the extraction effect on different trace carriers and different fingerprint, and the fingerprint contrast is different due to the light absorption difference of the objects.ConclusionThe experimental enrich the method system of fingerprint extraction by light distribution inspection technology and provide a reference for the extraction and evaluation of such fingerprints on site.

The stable operation of mechanical parts is the top priority to ensure the safety of production. Internal defects are an important factor causing the failure of mechanical parts. However, it is difficult to locate and quantify the internal defects of mechanical parts at present. Therefore, this paper realized the quantitative characterization of internal defect parameters, and proposed a method to characterize the Angle of internal defect. In this paper, based on the principle of laser ultrasound, the influence of internal defects on ultrasonic body wave time domain signals is analyzed through numerical simulation combined with experimental methods, and the location, depth, length and Angle of the quantitative characterization of the main internal defects are studied. Experimental results show that the pulse reflection method can accurate characterization of the internal defect parameters, swept through A experiment on the depth of internal defect characterization, the relative error within 5%, the length of the internal defect characterization by B scan experiment, the relative error is within 4%, through the way of combining A flicking A and B, realizes the quantitative characterization of Angle of internal defects. The results provide a reference for the quantitative characterization of internal defects of mechanical parts.

This paper presents a method for analyzing the UV light aging time of inkpads using infrared spectroscopy coupled with machine learning techniques. A spectral database reflecting various degrees of aging was established, and the spectral data were refined using multiplicative scatter correction (MSC), standard normal variate transform (SNV), and Savitzky-Golay convolution smoothing (SG) to enhance the signal-to-noise ratio. An AdaBoost regression model for predicting the UV light aging time of inkpads was developed, with its parameters optimized through a grid search approach. This optimized model was benchmarked against support vector regression, random forest regression, and gradient boosting regression models. The study found that the SNV preprocessed infrared spectrum yielded the most accurate modeling results, outperforming those preprocessed with MSC and SG. The AdaBoost algorithm performed optimally with a decision tree depth of 4 and when the number of trees was 50 or more. As the decision tree depth increased, the optimized model required fewer trees. The AdaBoost model achieved perfect scores in mean square error, relative absolute error, coefficient of determination, and explainable variance, all of which were significantly better than the comparative algorithms.

Anti-icing technology is of great significance to reduce the economic losses and casualties caused by freezing disasters. With its advantages of energy saving and environmental protection, photothermal de-icing technology has received extensive attention in the field of anti-icing. However, in practical applications such as aerospace and wind power generation, the development of photothermal deicing surfaces is still limited due to factors such as difficult preparation and low photothermal efficiency. Inspired by the shape of the compound eye of insects, an ultra-black light heat trap structure surface with excellent photothermal performance was prepared by femtosecond laser modification of aluminum alloy plate. The high depth (≥ 50 m) pit array structure allowed light to be refracted multiple times inside the structure, which effectively improved the light and heat absorption capacity of the structural surface. The experimental results show that under a standard intensity of sunlight, the surface of the structure can be heated from 25 ℃ to 60.1 ℃ in 5 minutes, representing a remarkable temperature increase of up to 35.1 ℃. In addition, after fluorination modification, the frosting and freezing time of the pit array structure that can store air pockets delay of over 40 minutes when compared to the surface of bare aluminum, and the excellent superhydrophobicity of the surface can make the melted ice water slide off quickly. Compared with traditional photothermal de-icing technology, this strategy of constructing microstructures by femtosecond laser without composite photothermal materials provides a new idea for the preparation of photothermal anti-icing/deicing surfaces.

This paper proposes a deep network-based defogging method to enhance the outdoor laser robot's vision perception capability, addressing the low visibility issues caused by atmospheric suspended particles, such as color bias, distortion, noise, etc., in the images. Firstly, flat and connected areas are identified and labeled as the sky light domain in the gradient domain and the gray domain, and the atmospheric light value is obtained using the quadtree method. Then, based on the input RGB feature map and the texture features extracted by the auto-encoder network, nonlinear mapping and transmission map reconstruction are completed. Finally, the estimated atmospheric light value and transmission rate are combined to restore the clear image using the atmospheric scattering model. Objective and subjective comparative experiments were conducted on our method, Retinex method, dual-threshold segmentation method, auto-encoder method, and AFF-Net method. The results showed that our method achieves superior objective indicators, both with and without reference, effectively preserving the original color tone of the real scene and generating clear, detailed images.

Flat-top beams are widely used in laser micromachining. Evaluation of the geometric morphology and uniformity of spot energy concentration area is important to judge the availability of beams. Obtaining the contour of energy concentration area of spot is a necessary step for analyzing geometric morphology. Routine methods are difficult to obtain the boundary surrounding the energy concentration area of spot as the edge features of flat-top beam are unobvious. In this paper, taking the flat-top beam in the laser homogenization system of laser lift-off equipment as an example, an adaptive contour extraction algorithm is proposed to obtain the contour of spot, which uses an adaptive gray threshold to divide the foreground and background, and then construct a convex hull of the maximum connected area to obtain the boundary. Geometric morphology indexes have consistent results between measured spot with noise and ideal spot image without noise based on this algorithm. The comparison results between the measured spot images with noise and ideal spot images show that RMS of light intensity has no significant difference between them. In this paper, based on spatial gray texture features from gray co-occurrence matrix, is proposed to evaluate the uniformity of flat-top beam, which has significant difference between the measured spot and ideal spot, and is helpful to evaluate the quality of flat-top beam.

The rapid advancements in information technology and artificial intelligence (AI) have fueled exponential data growth and an unprecedented demand for computational capabilities. However, the pace of computational power enhancement through integrated circuit technology advancements has struggled to keep up with the soaring needs of AI. Furthermore, traditional electronic computing systems, constrained by the Von Neumann Architecture, struggle to meet the stringent requirements of speed and power consumption. Optical computing systems emerge as promising solutions, addressing the computational limitations and power challenges faced by their electronic counterparts. At the heart of optical computing lie optical neural networks, realized through optical hardware, which inherently facilitate mathematical operations such as convolution, differentiation, and integration in a physical manner. Leveraging their inherent advantages of high parallelism, wide bandwidth, blazing speeds, and minimal power consumption, optical neural networks offer a viable path to alleviate the computational and power constraints hindering AI. Consequently, they hold significant potential for applications spanning image recognition, edge detection, voice recognition, and beyond.

This paper addresses the challenges of inefficient registration in point cloud data processing due to volume and complexity of spatial structures by introducing an optimized point cloud registration algorithm. The algorithm integrates a refined point cloud with an initial rough registration step. Key points are identified through voxel sampling, leveraging the variance in mean angles between neighborhood points and the center point normal line. The Fast Point Feature Histogram (FPFH) serves as the feature descriptor. In the search of correspondence relationship, according to the similarity of vector angle between adjacent matching pairs and Random Sampling Consistency (RANSAC) algorithm, filtering optimization is carried out to accurately correspond to the relationship and complete rough registration. Finally, the ICP algorithm is used to achieve accurate registration. The results show that the proposed algorithm effectively captures key points in regions of significant spatial variation, providing an advantageous initial position for precise registration and significantly reducing overall point cloud registration time.

In order to reduce the crosstalk in the channel, reduce the channel error rate, and improve the communication quality due to the non-linear effects such as Four-wave Mixing (FWM) in Wavelength Division Multiplexing (WDM) all-optical network, the mechanism of FWM in WDM all-optical network transmission system is analyzed, and a scheme combining the dispersion compensating grating with the wavelength assignment is presented. In 2-7 channel system, this scheme is numerically experimented with traditional scheme, wavelength assignment scheme, 100 GHz channel spacing scheme, and using dispersion compensated grating scheme. With the increase of the number of channels in the WDM all-optical network system, the effect of wavelength allocation scheme decreases gradually, while the effect of using the dispersion compensation grating to suppress the FWM effect increases gradually, and the dispersion value of the dispersion compensation grating also increases gradually. When the number of channels is small, using the wavelength assignment scheme, the average error rate of the channel is 6.5×10-217, and the average error rate of this scheme can reach 1.07×10-235. When the number of channels is large, using the dispersive compensated grating scheme, the average error rate of the channel is 1.36×10-51, and the average error rate of this scheme can reach 3.34×10-63. The results are of great importance to the minimization of FWM in WDM optical networks.

The massive data processing tasks in the era of big data and the development of artificial intelligence technology have produced explosive demand for computility. Due to the constraints of integrated circuit technology and Von Neumann architecture, it is difficult for traditional electronic computing systems to improve their computility and computing speed qualitatively. The lack of computility has become the bottleneck of the development of artificial intelligence technology, and the focus of new computing paradigms has changed from basic research to an urgent actual requirement. The characteristics of optical computing systems such as high speed, high parallelism, and low power consumption meet the requirements of the ideal computing paradigm. In this paper, the optical computing system based on metasurface with high design freedom and powerful functions, which conveniently to realize mathematical operations including convolution, differentiation, and integration. We carry out comparative studies in the field of artificial intelligence and deep learning, such as edge detection, pattern recognition, image processing, etc. The feasibility of metasurface optical computing system instead of electronic computing system is demonstrated.

Objective:To analyze the research hotspots and development trends of varicose vein of lower limb by bibliometrics method.Methods:Literatures on varicose vein of lower limb were collected by searching the core collection of the Web of Science database from the establishment of the database to June 13, 2022. VOSviewer1.6.17 was used to extract the authors, countries, institutions, keywords, etc. included into the study and generate a visual map of co-authors, countries, institutions, and keywords; the burst term detection function of CiteSpace6.1.R2 was used to search for burst point of keywords.Results:A total of 1 258 literature were included. The number of published papers related to varicose vein of lower limb maintained an upward trend. The United States is the country with the most number of published papers, and the University of California is the institution with the most number of published papers. A total of 4 971 authors published related research on varicose vein of lower limb, among which the most influential scholar is Labropoulos N of Medical Center, State University of New York at Stony Brook, who has published a total of 21 papers; the main authors formed 5 cooperative teams, and the cooperation within the team was relatively close, but the degree of cooperation between the teams was relatively low.Conclusion:The research hotspots of varicose vein of lower limb mainly focus on treatment methods, risk factors, complications, epidemiology, pathogenesis, etc. The treatment methods and risk management have become the frontier research fields of varicose vein of lower limb in recent years.