Laser powder bed fusion (LPBF) is of great importance for the visual measurement and analysis of the metallization process, which is the process of solid, liquid, and gas phase transformations of metal powders under high-energy laser irradiation due to the low boiling point/high saturated vapor pressure. Since the evaporation of metals involves the interaction of driving forces such as vapor back pressure, surface tension, and gravity, the movement of the melt pool is not stable. At the same time, it also produces vaporization products such as vapor plumes and sprays, which cause defects such as bubbles, porosity, lack of fusion, inclusions, etc., during the manufacturing process of the parts, affecting the performance and manufacturing quality of the parts. More and more researchers are using imaging technologies, such as high-speed X-ray, high-speed visible light cameras, and high-speed schlieren imaging, to perform noncontact visual measurements and analyses of the melt pool, vapor plume, and spatter during the metal evaporation process, and the results show that the metal evaporation process can be suppressed by optimizing the process parameters and changing the processing atmosphere, thereby reducing part defects and improving part performance and built part quality. This paper reviews the research on metal evaporation mechanisms and visual measurement methods of metal evaporation, then discusses the measures of metal evaporation, and finally summarizes and prospects the future research hotspots of LPBF technology, according to the existing scholars’ research on numerical simulation analysis and visual measurement methods of the metal evaporation process.

To address the issues of insufficient strength and poor precision in polystyrene forming parts during the selective laser sintering process, a ternary composite of polystyrene/glass fiber/hollow glass microbeads was prepared through co-modification by incorporating glass fiber and hollow glass microbeads into polystyrene using a mechanical mixing method. The bending strength and dimensional accuracy of the sintered composites were investigated by conducting an orthogonal test and analysis of variance to study the effects of laser power, scanning speed, scanning spacing, and delamination thickness. The process parameters were optimized and selected to determine the optimal combination. The results demonstrated that when considering bending strength and Z-dimensional accuracy as evaluation criteria for terpolymer sintered parts, the optimum process parameters are as follows: laser power of 24 W, scanning speed of 1600 mm/s, scanning spacing of 0.24 mm, and delamination thickness of 0.22 mm. Under these optimal process parameters, the bending strength of sintered parts reaches 6.12 MPa with a relative error in the Z-dimension of only 0.87%. The bending strength of pure polystyrene sintered parts is enhanced by 15.69% under the same conditions, while the relative error in the Z-dimension is reduced by 63.45%. It improves the forming strength and precision of polystyrene in the selective laser sintering process and achieves the effect of enhancement and modification, which provides a reference and a new direction for exploring polystyrene-based high-performance composites and expands the application scope of selective laser sintering technology.

The materials currently used for bone prostheses are mainly metals such as titanium alloy. Compared with human cortical bone, traditional metals exhibit high elastic moduli, which may lead to stress shielding. In contrast, the mechanical strength and elastic modulus of polyetheretherketone are slightly lower than those of human bone. In this study, we utilize multi-component co-modification in order to improve the mechanical properties of polyetheretherketone materials. Firstly, we blended three types of materials: multi-walled carbon nanotubes, nanohydroxyapatite, and carbon fibers. Then, the blended materials were used to reinforce polyetheretherketone, and the preparation of the multi-walled carbon nanotubes/nanohydroxyapatite/carbon fibers was described in detail. After the contact angle was determined, SEM, thermogravimetric analysis, and various tests were conducted on the ternary composites. Ball milling co-mingling technology was used to prepare tetrad composite specimens for tensile and compression experiments, a finite element model of the tetrad composite tensile and compression specimens was established, and the structural stresses of the specimens were analyzed under the specified loads. The experimental results show that the surface roughness of carbon fiber increased, and the multi-walled carbon nanotube/nanohydroxyapatite/carbon fiber/polyetheretherketone quaternary composites were well optimized in terms of strength and elastic modulus, which broadens the application field of polyetheretherketone materials.

Improving the quality of metal additive manufacturing parts requires online monitoring of the powder bed laying procedure during laser powder bed fusion production. In this article, a visual online monitoring tool for flaws in the powder laying process is examined, and machine vision technology is applied to LPBF manufacture. A multiscale improvement and model channel pruning optimization method based on convolutional neural networks is proposed, which makes up for the deficiencies of the defect recognition method of small-scale powder laying, reduces the redundant parameters of the model, and enhances the processing speed of the model under the premise of guaranteeing the accuracy of the model. Finally, we developed an LPBF manufacturing process laying powder defect recognition algorithm. Test experiments show the performance of the method: the minimum size of the detected defects is 0.54 mm, the accuracy rate of the feedback results is 98.63%, and the single-layer laying powder detection time is 3.516 s, which can realize the effective detection and control of common laying powder defects in the additive manufacturing process, avoids the breakage of the scraper, and ensures the safe operation of the LPBF equipment.

Aiming to solve the problem that the significant error between the actual joint torque and the calculated joint torque of a welding robot leads to the vibration of the end-effector, which in turn affects the stability of the end-effector, this paper proposes a identification algorithm based on the Weighted Least Squares Genetic Algorithm (WLS-GA) to construct and solve the dynamical model to obtain the accurate dynamical parameters. Firstly, a linear model of welding robot dynamics is derived. The fifth-order optimal Fourier series excitation trajectory is designed to collect experimental data such as joint torque. Then, a rough solution of the parameters to be recognized is obtained by solving the dynamics model through the Weighted Least Squares (WLS) method, the search space is determined based on the rough solution, and the optimal solution is obtained by using the Genetic Algorithm (GA) to perform a quadratic search in the search space. Finally, the identification data obtained from the algorithm is analyzed and compared with the experimental data. The results show that the error between the identification data obtained using the WLS-GA identification algorithm and the experimental data is relatively small. The results show that the identification data obtained using the WLS-GA identification algorithm have less error than the experimental data, taking the Root Mean Square (RMS) value of the joint torque error obtained using the weighted least squares algorithm as a criterion. The accuracy of the WLS-GA identification algorithm can be improved by up to 66.85% compared with that of the weighted least squares algorithm and by up to 78.0% compared with that of the Ordinary Least Squares (OLS) algorithm. In summary, the WLS-GA identification algorithm can accurately identify the dynamic parameters of the welding robot and more accurately construct a dynamic model to solve the effect of joint torque error on the control characteristics of the welding robot. It can improve the stability of the end-effector of the welding robot to ensure the quality of the automobile body and beam welding and welding speed.

Aiming at the current high-speed real-time inspection demand faced in the production of filter rods with cores and the limitations of traditional quality control methods, this study proposes a high-speed online inspection method based on machine vision. As the production speed of filter rods increases, the traditional inspection methods cannot meet the real-time and accuracy requirements in a high-speed environment, while the restricted production environment and more disturbing factors lead to an increase in the detection error. Therefore, this paper proposes an efficient visual inspection method, which combines the ellipse fitting algorithm to accurately obtain the coordinates of the filter rods and their mandrel centre points, and evaluates the mandrel deviation through the Euclidean distance calculation to judge the product qualification. The experimental results show that the system is capable of inspecting at a rate of 4200 pcs per minute in a complex environment with a defect detection rate of more than 95%. The technology significantly improves the inspection efficiency and product quality of the filter rod production line, providing a reliable intelligent inspection solution for the filter rod manufacturing industry.