Research on point cloud hole filling and 3D reconstruction in reflective area.

3D reconstruction is the process of obtaining the three-dimensional shape or surface structure of an object, which is widely used in advanced manufacturing fields such as automotive, aerospace, industrial inspection, and reverse engineering. However, due to the structural characteristics of the component itself, the reflective properties of the coating material, and other factors, there may be specular reflection during image acquisition, making it difficult to achieve complete 3D reconstruction of the component. This paper proposes a method to address the problem of incomplete 3D reconstruction of strongly reflective objects by recognizing outlier points and filling point cloud holes. The proposed View-Transform-PointNet outlier point recognition network improves the alignment of the initial point cloud plane and implements secondary alignment of the point cloud based on the perpendicularity between the outlier plane in mixed reflection and the point cloud plane. The point cloud hole-filling method is based on the principle of outlier formation and approximates a local Gaussian distribution to linear variation. The distance between the end of each outlier plane and the real surface is calculated to repair the depth information of outlier points. The proposed method achieves a 39.4% increase in the number of point cloud filling, a 45.2% increase in the number of triangular mesh faces, a 46.9% increase in surface area, and a chamfer distance (CD) of 0.4471009, which is better than existing geometric repair methods in terms of standard deviation and smoothness. The method improves the alignment of initial point cloud planes and enhances the accuracy of outlier point recognition, which are the main innovative points of this study. The 3D reconstruction of the repaired point cloud model is achieved through Poisson equation and parameter adjustment. The proposed method reduces the error caused by large curvature in the boundary region and improves the smoothness and accuracy of the reconstructed model.

Quantitative detection of phenol red by surface enhanced Raman spectroscopy based on improved GA-BP.

Phenol red (PR) is generally used as an acid-base indicator and a printing and dyeing colorant. When its content exceeds a certain concentration in water, it will cause great damage to the human body. Therefore, it is very important to detect the content of PR in water. The advantage of surface enhanced Raman scattering (SERS) is detecting samples quickly, non-destructive and high sensitivity without sample pre-treatment. SERS has attracted great attention in all fields of detection and analysis. In this paper, the method of attaching silver nanoparticles to metallic single-walled carbon nanotubes form carbon nanotubes/silver nanoparticles (CNTs/AgNPs) structure and then combining it with silica sheet is proposed. SERS substrate with silica/carbon nanotubes/silver nanoparticles (SiO2/CNTs/AgNPs) composite structure has extremely high reinforcement effect. In the quantitative analysis of the detected substance, mathematical fitting or machine learning is used to find the relationship between the intensity of Raman signal and the concentration of the detected substance. The BP neural network optimized by genetic algorithm (GA-BP) is designed in this study. The weights of GA-BP to enhance the robustness of BP neural network, the method of adaptive learning rate and the number of hidden nodes is set to solve the problem that GA-BP is easy to fall into local optimum, thus establishing a quantitative analysis model of PR solution concentration. The model can detect different concentrations of PR solutions with high accuracy quickly, simply and sensitively. Finally, compared with other published quantitative models, GA-BP correlation coefficient R2 determined by the training results of the model is 0.99996, and the root mean square error of the prediction is RMSEP = 0.002510, which is 0.0005 higher than the mathematical fitting method, it shows better performance. A reliable idea for the preparation of SERS substrate and online detection of PR concentration in water proposed in this study.

Continuous Carbonation for Synthesis of Pseudo-Boehmite by using Cross-Flow Rotating Packed Bed through the Reaction of NaAlO2 Solution with CO2 Gas.

This research established a novel method for the preparation of pseudo-boehmite (PB) via a continuous carbonation of CO2 gas and a NaAlO2 solution in a cross-flow rotating packed bed (CF-RPB). In the CF-RPB, the NaAlO2 solution can be sheared into fine liquid filaments and droplets, and react in full contact with the CO2 gas. Effects of synthesis parameters, including the concentration of the NaAlO2 solution, the gas-liquid ratio, the rotating speed of the CF-RPB, and the final pH of the solution on the crystal structure of PB, were fully investigated. A series of characterizations, including X-ray diffraction(XRD), scanning electron microscopy(SEM), transmission electron microscopy (TEM) and Brunauer-Emmett-Teller (BET) analysis, were carried out to explain the evaluation results and to find the relationship between PB properties and the synthetic conditions. The results showed that PB with a high specific surface area (495 m2/g) and large pore volume (2.125 cc/g) can be obtained when the concentration of the NaAlO2 solution was 0.1 mol/L, the gas-liquid ratio was 3:1, the rotating speed of RPB was 600 rpm, and the final pH was around 10.5. PB obtained by this method had a higher quality compared with that using a stirred tank reactor. Moreover, the continuous carbonation can be efficiently batch-produced, which provided a new idea for an industrial application.