Laser stripe center extraction is a key step for the profile measurement of line structured light sensors (LSLS). To accurately obtain the center coordinates at sub-pixel level, an improved gray-gravity method (IGGM) was proposed. Firstly, the center points of the stripe were computed using the gray-gravity method (GGM) for all columns of the image. By fitting these points using the moving least squares algorithm, the tangential vector, the normal vector and the radius of curvature can be robustly obtained. One rectangular region could be defined around each of the center points. Its two sides that are parallel to the tangential vector could alter their lengths according to the radius of the curvature. After that, the coordinate for each center point was recalculated within the rectangular region and in the direction of the normal vector. The center uncertainty was also analyzed based on the Monte Carlo method. The obtained experimental results indicate that the IGGM is suitable for both the smooth stripes and the ones with sharp corners. The high accuracy center points can be obtained at a relatively low computation cost. The measured results of the stairs and the screw surface further demonstrate the effectiveness of the method.

Porous structures can reduce the elastic modulus of implants, decrease stress shielding, and avoid bone loss in the alveolar bone and aseptic loosening of implants; however, there is a mismatch between yield strength and elastic modulus as well as biocompatibility problems. This study aimed to investigate the parametric design method of porous root-shaped implants to reduce the stress-shielding effect and improve the biocompatibility and long-term stability and effectiveness of the implants. Firstly, the porous structure part was parametrically designed, and the control of porosity gradient distribution was achieved by using the fitting relationship between porosity and bias and the position function of bias. In addition, the optimal distribution law of the porous structure was explored through mechanical and hydrodynamic analyses of the porous structure. Finally, the biomechanical properties were verified using simulated implant–bone tissue interface micromotion values. The results showed that the effects of marginal and central porosity on yield strength were linear, with the elastic modulus decreasing from 18.9 to 10.1 GPa in the range of 20–35% for marginal porosity, with a maximum decrease of 46.6%; the changes in the central porosity had a more consistent effect on the elastic modulus, ranging from 18.9 to 15.3 GPa in the range of 50–90%, with a maximum downward shift of 19%. The central porosity had a more significant effect on permeability, ranging from 1.9 × 10⁻⁷ m² to 4.9 × 10⁻⁷ m² with a maximum enhancement of 61.2%. The analysis showed that the edge structure had a more substantial impact on the mechanical properties. The central structure could increase the permeability more effectively. Hence, the porous structure with reasonable gradient distribution had a better match between mechanical properties and flow properties. The simulated implantation results showed that the porous implant with proper porosity gradient distribution had better biomechanical properties.

A line structured light sensor (LSLS) is generally constituted of a laser line projector and a camera. With the advantages of simple construction, non-contact, and high measuring speed, it is of great perspective in 3D measurement. For traditional LSLSs, the camera exposure time is usually fixed while the surface properties can be varied for different measurement tasks. This would lead to under/over exposure of the stripe images or even failure of the measurement. To avoid these undesired situations, an adaptive control method was proposed to modulate the average stripe width (ASW) within a favorite range. The ASW is first computed based on the back propagation neural network (BPNN), which can reach a high accuracy result and reduce the runtime dramatically. Then, the approximate linear relationship between the ASW and the exposure time was demonstrated via a series of experiments. Thus, a linear iteration procedure was proposed to compute the optimal camera exposure time. When the optimized exposure time is real-time adjusted, stripe images with the favorite ASW can be obtained during the whole scanning process. The smoothness of the stripe center lines and the surface integrity can be improved. A small proportion of the invalid stripe images further proves the effectiveness of the control method.

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.