The high humidity of marine atmosphere and the existence of corrosive chloride ions lead to the premature corrosion failure of aluminum alloy components. The development of surface-strengthening technology provides an opportunity to prolong their service life spans. As a mature surface-strengthening technology, the shot peening process is widely used, owing to its advantages over other strengthening technologies, including its easy operation and high production rate. The shot-peened surface integrity depends on shot peening variables that introduces the thermomechanical effect to the deformed surface layer. When the inappropriate shot peening parameters are adopted, the shot-peened surface integrity could be deteriorated, which further weakens the corrosion performance of the surface. Therefore, it is essential to optimize shot peening process variables with the consideration of the material and its application. In this paper, the strengthening mechanism of the shot peening process was firstly elaborated, and then the effects of process parameters on the surface integrity of aluminum alloy were reviewed. The relationship between the surface integrity and corrosion resistance was also revealed. Two directions, including the application of the surface temperature rise during the shot peening process and the shot-peened surface roughening, are proposed.

The magnesium alloys Mg-0.5Mn-2Zn, Mg-1.0Mn-2Zn, and Mg-1.5Mn-2Zn (wt.%) with potential biomedical applications, synthesized by powder metallurgy, were investigated to evaluate the influence of manganese content on their microstructure, mechanical properties, and corrosion resistance. The results show that Mg-Mn-Zn alloys prepared by powder metallurgy reached the maximum compressive stress of 316 MPa and the maximum bending strength of 186 MPa, showing their good resistance to compression and bending, and meeting the mechanical properties required for the human bone plate. With an increase in manganese content, the corrosion resistance improved. In the polarization curve, the maximum positive shift of corrosion potential was 92 mV and the maximum decrease of corrosion current density was 10.2%. It was concluded that, of the alloys tested, Mg-1.0Mn-2.0Zn (wt.%) had the best overall performance, and its maximum compressive stress force and corrosion current density reached 232.42 MPa and 1.32 × 10⁻⁵ A·cm⁻², respectively, being more suitable for service in human body fluids.

This study addresses the challenges associated with incomplete or missing information in obstacle detection methods that employ a single sensor. Additionally, it tackles the issue of motion distortion in LiDAR point cloud data during synchronization and mapping in complex environments. The research introduces two significant contributions. Firstly, a novel obstacle detection method, named the point-map fusion (PMF) algorithm, was proposed. This method integrates point cloud data from the LiDAR, camera, and odometer, along with local grid maps. The PMF algorithm consists of two components: the point-fusion (PF) algorithm, which combines LiDAR point cloud data and camera laser-like point cloud data through a point cloud library (PCL) format conversion and concatenation, and selects the most proximate point cloud to the quadruped robot dog as the valid data; and the map-fusion (MF) algorithm, which incorporates local grid maps acquired using the Gmapping and OctoMap algorithms, leveraging Bayesian estimation theory. The local grid maps obtained by the Gmapping and OctoMap algorithms are denoted as map A and map B, respectively. This sophisticated methodology enables seamless map fusion, which significantly enhances the precision and reliability of the approach. Secondly, a motion distortion removal (MDR) method for LiDAR point cloud data based on odometer readings was proposed. The MDR method utilizes legged odometer data for linear data interpolation of the original distorted LiDAR point cloud data, facilitating the determination of the corresponding pose of the quadruped robot dog. Subsequently, the LiDAR point cloud data are then transformed to the quadruped robot dog coordinate system, efficiently mitigating motion distortion. Experimental results demonstrated that the proposed PMF algorithm achieved a 50% improvement in success rate compared to using only LiDAR or the PF algorithm in isolation, while the MDR algorithm enhanced mapping accuracy by 45.9% when motion distortion was taken into account. The effectiveness of the proposed methods was confirmed through rigorous experimentation.

The rolling process is one of the most effective ways for strengthening a part’s surface. As the press force exerted on specimen in rolling process, material in the surface layer will deform plastically if the press force is sufficient. That might result in grain refinement, dislocation configuration change, or phase change in specimen surface layer material. Consequently, the surface material mechanical properties can be changed. The effects of rolling parameters on surface residual stress, micro-hardness, and surface roughness for a 210Cr12 shaft have been investigated. After the rolling process, the surface residual stress of the specimen changes from tensile stress to compressive stress, and a stable residual compressive stress layer is formed. The maximum absolute value of compressive stress can be up to 216MPa. With the increase of the value of contact stress exerted on shaft surface and the number of rolling cycles, the absolute value of residual compressive stress increases firstly and then becomes stable. With the increase of depth from shaft surface to interior, the absolute value of residual compressive stress increases initially, then decreases and disappears finally. The maximum absolute value of residual compressive stress exists at the position beneath specimen surface about 0.025mm. The depth of residual stress layer is about 0.2 mm. Research results indicate that shaft surface microhardness can be improved within small range, surface roughness can be reduced up to 67%.