Characteristics of Friction Plug Joints for AA2219-T87 FSW Welds

In this study, Friction plug welding (FPW) for 8 mm thickness AA2219-T87 sheets were carried out, and defect-free joints were obtained. The geometric size of plug and plate hole, rotational speed and welding force exhibit significant effects on the weld formation. Meanwhile, it is concluded that significant inhomogeneity of microstructure and mechanical properties exists in FPW joints. The recrystallization zone has the highest mechanical properties owing to the fine equiaxed grains and uniformly distributed θ precipitates. The entire plug, thermo-mechanically affected zone and nugget thermo-mechanically affected zone closed to the bonding interface are significantly softened due to the deformation of the grains and θ’ precipitate dissolution. The ultimate tensile strength (UTS) and elongation of the FPW joints can reach 359 MPa and 7.3% at 77 K and 305 MPa and 5% at 298 K, respectively

Microstructure and properties of stationary shoulder friction stir welded joints for aluminum alloy thick-plate

The stationary shoulder friction stir welding (SSFSW) processes for 2A14-T4 aluminum alloy with the thickness of 8.5 mm were performed by using the self-developed tools, and the influences of welding process parameters on the microstructure and mechanical properties of SSFSW welded joints were investigated. The results show that the SSFSW joints with smooth weld surface and defect-free for aluminum alloy thick-plate can only be obtained under the process parameter condition of lower rotational speed (rotational speed ω=400-600 r/min and welding speed v=60-120 mm/min).The weld zone of SSFSW joints mainly consists of nugget zone (NZ), and the widths of thermo-mechanically affected zone (TMAZ) and the heat affected zone (HAZ) around the NZ are obviously reduced; the NZ is similar with the shape of tool pin and it is composed of two kinds of fine equiaxed grains with different sizes, the grains on the advancing side are more finer than that of retreating side. The profiles of microhardness across the weld section present the "W" shape, the hardness values of NZ reach the 80%-90% of the base metal of hardness value, the softened region is produced between interfaces of TMAZ and HAZ, and its hardness is the lowest with the 72% of the base metal of hardness value. The tensile strengths of SSFSW joints reach the 88% of base metal under the welding parameters of ω=500 r/min, v=140 mm/min, and the fractured sites are always located at the softened zones between TMAZ and HAZ on the retreating side, exhibiting the toughness fracture features

Regional Time-Series Coding Network and Multi-View Image Generation Network for Short-Time Gait Recognition

Gait recognition is one of the important research directions of biometric authentication technology. However, in practical applications, the original gait data is often short, and a long and complete gait video is required for successful recognition. Also, the gait images from different views have a great influence on the recognition effect. To address the above problems, we designed a gait data generation network for expanding the cross-view image data required for gait recognition, which provides sufficient data input for feature extraction branching with gait silhouette as the criterion. In addition, we propose a gait motion feature extraction network based on regional time-series coding. By independently time-series coding the joint motion data within different regions of the body, and then combining the time-series data features of each region with secondary coding, we obtain the unique motion relationships between regions of the body. Finally, bilinear matrix decomposition pooling is used to fuse spatial silhouette features and motion time-series features to obtain complete gait recognition under shorter time-length video input. We use the OUMVLP-Pose and CASIA-B datasets to validate the silhouette image branching and motion time-series branching, respectively, and employ evaluation metrics such as IS entropy value and Rank-1 accuracy to demonstrate the effectiveness of our design network. Finally, we also collect gait-motion data in the real world and test them in a complete two-branch fusion network. The experimental results show that the network we designed can effectively extract the time-series features of human motion and achieve the expansion of multi-view gait data. The real-world tests also prove that our designed method has good results and feasibility in the problem of gait recognition with short-time video as input data