Microstructure, mechanical properties and machinability of particulate reinforced Al matrix composites: a comparative study between SiC particles and high-entropy alloy particles

In this study, 2024Al matrix composites reinforced by SiC particles (SiC-2024Al) and nanocrystalline high-entropy alloy particles (HEA-2024Al) fabricated by powder metallurgy were systematically compared for the first time. There is a significant difference in microstructure and mechanical properties as well as machinability between two kinds of composites. In term of microstructure, when the volume fraction of reinforcements was 10%, both SiC-2024Al and HEA-2024Al composites showed a homogeneous particle distribution in the matrix. With the increase of reinforcement content, HEA-2024Al composites presented denser microstructure than that of SiC-2024Al composites. The composites with 10, 20 and 30 vol.% HEA reinforcements all showed better plasticity than that of the SiC-2024Al composites with same volume fraction of reinforcements, which was related with better particle distribution and interface bonding. However, the strength showed the opposite tendency in the two kinds of composites. Selecting 10SiC-2024Al and 10HEA-2024Al composites as examples to explore the difference in the yield strength of two kinds of composites, it is ascribed to the dislocation punched zones around interface between the Al matrix and reinforcements, which was analyzed in detail by a combination of calculation, nanoindentation tests and finite element analysis. Additionally, HEA-2024Al composites showed better machinability than those of SiC-2024Al composites. This work provides insight into the application of particulate reinforced Al matrix composites

The Kernel Based Multiple Instances Learning Algorithm for Object Tracking

To realize real time object tracking in complex environments, a kernel based MIL (KMIL) algorithm is proposed. The KMIL employs the Gaussian kernel function to deal with the inner product used in the weighted MIL (WMIL) algorithm. The method avoids computing the pos-likely-hood and neg-likely-hood many times, which results in a much faster tracker. To track an object with different motion, the searching areas for cropping the instances are varied according to the object’s size. Furthermore, an adaptive classifier updating strategy is presented to handle with the occlusion, pose variations and illumination changes. A similar score range is defined with respect to two given thresholds and a similar score from the second frame. Then, the learning rate will be set to be a small value when a similar score is out of the range. In contrast, a big learning rate is used. Finally, we compare its performance with that of the state-of-art algorithms on several classical videos. The experimental results show that the presented KMIL algorithm is faster and robust to the partial occlusion, pose variations and illumination changes

Mechanical behavior and deformation mechanism of shape memory bulk metallic glass composites synthesized by powder metallurgy

The synthesis of martensitic or shape-memory bulk metallic glass composites (BMGCs) via solidification of the glass-forming melts requires the meticulous selection of the chemical composition and the proper choice of the processing parameters in order to ensure that the glassy matrix coexists with the desired amount of austenitic phase. Unfortunately, a relatively limited number of such systems, where austenite and glassy matrix coexist over a wide range of compositions, is available. Here, we study the effectiveness of powder metallurgy as an alternative to solidification for the synthesis of shape memory BMGCs. Zr$_{48}$Cu$_{36}$Al$_{8}$Ag$_8$ matrix composites with different volume fractions of Ni$_{50.6}$Ti$_{49.4}$ are fabricated using hot pressing and their microstructure, mechanical properties and deformation mechanism are investigated employing experiments and simulations. The results demonstrate that shape-memory BMGCs with tunable microstructures and properties can be synthesized by hot pressing. The phase stability of the glass and austenitic components across a wide range of compositions allows us to examine fundamental aspects in the field of shape memory BMGCs, including the effect of the confining stress on the martensitic transformation exerted by the glassy matrix, the contribution of each phase to the plasticity and the mechanism responsible for shear band formation. The present method gives a virtually infinite choice among the possible combinations of glassy matrices and shape memory phases, expanding the range of accessible shape memory BMGCs to systems where the glassy and austenitic phases do not form simultaneously using the solidification route