The characterization of reinforcement in 15% SiC particles reinforced AI matrix composites processed by powder metallurgy route was studied by statistical method. During the analysis, a new approach for the estimation...The characterization of reinforcement in 15% SiC particles reinforced AI matrix composites processed by powder metallurgy route was studied by statistical method. During the analysis, a new approach for the estimation of the characterization of reinforcement was presented. The mathematic software MATLAB was used to calculate the area and perimeter of reinforcement, in which the image processing technique was applied. Based on the calculation, the fractal dimension, shape factor, reinforcement size distribution and reinforcement distribution were investigated. The results show that the reinforcement shape is similar to rectangle; the reinforcement size distribution is broad with the＇ range of 1-12 μm; the topography of reinforcement is smooth; and the reinforcement distribution is inhomogeneous. Furthermore, the cell model based on the statistical characterization was established and tested.展开更多
In this work, we make the best use of the vanadium element; a series of A1-V-B alloys and VB2/A390 composite alloys were fabricated. For Ak-10V-6B alloy, the grain size of VB2 can be controlled within about 1 μm and ...In this work, we make the best use of the vanadium element; a series of A1-V-B alloys and VB2/A390 composite alloys were fabricated. For Ak-10V-6B alloy, the grain size of VB2 can be controlled within about 1 μm and is distributed uniformly in the AI matrix. Further, it can be found that VB2 promises to be a useful reinforcement particle for piston alloy. The addition of VB2 can improve the mechanical properties of the A390 composite alloys significantly. The results show that with 1 % VB2 addition, A390 composite alloy exhibits the best performance. Compared with the A390 alloy, the coefficient of thermal expansion is 13.2 × 10^-6 K-1, which decreased by 12.6%; the average Brinell hardness can reach 156.5 HB, wear weight loss decreased by 28.9% and ultimate tensile strength at 25℃ （UTS25 ℃） can reach 355 MPa, which increased by 36.5%.展开更多
AA6061-10 vol.% SiC composite was successfully prepared by spark plasma sintering. The deformation behaviour of this composite was studied using the uniaxial compression test, which was conducted at temperatures betwe...AA6061-10 vol.% SiC composite was successfully prepared by spark plasma sintering. The deformation behaviour of this composite was studied using the uniaxial compression test, which was conducted at temperatures between 300 and 500℃ and strain rates between 0.001 and 1 s^-1. Results indicate that the stress-strain curves of the AA6061-10 vol.% SiC composite typically feature dynamic recrystallization. The steady stress can be described by a hyperbolic sine constitutive equation, and the activation energy of the composite is 230.88 kJ/mol. The processing map was established according to the dynamic materials model. The optimum hot deformation temperature is 450-500℃ and the strain rate is 1-0.1 s^-1. The instability zones of flow behaviour can also be identified using the processing map.展开更多
文摘The characterization of reinforcement in 15% SiC particles reinforced AI matrix composites processed by powder metallurgy route was studied by statistical method. During the analysis, a new approach for the estimation of the characterization of reinforcement was presented. The mathematic software MATLAB was used to calculate the area and perimeter of reinforcement, in which the image processing technique was applied. Based on the calculation, the fractal dimension, shape factor, reinforcement size distribution and reinforcement distribution were investigated. The results show that the reinforcement shape is similar to rectangle; the reinforcement size distribution is broad with the＇ range of 1-12 μm; the topography of reinforcement is smooth; and the reinforcement distribution is inhomogeneous. Furthermore, the cell model based on the statistical characterization was established and tested.
基金supported by the National Basic Research Program of China ("973 Program", No. 2012CB825702)the National Natural Science Foundation of China (Nos. 51001065 and 51071097)+1 种基金the Taishan Scholar Blue Industry Talents Support Program of Shandong Province (2013)Young Scholars Program of Shandong University
文摘In this work, we make the best use of the vanadium element; a series of A1-V-B alloys and VB2/A390 composite alloys were fabricated. For Ak-10V-6B alloy, the grain size of VB2 can be controlled within about 1 μm and is distributed uniformly in the AI matrix. Further, it can be found that VB2 promises to be a useful reinforcement particle for piston alloy. The addition of VB2 can improve the mechanical properties of the A390 composite alloys significantly. The results show that with 1 % VB2 addition, A390 composite alloy exhibits the best performance. Compared with the A390 alloy, the coefficient of thermal expansion is 13.2 × 10^-6 K-1, which decreased by 12.6%; the average Brinell hardness can reach 156.5 HB, wear weight loss decreased by 28.9% and ultimate tensile strength at 25℃ （UTS25 ℃） can reach 355 MPa, which increased by 36.5%.
基金supported by the National Basic Research Program of China(“973”Project)(Grant No.2013CB733000)
文摘AA6061-10 vol.% SiC composite was successfully prepared by spark plasma sintering. The deformation behaviour of this composite was studied using the uniaxial compression test, which was conducted at temperatures between 300 and 500℃ and strain rates between 0.001 and 1 s^-1. Results indicate that the stress-strain curves of the AA6061-10 vol.% SiC composite typically feature dynamic recrystallization. The steady stress can be described by a hyperbolic sine constitutive equation, and the activation energy of the composite is 230.88 kJ/mol. The processing map was established according to the dynamic materials model. The optimum hot deformation temperature is 450-500℃ and the strain rate is 1-0.1 s^-1. The instability zones of flow behaviour can also be identified using the processing map.
