Effects of Al_2O_3 Nano-additive on Performance of Micro-arc Oxidation Coatings Formed on AZ91D Mg Alloy

Ceramic coatings were prepared on AZ91 D Mg alloy by micro-arc oxidation （MAO） in aluminate electrolytes, with Al2O3 nano-additive suspending at different concentrations. Effects of nano-additive concentration on the structure, phase composition, hardness and anti-corrosion property of the MAO coatings were analyzed by scanning electron microscopy, X-ray diffraction, micro-hardness test and electrochemical method, respectively. The results revealed that Al2O3 nano-particles were mostly incorporated into ceramic coating chemically, transferred into MgAl2O4, rather than being trapped mechanically during MAO process. With the increase of Al2O3 concentration, the voltage-time response, content of MgAl2O4, hardness and anti-corrosion property increased. However, when the concentration varied from 10 g/L to 15 g/L, these behaviors and properties changed only a little. This result indicated that, after the concentration of Al2O3 nano-additive reaching 10 g/L, the incorporation of Al2O3 nano-particles turned into a saturation state, due to the complex process during MAO treatment. Therefore, 10 g/L might be a proper concentration for MAO coating to incorporate Al2O3 nano-particles,

Denoising method of X-ray phase contrast DR image for TRISO-coated fuel particles

TRISO （tristructural-isotropic） fuel is a type of micro fuel particles used in high-temperature gas-cooled reactors （HTGRs）. Among the quality evaluation methods for such particles, inqine phase contrast imaging technique （PCI） is more feasible for nondestructive measurement. Due to imaging hardware limitations, high noise level is a distinct feature of PCI images, and as a result, the dimensional measurement accuracy of TRISO-coated fuel particles decreases. Therefore, we propose an improved denoising hybrid model named as NL P-M model which introduces non-local theory and retains the merits of the Perona-Malik （P-M） model. The improved model is applied to numerical simulation and practical PCI images. Quanti- tative analysis proves that this new anisotropic diffusion model can preserve edge or texture information effectively, while ruling out noise and distinctly decreasing staircasing artifacts. Especially during the process of coating layer thickness measurement, the NL P-M model makes it easy to obtain continuous contours without noisy points or fake contour segments, thus enhancing the measurement accuracy. To address calculation complexity, a graphic processing unit （GPU） is adopted to realize the acceleration of the NL P-M denoising.