A novel mullite-bonded SiC-whisker-reinforced SiC matrix composite (SiCw/SiC, SiC whisker-to-SiC powder mass ratio of 1:9) was designed and successfully prepared. Before preparing the composite, the inexpensive lab-made SiCw was first modified by an oxidation/leaching process and then coated with Al2O3. The kinetics results indicate that the oxidation process can be described by improved shrinking-cylinder models. The aspect ratio of SiCw improved after modification. Subsequently, raw materials with a SiC–SiO2–Al2O3 triple-layered structure were obtained after the Al2O3-coating process and used as feedstocks during the subsequent hot-pressing sintering. Finally, the characterization of the composites indicates that the mullite-bonded sample performs better (relative density of 93.8?±?1.4%, flexural strength of 533.3?±?18.2?MPa, fracture toughness of 13.6?±?2.1?MPa?m1/2, and Vickers hardness of 20.6?±?2.5?GPa) than the reference sample without the mullite interface. The improved toughness could essentially be attributed to the moderately strong interface bonding and effective load transfer effects of the mullite interface. 相似文献
The Iterative Closest Point (ICP) scheme has been widely used for the registration of surfaces and point clouds. However, when working on depth image sequences where there are large geometric planes with small (or even without) details, existing ICP algorithms are prone to tangential drifting and erroneous rotational estimations due to input device errors. In this paper, we propose a novel ICP algorithm that aims to overcome such drawbacks, and provides significantly stabler registration estimation for simultaneous localization and mapping (SLAM) tasks on RGB-D camera inputs. In our approach, the tangential drifting and the rotational estimation error are reduced by: 1) updating the conventional Euclidean distance term with the local geometry information, and 2) introducing a new camera stabilization term that prevents improper camera movement in the calculation. Our approach is simple, fast, effective, and is readily integratable with previous ICP algorithms. We test our new method with the TUM RGB-D SLAM dataset on state-of-the-art real-time 3D dense reconstruction platforms, i.e., ElasticFusion and Kintinuous. Experiments show that our new strategy outperforms all previous ones on various RGB-D data sequences under different combinations of registration systems and solutions.