The Role of Particles in Fatigue Crack Propagation of Aluminum Matrix Composites and Casting Aluminum Alloys

疲劳裂缝繁殖( FCP )行为被学习原文如此理解 roleof 在 10 wt % SiCp/A2024 composites 的粒子和在扔铝 alloyA356.The 结果的 Si 粒子证明一些粒子在高三角洲 K 在 SiCp/Al compositeseven 出现在破裂表面上区域，它显示裂缝原文如此在 matrixavoiding 以内主要宣传了粒子由于粒子和强壮的 particle/matrixinterface.In 的高力量扔铝合金， Si

Inhibition effect of swelling characteristics of expansive soil using cohesive non-swelling soil layer under unidirectional seepage

Cohesive non-swelling soil(CNS)cushion technology is widely used to solve swelling deformation problems in expansive soil areas.However,the swelling inhibition mechanism is still not fully understood.In this study,the inhibition effect on expansive soil using a CNS layer was studied by performing five types of laboratory model tests under unidirectional seepage.The results showed that CNS cushion technology produced a sound inhibition effect on the swelling characteristics of expansive soil.It was shown that the cations in the CNS layer moved downward and accumulated on the surface of solids and produced an electrical environment inside the expansive soil.In this process,the adsorbed hydrated cations participated in ion exchange with the expansive soil,leading to the modification effect on its swelling potential.Meanwhile,the adsorbed water membrane surrounding the expansive soil aggregates formed by the hydrated cations obstructed further adsorption of water molecules,which inhibited the swelling development of expansive soil.Therefore,the swelling inhibition mechanism can be attributed to three factors:(i)modification effect,(ii)electrical environment,and(iii)deadweight of the CNS layer.The combined contribution of modification effect and electrical environment can be considered as an electric charge effect,which mainly controls the swelling characteristics of expansive soil.

Splitting and Merging Based Multi-model Fitting for Point Cloud Segmentation

This paper deals with the massive point cloud segmentation processing technology on the basis of machine vision, which is the second essential factor for the intelligent data processing of three dimensional conformation in digital photogrammetry. In this paper, multi-model fitting method is used to segment the point cloud according to the spatial distribution and spatial geometric structure of point clouds by fitting the point cloud into different geometric primitives models. Because point cloud usually possesses large amount of 3D points, which are uneven distributed over various complex structures, this paper proposes a point cloud segmentation method based on multi-model fitting. Firstly, the pre-segmentation of point cloud is conducted by using the clustering method based on density distribution. And then the follow fitting and segmentation are carried out by using the multi-model fitting method based on split and merging. For the plane and the arc surface, this paper uses different fitting methods, and finally realizing the indoor dense point cloud segmentation. The experimental results show that this method can achieve the automatic segmentation of the point cloud without setting the number of models in advance. Compared with the existing point cloud segmentation methods, this method has obvious advantages in segmentation effect and time cost, and can achieve higher segmentation accuracy. After processed by method proposed in this paper, the point cloud even with large-scale and complex structures can often be segmented into 3D geometric elements with finer and accurate model parameters, which can give rise to an accurate 3D conformation.