Deep-Learning-Guided Point Cloud Modeling with Applications in Intelligent Manufacturing.

Machining feature recognition is a key technology to realize CAD/CAPP/CAM system integration. Aiming at high robustness of traditional processing feature recognition in image reasoning, an automatic processing shape recognition method based on fuzzy learning of processing surrounding point black data is proposed. The Cloud RNN in the PointNet stage strongly demonstrates that the framework originates from convolutional neural spider webs. Protector shape for detailed discoloration data on constructed prominence surfaces for automatic rifle recognition is conducted. Spot staining data sample library is also constructed. The prosecuting feature recognizer gained advantages through sample training, which realized robot-style notification of 36 processing shapes. This is conducted with a recognition accuracy rate of over 90%. The method is simple and efficient, although it is not suitable for point cloud data with backlash and defects. It is sensible and still has usable robustness and confirmation performance against mischief around shape peripheries due to shape intersections.

Spatial Distribution Characteristics of Microbial Mineralization in Saturated Sand Centrifuge Shaking Table Test.

Calcium carbonate induced by microorganisms can quickly fill and cement sand particles, thereby effectively reducing the potential for the liquefaction of sand. This process could represent a new green approach to the liquefaction treatment of saturated sand and has good prospects for application. However, owing to the diversity of microbial activities and the heterogenous spatiotemporal distribution of bacterial nutrient seepage in sandy soil foundations, the resultant complex distribution of calcium carbonate deposition in a sandy soil foundation can lead to differences in solidification strength and improvement effect. To understand the influence of earthquake action on the liquefaction resistance of saturated sand treated by microorganisms, and to evaluate the effect of microbial technology on sand liquefaction prevention under dynamic load, this study simulated the dynamic stress conditions of saturated sand under shear waves, using the world's first centrifuge shaking table (R500B), which realizes horizontal and vertical two-way vibration. On the basis of spatial heterogeneity of microbial mineralization after centrifuge shaking table tests, the effect of microbial strengthening on liquefied sand was analyzed, and the spatial distribution of calcium carbonate mineralization was examined. The results showed that the distribution of microorganisms in the solidified soil exhibited obvious spatial heterogeneity with a significant edge effect. Although microbial mineralization effectively improved the liquefaction resistance of saturated sand, a sudden change in the process of calcium carbonate deposition altered the cementation of the sand with depth. Moreover, the curing strength had obvious complexity and uncertainty that directly affected the shear stiffness of the soil under dynamic load, and this constitutes one of the reasons for the degradation of shear stiffness of sand during liquefaction. The derived conclusions could be used as a reference for engineering applications of microbial treatment of a liquefiable sandy soil foundation.