As for the ultra-precision grinding of the hemispherical fused silica resonator,due to the hard and brittle nature of fused silica,subsurface damage(SSD)is easily generated,which enormously influences the performance ...As for the ultra-precision grinding of the hemispherical fused silica resonator,due to the hard and brittle nature of fused silica,subsurface damage(SSD)is easily generated,which enormously influences the performance of such components.Hence,ultra-precision grinding experiments are carried out to investigate the surface/subsurface quality of the hemispherical resonator machined by the small ball-end fine diamond grinding wheel.The influence of grinding parameters on the surface roughness(SR)and SSD depth of fused silica samples is then analyzed.The experimental results indicate that the SR and SSD depth decreased with the increase of grinding speed and the decrease of feed rate and grinding depth.In addition,based on the material strain rate and the maximum undeformed chip thickness,the effect of grinding parameters on the subsurface damage mechanism of fused silica samples is analyzed.Furthermore,a multi-step ultra-precision grinding technique of the hemispherical resonator is proposed based on the interaction influence between grinding depth and feed rate.Finally,the hemispherical resonator is processed by the proposed grinding technique,and the SR is improved from 454.328 nm to 110.449 nm while the SSD depth is reduced by 94%from 40μm to 2.379μm.The multi-step grinding technique proposed in this paper can guide the fabrication of the hemispherical resonator.展开更多
The designation of the cluster number K and the initial centroids is essential for K-modes clustering algorithm. However, most of the improved methods based on K-modes specify the K value manually and generate the ini...The designation of the cluster number K and the initial centroids is essential for K-modes clustering algorithm. However, most of the improved methods based on K-modes specify the K value manually and generate the initial centroids randomly, which makes the clustering algorithm significantly dependent on human-based decisions and unstable on the iteration time. To overcome this limitation, we propose a cohesive K-modes (CK-modes) algorithm to generate the cluster number K and the initial centroids automatically. Explicitly, we construct a labeled property graph based on index-free adjacency to capture both global and local cohesion of the node in the sample of the input datasets. The cohesive node calculated based on the property similarity is exploited to split the graph to a K-node tree that determines the K value, and then the initial centroids are selected from the split subtrees. Since the property graph construction and the cohesion calculation are only performed once, they account for a small amount of execution time of the clustering operation with multiple iterations, but significantly accelerate the clustering convergence. Experimental validation in both real-world and synthetic datasets shows that the CK-modes algorithm outperforms the state-of-the-art algorithms.展开更多
基金This work was supported by the National Key Research and Development Program of China(No.2022YFB3403600)the National Natural Science Foundation of China(No.52293403)Self-Planned Task of State Key Laboratory of Robotics and System(HIT)(No.SKLRS202204C).
文摘As for the ultra-precision grinding of the hemispherical fused silica resonator,due to the hard and brittle nature of fused silica,subsurface damage(SSD)is easily generated,which enormously influences the performance of such components.Hence,ultra-precision grinding experiments are carried out to investigate the surface/subsurface quality of the hemispherical resonator machined by the small ball-end fine diamond grinding wheel.The influence of grinding parameters on the surface roughness(SR)and SSD depth of fused silica samples is then analyzed.The experimental results indicate that the SR and SSD depth decreased with the increase of grinding speed and the decrease of feed rate and grinding depth.In addition,based on the material strain rate and the maximum undeformed chip thickness,the effect of grinding parameters on the subsurface damage mechanism of fused silica samples is analyzed.Furthermore,a multi-step ultra-precision grinding technique of the hemispherical resonator is proposed based on the interaction influence between grinding depth and feed rate.Finally,the hemispherical resonator is processed by the proposed grinding technique,and the SR is improved from 454.328 nm to 110.449 nm while the SSD depth is reduced by 94%from 40μm to 2.379μm.The multi-step grinding technique proposed in this paper can guide the fabrication of the hemispherical resonator.
基金supported by the National Natural Science Foundation of China(62125404,62004193,61922077,12274456,92163206,51991342,and 11874347)the Strategic Priority Research Program of Chinese Academy of Sciences(XDB43000000)+3 种基金the Guangdong Major Project of Basic and Applied Basic Research(2021B0301030002)the National Key R&D Program of China(2021YFA1400502 and 2022YFA1405600)supported by the Youth Innovation Promotion Association of Chinese Academy of Sciences(Y2021042)the Nanofabrication Laboratory in the National Centre for Nanoscience and Technology for the electron beam lithography。
基金supported by the National Natural Science Foundation of China under Grant No. 61772534the Excellent Chinese-Foreign Youth Exchange Foundation Program of Chinese Association of Science and Technology under Grant No. 311319000207.
文摘The designation of the cluster number K and the initial centroids is essential for K-modes clustering algorithm. However, most of the improved methods based on K-modes specify the K value manually and generate the initial centroids randomly, which makes the clustering algorithm significantly dependent on human-based decisions and unstable on the iteration time. To overcome this limitation, we propose a cohesive K-modes (CK-modes) algorithm to generate the cluster number K and the initial centroids automatically. Explicitly, we construct a labeled property graph based on index-free adjacency to capture both global and local cohesion of the node in the sample of the input datasets. The cohesive node calculated based on the property similarity is exploited to split the graph to a K-node tree that determines the K value, and then the initial centroids are selected from the split subtrees. Since the property graph construction and the cohesion calculation are only performed once, they account for a small amount of execution time of the clustering operation with multiple iterations, but significantly accelerate the clustering convergence. Experimental validation in both real-world and synthetic datasets shows that the CK-modes algorithm outperforms the state-of-the-art algorithms.
