Browsing by Author "Yao, J."
Now showing 1 - 2 of 2
- Results Per Page
- Sort Options
Item Stereolithography of SiOC Polymer-Derived Ceramics Filled with SiC Micronwhiskers(WILEY-VCH Verlag) Brinckmann, S. A.; Patra, N.; Yao, J.; Ware, Taylor H.; Frick, C. P.; Fertig, R. S.,III; Ware, Taylor H.Due to complicated manufacturing methods and lack of machinability, the use of engineering ceramics is limited by the manufacturing processes used to fabricate parts with intricate geometries. The 3D printing of polymers that can be pyrolyzed into functional ceramics has recently been used to significantly expand the range of geometries that can be manufactured, but large shrinkage during pyrolysis has the potential to lead to cracking. In this work, a method to additively manufacture particle-reinforced ceramic matrix composites is described. Specifically, stereolithography is used to crosslink a resin comprised of acrylate and vinyl-functionalized siloxane oligomers with dispersed SiC whiskers. After crosslinking, the part is pyrolyzed to amorphous SiOC while the SiC whiskers remain unaffected. Composite ceramics shrink 37% while unreinforced parts shrink 42%; this significant reduction in shrinkage improves part stability. Importantly, these ceramic matrix composites contain no visible porosity nor cracking on the microstructural level. With the introduction of SiC, hardness increases from 10.8 to 12.1 GPa and density decreases from 2.99 to 2.86 g cm−3. Finally, printed ceramic porous structures, gears, and components for turbine blades are demonstrated. Applying stereolithographic techniques to ceramic matrix composites, this work may improve processing and properties of ceramics for applications that require complex geometries. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, WeinheimItem Superpixel Generation by Agglomerative Clustering with Quadratic Error Minimization(Blackwell Publishing Ltd) Dong, Xiao; Chen, Z.; Yao, J.; Guo, Xiaohu; Dong, Xiao; Guo, XiaohuSuperpixel segmentation is a popular image pre-processing technique in many computer vision applications. In this paper, we present a novel superpixel generation algorithm by agglomerative clustering with quadratic error minimization. We use a quadratic error metric (QEM) to measure the difference of spatial compactness and colour homogeneity between superpixels. Based on the quadratic function, we propose a bottom-up greedy clustering algorithm to obtain higher quality superpixel segmentation. There are two steps in our algorithm: merging and swapping. First, we calculate the merging cost of two superpixels and iteratively merge the pair with the minimum cost until the termination condition is satisfied. Then, we optimize the boundary of superpixels by swapping pixels according to their swapping cost to improve the compactness. Due to the quadratic nature of the energy function, each of these atomic operations has only O(1) time complexity. We compare the new method with other state-of-the-art superpixel generation algorithms on two datasets, and our algorithm demonstrates superior performance. ©2018 The Authors Computer Graphics Forum ©2018 The Eurographics Association and John Wiley & Sons Ltd.