Kinetics of the \u3b4 to \u3b3 zirconium hydride transformation in Zr-2.5Nb

When the concentration of hydrogen exceeds the solubility limit in a metal matrix, metal hydrides may appear as precipitates that degrade the performance of the material. Neutron diffraction was combined with microscopy to study the [delta] to [gamma] phase transformation of zirconium hydride precipitates in Zr-2.5 wt.% Nb. Specimens were heated to dissolve all hydrides, then cooled to holding temperatures ranging from 17-100 ?C, to investigate the kinetics of transformation from the high-temperature [delta]-hydride to the low-temperature [gamma]-hydride. The [delta] to [gamma] transformation proceeds over a period of many hours, with a rate that increases as the holding temperature is decreased. Transmission Electron Microscopy images indicate that the boundary regions of hydride precipitates transform to the [gamma]-phase, leaving a shrinking core of the [delta]-phase. The crystallographic orientations of the hydrides appear to be determined by the texture of the [alpha]-Zr matrix, even after complete dissolution and re-precipitation.NRC publication: Ye

PointTriNet: Learned Triangulation of 3D Point Sets

21 pages, 9 figuresInternational audienceThis work considers a new task in geometric deep learning: generating a triangulation among a set of points in 3D space. We present PointTriNet, a differentiable and scalable approach enabling point set triangulation as a layer in 3D learning pipelines. The method iteratively applies two neural networks: a classification network predicts whether a candidate triangle should appear in the triangulation, while a proposal network suggests additional candidates. Both networks are structured as PointNets over nearby points and triangles, using a novel triangle-relative input encoding. Since these learning problems operate on local geometric data, our method is efficient and scalable, and generalizes to unseen shape categories. Our networks are trained in an unsupervised manner from a collection of shapes represented as point clouds. We demonstrate the effectiveness of this approach for classical meshing tasks, robustness to outliers, and as a component in end-to-end learning systems