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一个关于合成面心立方3D多晶微结构、逐晶粒微观结构描述符以及单轴拉伸变形下晶粒平均应力场的数据集。

A dataset of synthetic face centered cubic 3D polycrystalline microstructures, grain-wise microstructural descriptors and grain averaged stress fields under uniaxial tensile deformation.

作者信息

Mangal Ankita, Holm Elizabeth A

机构信息

Carnegie Mellon University, United States.

出版信息

Data Brief. 2018 Jun 28;19:2029-2036. doi: 10.1016/j.dib.2018.06.072. eCollection 2018 Aug.

Abstract

This data article presents a data set comprised of 36 synthetic 3D equiaxed polycrystalline microstructures, the microstructural descriptors for each grain, and the stress and strain fields resulting from crystal plasticity simulations mimicking uniaxial tensile deformation to a total strain of 4%. This is related to the research article entitled "Applied Machine Learning to predict stress hotspots I: Face Centered Cubic Materials" (Mangal and Holm, 2018) [1]. The microstructures were created using an open source Dream.3D software tool, and the crystal plasticity simulations were carried out using the elasto-viscoplastic fast Fourier transform (EVPFFT) method. Six different kinds of FCC textures are represented with six stochastically different microstructures with varying texture intensity for each texture kind. This dataset is freely available in a Mendeley Data archive "A dataset of synthetic face centered cubic 3D polycrystalline microstructures, grain-wise microstructural descriptors and grain averaged stress fields under uniaxial tensile deformation" located at 〈http://dx.doi.org/10.17632/ss75fdg5dg.1〉 for any academic, educational, or research purposes.

摘要

本数据文章展示了一个数据集,该数据集由36个合成的三维等轴多晶微观结构、每个晶粒的微观结构描述符以及模拟单轴拉伸变形至总应变为4%的晶体塑性模拟所产生的应力和应变场组成。这与题为《应用机器学习预测应力热点I:面心立方材料》(Mangal和Holm,2018年)[1]的研究文章相关。微观结构是使用开源的Dream.3D软件工具创建的,晶体塑性模拟是使用弹粘塑性快速傅里叶变换(EVPFFT)方法进行的。六种不同类型的面心立方织构由六种随机不同的微观结构表示,每种织构类型具有不同的织构强度。该数据集可在Mendeley数据存档库《单轴拉伸变形下合成面心立方三维多晶微观结构、晶粒级微观结构描述符和晶粒平均应力场的数据集》中免费获取,网址为〈http://dx.doi.org/10.17632/ss75fdg5dg.1〉,供任何学术、教育或研究目的使用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/31e6/6141364/bf33fa929559/gr1.jpg

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