Controlled Poisson Voronoi tessellation for virtual grain structure generation: a statistical evaluation
Controlled Poisson Voronoi tessellation for virtual grain structure generation: a statistical evaluation
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DOI：
10.1080/14786435.2011.613860
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发表时间：
2011-11
影响因子：
1.6
通讯作者：
P. Zhang;D. Balint;Jianguo Lin
中科院分区：
文献类型：
--
作者：
P. Zhang;D. Balint;Jianguo Lin
发展了一种受控Poisson Voronoi镶嵌(CPVT)模型，用于产生在镶嵌规则性和晶粒度分布方面与多晶材料的金相观察结果在统计上等价的二维虚拟晶格结构。对将粒度分布参数与规则性参数联系起来至关重要的描述性拟合模型进行了改进；以前的描述性模型对于较大的分布参数c值很差。在唯一确定粒度分布特性和配置CPVT系统时涉及一组四个物理参数。重点考察了CPVT系统在生成具有特定性质的虚拟颗粒结构方面的有效性和稳健性。进行了两个系列的统计检验，以验证规定的规律性与所产生的镶嵌的规律性之间的一致性，并调查总体粒度分布的细节。为了探索样本量效应，对一系列规则性值进行了三次统计检验。此外，还介绍了一个用于晶体塑性有限元分析的材料模拟系统，该系统实现了用于晶体组织生成的CPVT模型。两幅具有不同粒度分布特征的真实显微图像被用来检验该系统生成与物理测量相匹配的虚拟颗粒结构的能力。
A controlled Poisson Voronoi tessellation (CPVT) model has been developed for producing two-dimensional virtual grain structures that are statistically equivalent to metallographic observations of polycrystalline materials in terms of the tessellation's regularity and grain size distribution. The descriptive fitting model, which is critical to link the grain size distribution parameter to the regularity parameter, has been improved in this work; previously, the descriptive model was poor for large values of the distribution parameter c. A set of four physical parameters is involved in uniquely determining the grain size distribution properties and configuring the CPVT system. Emphasis is devoted to examining the effectiveness and robustness of the CPVT system in generating virtual grain structures with specified properties. Two series of statistical tests are performed to validate the agreement between the prescribed regularity and that of the resultant tessellations, and to investigate the details of the overall grain size distribution. In order to explore sample size effects, three statistical tests were conducted for a range of regularity values. In addition, a materials modelling system for crystal plasticity finite element analysis is demonstrated, which implements the CPVT model for grain structure generation. Two real microscopic images with different grain size distribution features are employed to examine the capability of the system to generate virtual grain structures that match physical measurements.