基于改进STIT镶嵌的裂纹模式建模

IF 0.8 4区 计算机科学 Q4 IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY Image Analysis & Stereology Pub Date : 2020-04-13 DOI:10.5566/ias.2245
R. Leon, W. Nagel, J. Ohser, S. Arscott
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引用次数: 2

摘要

随机平面镶嵌是由多边形细胞的后续分裂产生的。目的是为出现在长度尺度上的裂纹模式开发参数化模型,这些模型可以在纳米技术、材料科学、软物质和地质学等领域以数量级变化。以STIT镶嵌为参考模型,并与实际裂纹模式中的现象进行比较,提出了三种STIT的修正方法。对于所有这些模型,在网络上提供了一个仿真工具,该工具也可以为镶嵌单元提供一些统计数据。该软件可以通过本文参考书目中的链接免费获得。本文包含了模拟研究的结果,表明了模型的一些基本特征。最后,考虑了一个真实断裂模式的例子,该例子是通过在弹性体材料上沉积薄金属膜获得的,并将其结果与模型的预测进行了比较。
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Modeling Crack Patterns by Modified STIT Tessellations
Random planar tessellations are presented which are generated by subsequent division of their polygonal cells. The purpose is to develop parametric models for crack patterns appearing at length scales which can change by orders of magnitude in areas such as nanotechnology, materials science, soft matter, and geology. Using the STIT tessellation as a reference model and comparing with phenomena in real crack patterns, three modifications of STIT are suggested. For all these models a simulation tool, which also yields several statistics for the tessellation cells, is provided on the web. The software is freely available via a link given in the bibliography of this article. The present paper contains results of a simulation study indicating some essential features of the models. Finally, an example of a real fracture pattern is considered which is obtained using the deposition of a thin metallic film onto an elastomer material – the results of this are compared to the predictions of the model.
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来源期刊
Image Analysis & Stereology
Image Analysis & Stereology MATERIALS SCIENCE, MULTIDISCIPLINARY-MATHEMATICS, APPLIED
CiteScore
2.00
自引率
0.00%
发文量
7
审稿时长
>12 weeks
期刊介绍: Image Analysis and Stereology is the official journal of the International Society for Stereology & Image Analysis. It promotes the exchange of scientific, technical, organizational and other information on the quantitative analysis of data having a geometrical structure, including stereology, differential geometry, image analysis, image processing, mathematical morphology, stochastic geometry, statistics, pattern recognition, and related topics. The fields of application are not restricted and range from biomedicine, materials sciences and physics to geology and geography.
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