.png){kind=icon}
返回
Optimized stereo digital image correlation setup for miniature round specimen: framework development and implementation
.png){kind=icon}
OA
AI
DOI：10.1016/j.optlaseng.2021.106555.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
For the advancement of micro- and nano-technologies, multiaxial material testing at a small length scale is imperative. A novel multiaxial miniature testing system (MMTS) is under development for testing a tubular specimen of outer diameter (OD) as small as 1 mm. Because of the small specimen size of MMTS, stereo-Digital Image Correlation (DIC) is the preferred strain measurement technique. Although theoretically, stereo-DIC is length-scale independent, the implementation of stereo-DIC, particularly for miniature testing, faces experimental setup related challenges. For this reason, although stereo-DIC is strongly recommended over 2D DIC, researchers are sometimes compelled to use the latter. It is shown in the present study that the experimental setup related difficulties, particularly for miniature round specimen testing, can be overcome by a systematic development of a mathematical framework for stereo-DIC implementation. This framework addresses all setup decisions concerning stereo-DIC implementation, such as selections of stereo angle, speckle size, camera position, camera sensor size, lens focal length, dimensions of camera and lens bodies, calibration grid size, etc., as well as stereo-DIC analysis parameters, such as subset and step size. Besides serving the need of building a stereo-DIC setup for MMTS, since the developed mathematical framework treats all stereo-DIC setup decisions as variables, it can be used to develop an optimized stereo-DIC setup for any application. Examples of two general cases are reported. Since this general framework serves as a tool to solve the stereo-DIC experimental setup related challenges, the developed framework will contribute to the wider adoption of stereo-DIC over 2D DIC.
Keyword:
Stereo digital image correlation
Miniature material testing
Optimization
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.7
论文数:
7.3K
被引数:
1.7W
机构
引用论文
.png){kind=icon}
分享收藏
查看更多内容