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py2DIC：实验力学领域中用于位移和应变测量的全新免费开源软件。

py2DIC: A New Free and Open Source Software for Displacement and Strain Measurements in the Field of Experimental Mechanics.

机构信息

Geodesy and Geomatics Division, DICEA, Sapienza University of Rome, 00184 Rome, Italy.

Geoinformatics Division, Department of Urban Planning and Environment, KTH Royal Institute of Technology, 10044 Stockholm, Sweden.

出版信息

Sensors (Basel). 2019 Sep 5;19(18):3832. doi: 10.3390/s19183832.

DOI:10.3390/s19183832
PMID:31491860
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6766978/
Abstract

Thanks to the advances in computer power, memory storage and the availability of low-cost and high resolution digital cameras, Digital Image Correlation (DIC) is currently one of the most used optical and non-contact techniques for measuring material deformations. A free and open source 2D DIC software, named , was developed at the Geodesy and Geomatics Division of the Sapienza University of Rome. Implemented in Python, the software is based on the template matching method and computes the 2D displacements and strains of samples subjected to mechanical loading. In this work, the potentialities of were evaluated by processing two different sets of experimental data and comparing the results with other three well known DIC software packages , and . Moreover, an accuracy assessment was performed comparing the results with the values independently measured by a strain gauge fixed on one of the samples. The results demonstrate the possibility of successfully characterizing the deformation mechanism of the investigated materials, highlighting the pros and cons of each software package.

摘要

得益于计算机能力、内存存储和低成本、高分辨率数码相机的普及，数字图像相关（DIC）技术目前是最常用的光学和非接触式测量材料变形的技术之一。在罗马萨皮恩扎大学的大地测量和测绘部门开发了一款免费的开源 2D DIC 软件，名为。该软件采用 Python 实现，基于模板匹配方法，可计算在机械加载下的样本的 2D 位移和应变。在这项工作中，通过处理两组不同的实验数据和与其他三个知名的 DIC 软件包（、和）的结果进行比较，评估了的潜力。此外，通过与固定在其中一个样本上的应变计独立测量的值进行比较，进行了准确性评估。结果表明，成功地描述了所研究材料的变形机制的可能性，突出了每个软件包的优缺点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/779b/6766978/331eca311ccc/sensors-19-03832-g015.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/779b/6766978/71b134a91343/sensors-19-03832-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/779b/6766978/9d93b3ba3257/sensors-19-03832-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/779b/6766978/af5889914916/sensors-19-03832-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/779b/6766978/a6b1c9ec953e/sensors-19-03832-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/779b/6766978/d10b4b252e35/sensors-19-03832-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/779b/6766978/5faa8218519b/sensors-19-03832-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/779b/6766978/0c1f8219a148/sensors-19-03832-g012.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/779b/6766978/331eca311ccc/sensors-19-03832-g015.jpg

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