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DVC中内部散斑质量评价及计算体素点的优化选择

邹翔 张轩豪 王延珺 潘兵

邹翔, 张轩豪, 王延珺, 潘兵. DVC中内部散斑质量评价及计算体素点的优化选择. 力学学报, 2021, 53(7): 1-10 doi: 10.6052/0459-1879-21-158
引用本文: 邹翔, 张轩豪, 王延珺, 潘兵. DVC中内部散斑质量评价及计算体素点的优化选择. 力学学报, 2021, 53(7): 1-10 doi: 10.6052/0459-1879-21-158
Zou Xiang, Zhang Xuanhao, Wang Yanjun, Pan Bing. Internal speckle pattern quality assessment and optimal selection of voxel points for digital volume correlation. Chinese Journal of Theoretical and Applied Mechanics, 2021, 53(7): 1-10 doi: 10.6052/0459-1879-21-158
Citation: Zou Xiang, Zhang Xuanhao, Wang Yanjun, Pan Bing. Internal speckle pattern quality assessment and optimal selection of voxel points for digital volume correlation. Chinese Journal of Theoretical and Applied Mechanics, 2021, 53(7): 1-10 doi: 10.6052/0459-1879-21-158

DVC中内部散斑质量评价及计算体素点的优化选择

doi: 10.6052/0459-1879-21-158
基金项目: 国家自然科学基金资助项目(11925202, 11872009, 11762009)
详细信息
    作者简介:

    潘兵, 教授, 主要研究方向: 光测力学方法及其应用. E-mail: panb@buaa.edu.cn

  • 中图分类号: O348.1

INTERNAL SPECKLE PATTERN QUALITY ASSESSMENT AND OPTIMAL SELECTION OF VOXEL POINTS FOR DIGITAL VOLUME CORRELATION

  • 摘要: 数字体图像相关方法(digital volume correlation, DVC)是一种可测量物体内部三维全场变形的先进实验力学测试技术, 通过分析由体图像成像设备(如X-ray CT)获取的物体变形前后的三维体图像, DVC可获得物体内部具有亚体素精度的三维变形信息. 在应用DVC测量内部变形时, 被测试样体图像的内部散斑质量对其测量精度有着重要影响. 本文从DVC算法位移测量误差的理论分析和数值模拟实验两方面证实了DVC的位移测量误差与计算子体块的灰度梯度平方和(sum of square subvolume intensity gradient, SSSIG)值呈负相关关系, 即: 计算子体块的SSSIG值越大, 其位移测量精度越高, 因此SSSIG可用于体图像内部散斑质量的定量评价. 尽管直接增加计算子体块尺寸可以增加SSSIG, 但是较大计算子体块内更多的计算点会导致计算量的显著增加. 为此, 本文进一步提出一种计算体素点优化选择方法, 该方法通过将计算子体块中灰度梯度较小的体素点剔除出计算, 以实现在增大计算子体块尺寸的同时不会显著增加计算量. 模拟和真实实验结果显示了该计算体素点优化选择方法的有效性.

     

  • 图  1  3D IC-GN算法计算流程图[22]

    Figure  1.  Flow chart of the 3D IC-GN algorithm[22]

    图  2  3种不同类型内部散斑的切片图、VOI及其灰度分布直方图

    Figure  2.  The slice figures, VOI and histograms of three speckle patterns

    图  3  3种不同类型内部散斑测量结果

    Figure  3.  Measurement results of three speckle patterns

    图  4  多孔材料灰度梯度分布图

    Figure  4.  Intensity gradient distribution of porous materials

    图  5  体素点优化选择方法计算流程图

    Figure  5.  Calculation flow chart of voxel selection DVC method

    图  6  两种方法测量结果对比

    Figure  6.  Comparison of the results of two methods

    图  7  实验设置

    Figure  7.  Compression experimental setup

    图  8  重扫描实验结果

    Figure  8.  Rescan results

    表  1  重扫描实验位移测量结果对比

    Table  1.   Comparison of displacement measurement results in rescan experiment

    MethodNumber of points
    (subvolume)
    Mean bias error ±SD error/voxels
    uvw
    regular DVC68921−0.0519±0.0706−0.3498±0.0642−0.5477±0.0718
    voxel selection DVC63514−0.0526±0.0545−0.3569±0.0464−0.5498±0.0513
    下载: 导出CSV
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    (Su Yong. Quality assessment of speckle patterns in digital image correlation.[PhD Thesis]. Hefei: University of Science and Technology in China, 2016 (in Chinese))
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    [23] Pan B, Wang B. Digital image correlation with enhanced accuracy and efficiency: A comparison of two subpixel registration algorithms. Experimental Mechanics, 2016, 56(8): 1395-1409 doi: 10.1007/s11340-016-0180-z
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    [29] Wang B, Pan B. Self-adaptive digital volume correlation for unknown deformation fields. Experimental Mechanics, 2018, 59(3): 149-162
    [30] Mao LT, Chiang F. 3D strain mapping in rocks using digital volumetric speckle photography technique. Acta Mechanica, 2016, 227(11): 3069-3085 doi: 10.1007/s00707-015-1531-z
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出版历程
  • 收稿日期:  2021-04-16
  • 录用日期:  2021-06-15
  • 网络出版日期:  2021-06-15

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