通过基于反向复合高斯-牛顿的数字图像相关实现高角度分辨率 EBSD 的新水平。
New levels of high angular resolution EBSD performance via inverse compositional Gauss-Newton based digital image correlation.
机构信息
National Institute of Aerospace, 100 Exploration Way, Hampton, VA 23666, USA.
National Air and Space Administration, Hampton, VA 23666, USA.
出版信息
Ultramicroscopy. 2018 Dec;195:85-92. doi: 10.1016/j.ultramic.2018.08.020. Epub 2018 Aug 29.
Conventional high angular resolution electron backscatter diffraction (HREBSD) uses cross-correlation to track features between diffraction patterns, which are then related to the relative elastic strain and misorientation between the diffracting volumes of material. This paper adapts inverse compositional Gauss Newton (ICGN) digital image correlation (DIC) to be compatible with HREBSD. ICGN-based works by efficiently tracking not just the shift in features, but also the change in their shape. Modeling a shape change as well as a shift results in greater accuracy. This method, ICGN-based HREBSD, is applied to a simulated data set, and its performance is compared to conventional cross-correlation HREBSD, and cross-correlation HREBSD with remapping. ICGN-based HREBSD is shown to have about half the strain error of the best cross-correlation method with a comparable computation time.
传统的高角度分辨率电子背散射衍射（HREBSD）使用互相关来跟踪衍射图案之间的特征，然后将这些特征与材料的衍射体积之间的相对弹性应变和位向差相关联。本文将基于逆组合高斯牛顿（ICGN）的数字图像相关（DIC）方法进行改编，使其与 HREBSD 兼容。基于 ICGN 的方法不仅可以有效地跟踪特征的移动，还可以跟踪其形状的变化。对形状变化和位移进行建模可以提高准确性。该方法，基于 ICGN 的 HREBSD，应用于模拟数据集，并将其性能与传统的互相关 HREBSD 以及带有重映射的互相关 HREBSD 进行了比较。结果表明，基于 ICGN 的 HREBSD 的应变误差约为最佳互相关方法的一半，而计算时间相当。
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