鉴于传统数字图像相关(DIC)方法采用的布点方式(水平成行,垂直成列)很可能会将一些测点分布在散斑质量较差的位置,提出了一种基于测点优化、Newton-Raphson(N-R)迭代与粒子群优化(PSO)算法的DIC方法.首先,通过在原始测点周围寻找散斑质量较好的区域来优化测点位置;然后,采用基于N-R迭代与PSO的粗细搜索方法计算优化后(非均匀分布)测点的位移场;最后,采用二维格林样条插值算法对该位移场进行插值以获得原始测点处的位移场,再由中心差分方法获得应变场.对3幅散斑质量差别较大的散斑图上的测点进行了优化,并将传统方法和提出方法获得的应变的各种结果进行了比较.研究发现,当样本子区尺寸在21~41 pixel之间时,对于平均灰度梯度处于10~20 pixel-3,且预加应变量处于0.01~0.05之间的散斑图,采用该方法可以获得较好的测量结果,这与优化测点位置有关.若采用该方法仅对原始测点中分布在散斑质量较差位置处的那些测点进行优化,有望获得更为理想的测量结果.%In traditional Digital Image Correlation(DIC)methods, points are regularly distributed, that is, points are parallel in horizontal and vertical directions. Thus, a few points can be distributed in positions where speckle qualities are bad. In this paper, a digital image correlation method based on the optimization of positions of points, Newton-Raphson(N-R) iteration and particle swarm optimization is proposed. Firstly, the positions of original points are optimized; secondly, the displacement field for the irregular optimal points is obtained by use of the N-R iteration and PSO;finally, the displacement field is interpolated using two-dimensional Green's function-based interpolator to obtain the displacement field at regularity distributed points, and then, the strain field is obtained by use of the central difference method. For three speckle images where speckle qualities are greatly different, positions of points are optimized, and comparisons between the present and traditional results are conducted. It is found that the present method has reasonable accuracy for speckle images with the strain of 0.01~0.05, the size of subsets of 21~41 pixel, and the mean intensity gradient of 10~20 pixel-3, which is related to the point optimization. It is expected that high accurate results can be obtained using the present method if only the positions of points distributed at positions with bad speckle qualities are optimized.
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