X-rays can provide images when an object is visibly obstructed, allowing for motion measurements via x-ray digital image correlation (DIC). However, x-ray images are path-integrated and contain data for all objects between the source and detector. If multiple objects are present in the x-ray path, conventional DIC algorithms may fail to correlate the x-ray images. A new DIC algorithm called path-integrated (PI)-DIC addresses this issue by reformulating the matching criterion for DIC to account for multiple, independently-moving objects. PI-DIC requires a set of reference x-ray images of each independent object. However, due to experimental constraints, such reference images might not be obtainable from the experiment. This work focuses on the reliability of synthetically-generated reference images, in such cases. A simplified exemplar is used for demonstration purposes, consisting of two aluminum plates with tantalum x-ray DIC patterns undergoing independent rigid translations. Synthetic reference images based on the “as-designed” DIC patterns were generated. However, PI-DIC with the synthetic images suffered some biases due to manufacturing defects of the patterns. A systematic study of seven identified defect types found that an incorrect feature diameter was the most influential defect. Synthetic images were re-generated with the corrected feature diameter, and PI-DIC errors were improved by a factor of 3-4. Final biases ranged from 0.00-0.04 px, and standard uncertainties ranged from 0.06-0.11 px. In conclusion, PI-DIC accurately measured the independent displacement of two plates from a single series of path-integrated x-ray images using synthetically-generated reference images, and the methods and conclusions derived here can be extended to more generalized cases involving stereo PI-DIC for arbitrary specimen geometry and motion. This work thus extends the application space of x-ray imaging for full-field DIC measurements of multiple surfaces or objects in extreme environments where optical DIC is not possible.

当物体明显被遮挡时，X 射线可以提供图像，从而可以通过 X 射线数字图像相关 (DIC) 进行运动测量。然而，X 射线图像是路径积分的，包含源和探测器之间所有物体的数据。如果 X 射线路径中存在多个物体，传统的 DIC 算法可能无法关联 X 射线图像。一种称为路径集成 (PI)-DIC 的新 DIC 算法通过重新制定 DIC 匹配标准来解决此问题，以考虑多个独立移动的对象。 PI-DIC 需要每个独立物体的一组参考 X 射线图像。然而，由于实验的限制，这样的参考图像可能无法从实验中获得。这项工作的重点是在这种情况下合成生成的参考图像的可靠性。一个简化的示例用于演示目的，由两块带有钽 X 射线 DIC 图案的铝板组成，进行独立的刚性平移。生成了基于“设计的”DIC 图案的合成参考图像。然而，由于图案的制造缺陷，具有合成图像的 PI-DIC 存在一些偏差。对七种已识别缺陷类型的系统研究发现，不正确的特征直径是影响最大的缺陷。使用校正后的特征直径重新生成合成图像，PI-DIC 误差改善了 3-4 倍。最终偏差范围为 0.00-0.04 像素，标准不确定性范围为 0.06-0.11 像素。总之，PI-DIC 使用合成生成的参考图像从一系列路径积分 X 射线图像中准确测量了两个板的独立位移，并且此处得出的方法和结论可以扩展到涉及立体 PI 的更一般情况-DIC 适用于任意样本几何形状和运动。因此，这项工作扩展了 X 射线成像的应用空间，可在无法进行光学 DIC 的极端环境中对多个表面或物体进行全场 DIC 测量。