Digital Image Correlation (DIC) is a length scale independent surface pattern matching and tracking algorithm capable of providing full field deformation measurements. The confident registration of this pattern within the imaging system becomes key to the derived results. Practically, conventional speckling methods use non-reliable, non-repeatable patterning methodologies including spray paints and air brushing leading to increased systematic and random errors based on the user’s experience. A methodology to develop a speckle pattern tailored to the imaging and experimental conditions of the given system is developed in this paper. In this context, a novel bio-inspired speckle pattern development technique is introduced, leveraging spatial imaging parameters in addition to frequency characteristics of speckle patterns, enhancing the results obtained through DIC. This novel technique leverages gradient parameters in the frequency spectrum obtained from patterns fabricated using a bio-templating manufacturing technique. The analysis presented shows that optimized gradient features alongside tailored spatial characteristics reduce errors while increasing the usefulness of DIC results across the entire region of interest. With this new approach, gradient information is derived from the bio-templated pattern, extracted, optimized and then convolved with spatial properties of a numerically generated 2D point clouds which can then be transferred onto actual specimens. Numerical error analysis shows that the optimized patterns result in significant reduction in root mean square error compared to conventional speckling methods. Physical experiments show the scalability of this optimized pattern to allow for varying working distances while consistently maintaining a lower error threshold compared to conventional speckling techniques.

中文翻译：

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一种基于仿生频率的可定制和可扩展散斑图案生成方法

数字图像相关 (DIC) 是一种与长度尺度无关的表面图案匹配和跟踪算法，能够提供全场变形测量。这种模式在成像系统内的可靠配准成为得出结果的关键。实际上，传统的散斑方法使用不可靠、不可重复的图案化方法，包括喷漆和气刷，导致基于用户体验的系统和随机误差增加。本文开发了一种开发适合给定系统的成像和实验条件的散斑图案的方法。在此背景下，引入了一种新颖的仿生散斑图案开发技术，除了散斑图案的频率特性外，还利用空间成像参数，增强通过 DIC 获得的结果。这种新技术利用了从使用生物模板制造技术制造的图案获得的频谱中的梯度参数。所呈现的分析表明，优化的梯度特征与定制的空间特征一起减少了错误，同时提高了整个感兴趣区域的 DIC 结果的有用性。使用这种新方法，梯度信息从生物模板模式中提取、提取、优化，然后与数字生成的 2D 点云的空间特性进行卷积，然后可以将其转移到实际样本上。数值误差分析表明，与传统散斑方法相比，优化后的图案显着降低了均方根误差。