Digital Image Correlation (DIC), with its advantages of full-field, non-contact and applicability to extreme environments, is a powerful deformation measurement technique to in-situ monitor the high-temperature fatigue crack propagation behavior. For the DIC-based fracture mechanics analyses of cracking, crack tip position in DIC images, which is usually determined visually and manually, plays a significant role. However, the long-term fatigue-DIC experiment at both room-and high-temperatures poses a real challenge due to the huge amount of DIC images, lower image quality at high-temperature, and even the invisibility of cracks. In this paper, a fully automated algorithm for localizing the crack tip is proposed based on the crack tip displacement field and the immune algorithm. Compared to traditional algorithms, it does not necessitate iterative initial values of crack tip location, and permits precise and fast determination of critical fracture mechanics parameters, such as the Stress Intensity Factors (SIFs). The algorithm reliability is validated by both numerical simulation and fatigue testing of nickel-based superalloy GH4169 at room temperature and 650℃. It is shown that crack tip position and SIF values can be precisely and automatically determined within 7 s per image using an ordinary low-cost computer, which significantly improves the analyzing efficiency.

中文翻译：

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在巨大的 DIC 图像中自动进行裂纹尖端定位，以原位表征高温疲劳裂纹扩展行为

数字图像相关（DIC）具有全场、非接触、适用于极端环境等优点，是现场监测高温疲劳裂纹扩展行为的强大变形测量技术。对于基于 DIC 的裂纹断裂力学分析，通常通过目视和手动确定的 DIC 图像中的裂纹尖端位置起着重要作用。然而，由于DIC图像数量巨大，高温下图像质量较低，甚至裂纹不可见，室温和高温下的长期疲劳DIC实验提出了真正的挑战。本文提出了一种基于裂纹尖端位移场和免疫算法的全自动裂纹尖端定位算法。与传统算法相比，它不需要迭代裂纹尖端位置的初始值，并且可以精确快速地确定关键断裂力学参数，例如应力强度因子（SIF）。通过数值模拟和镍基高温合金GH4169在室温和650℃下的疲劳试验验证了算法的可靠性。结果表明，使用普通的低成本计算机可以在7 s内精确地自动确定每张图像的裂纹尖端位置和SIF值，从而显着提高了分析效率。