文献类型：期刊文献

英文题名：Online evaluation of microscale fatigue crack propagation using a real-time digital image correlation method

作者：Shen, Zhengyu[1];Li, Peiran[1];Tan, Kai[1];Zhang, Haiyang[2];Wen, Jianfeng[3];Li, Lang[1];Wang, Chong[1];Wang, Qingyuan[1]

机构：[1]Sichuan Univ, Failure Mech & Engn Disaster Prevent & Mitigat, Key Lab Sichuan Prov, Chengdu 610207, Peoples R China;[2]Liaoning Res Ctr, Taihang Lab, Shenyang 110042, Peoples R China;[3]East China Univ Sci & Technol, Key Lab Pressure Syst & Safety, MOE, Shanghai 200237, Peoples R China

年份：2025

卷号：256

外文期刊名：MEASUREMENT

收录：;EI(收录号：20252518635841);WOS:【SCI-EXPANDED(收录号:WOS:001514084100003)】；

基金：This work was supported by the Taihang Laboratory Program (AK023) , Sichuan Province Science and Technology Support Program (24NSFJQ0169) , National Natural Science Foundation of China (12022208) and National Science and Technology Major Project (J2019-IV-0010-0078) .

语种：英文

外文关键词：Crack growth rate; In-situ experiment; Real-time crack detection; Digital image correlation; Microscale crack

摘要：To achieve quasi-real-time crack growth evaluation in in-situ fatigue tests, an automated approach is proposed that could potentially benefit failure prevention by addressing fatigue crack propagation. This method significantly reduces the manual effort required to extract geometric characteristics, such as crack paths and lengths, while enhancing the efficiency of fatigue crack studies in the early stages. The framework consists of a series of operations, including image filtering, correlation search, static feature recognition, connected domain analysis, and crack block connection, to automatically identify microscale cracks and determine growth rates in nearly real time. The influence of input identification parameters on the effectiveness of the recognition process is also examined. Additionally, the framework proves effective in identifying cracks under various complex surface conditions, demonstrating its robustness. Finally, three methods based on this framework are evaluated by comparing their results with those obtained through human labeling in reverse time order.