Abstract
This paper proposes a metallic fracture estimation method that combines digital image correlation and convolutional neural networks, based on a proven theory that the strain distribution of a component changes when a crack occurs in a structure. By using digital image correlation, the method achieves noncontact and nondestructive sensing, as well as high interference immunity. We utilize a digital image correlation system to produce strain distribution graphs that reflect occurrences and propagations of fractures during fatigue processes. A deep residual network (ResNet) regression model is trained by correlating strain distribution graphs with the corresponding fracture lengths, so that the fracture propagation condition can be estimated by data from digital image correlation. In the experiment, according to the American Society for Testing Materials (ASTM) standards, we fabricate a set of aluminum specimens and perform fatigue tests with data acquisition by digital image correlation. Finally, we obtain a crack length estimation mean absolute error of 0.0077 mm, or 0.26% of the measuring range. The results show the precision, as well as the practicality, of the proposed method.