Abstract
In this study, a processing flow model for recognizing, statistically analyzing, and assessing pitting images in 2024 aluminum alloy was established based on deep learning object detection algorithms. By analyzing the statistical outcomes, the process of pitting development can be intuitively distinguished. Furthermore, the critical size for the transition from metastable pitting to stable pitting in aluminum alloy was accurately determined. In addition, by combining X-ray computed tomography testing, the evolution of pitting in aluminum alloys and its correlation with microstructure were delineated. Integrating deep learning into analysis provides valuable insights into the behavior and mechanisms of metastable pitting.
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