論文ID: ISIJINT-2022-105
Finding the fracture initiation sites is critical for understanding the fracture mechanism and thus improving the products' quality, whereas some hard-to-detected features are easily missed in the human-effort-based characterizations with human eye and involve lots of labor force. In this study, computer-aided detection of fracture initiation sites is proposed to augment human expertise to efficiently find the fracture initiation sites, and thus to reduce the labor cost. With a deep-learning you only look once object detector, the fracture initiation sites of steel were successfully detected in this study. Furthermore, based on the trained detection model, an easy-to-use application for detecting initiation sites has been further developed, exhibiting great potential for high-efficiency detection of fracture initiation sites.
