Abstract：The sorbite content of highcarbon wire rod steel is one of important indexes for evaluating its performance. The current test methods have some disadvantages, for example, the recognition accuracy is poor, and the detection results are easily affected by the inspectors. A material library for neural network learning was established through sample preparation standardization, material collection, material setting, material labeling and so on. The computer artificial intelligence and deep neural network technology were used to initialize the recognition model. The unlabeled material was used to test and interactively optimize the initialization model. Finally, it proved that the recognition model had high accuracy and fast detection speed. In other words, the recognition model was feasible. The successful implementation of sorbite recognition based on artificial intelligence also provided useful practice for the intelligent recognition of other metallographic testing items such as grain size classification, decarburization layer identification, nonmetallic inclusion identification, and band structure classification.
罗新中，肖命冬，张兆洋，李富强，朱祥睿. 基于人工智能高碳盘条钢索氏体识别探讨[J]. 物理测试, 2021, 39(3): 34-.
LUO Xinzhong,XIAO Mingdong,ZHANG Zhaoyang,LI Fuqiang,ZHU Xiangrui. Recognition discussion about sorbite in highcarbon wire rod steel based on artificial intelligence. PHYSICS EXAMINATION AND TESTING, 2021, 39(3): 34-.
2021, Vol. 39 
