Abstract
High chromium martensitic heat-resistant steel is considered as a candidate material for pressure components of the next generation of incinerators of the subcritical level or above in China due to its excellent high-temperature and corrosion resistance, but in the long-term service, aging will significantly affect the service safety of materials. So, accurate identification of its aging state is important to enhance the safety of a power plant. In this paper, an automatic aging grading model of high chromium martensite heat-resistant steel based on the depth residual network is proposed according to different scales of metallographic data. A multiscale data set is constructed by image reduction to verify the accuracy of the model in identifying microstructure images of high chromium martensitic heat-resistant steel with different scales. The experimental results show that the model using multiscale data sets performs well, and then, through feature pyramid network model training, the accuracy rate is further improved, and a relatively good prediction accuracy model is obtained. The validity of the deep learning method for the classification of damage and aging of P91 steel with different scales is verified.