Research on a deep-learning-based method for assessing the metallographic structure of steel used in thermal power plants

The steel used in thermal power plants will undergo creep aging and performance degradation after long-term service in high-temperature and high-pressure environments. Regular metallographic inspection is required to ensure the safe and stable operation of the equipment. In response to the issues of low efficiency and poor repeatability in the evaluation of metallographic structure, which are easily influenced by human factors, this paper uses metallographic inspection images to establish a sample dataset and presents a deep learning-based metallographic structure evaluation method for thermal power steel based on the ConvNeXt-T convolutional neural network model. The performance of the constructed model on the validation set was evaluated using a confusion matrix. The accuracy of the model's spheroidization assessment for pearlite was 98.7%, the precision was 97.3%, the sensitivity was 97.2%, the specificity was 99.1%, and the F1-Score was 97.2%. This indicates that the method is capable of accurately assessing the metallographic structure of thermal power steel, overcoming human factors, improving rating efficiency, and forming an objective evaluation. It provides a new method for the intelligent assessment of the metallographic microstructure of thermal power steel, helping the power industry move towards digitalization and intelligence in metallographic.