Research on Metallographic Structure Rating Method for Steel Used in Thermal Power Units Based on Deep Learning
In response to the issues of susceptibility to human factors,low efficiency,and poor repeatability in metallographic structure rating,this paper uses metallographic images to establish a sample dataset,and uses ConvNeXt-T convolutional neural network model to study the deep learning based metallographic structure rating method for steel used in thermal power units.At the same time,the performance of the constructed model on the validation set is evaluated using a confusion matrix.The average accuracy rate,precision,sensitivity,specificity,and F1-Score of the model for spheroidization rating of ferrite and pearlite structure are 98.7%,97.3%,97.2%,99.1%,and 97.2%,respectively,which indicates that this method can accurately grade the metallographic structure of steel used in thermal power units,overcome human factors,improve rating efficiency,and form an objective evaluation,and provide a new method for intelligent grading of metallographic structure of steel used in thermal power units,and assist the power industry in moving towards digitalization and intelligence in metallographic examination.
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