Identification of continuous casting round blooms shrinkage defects based on deep learning

In the continuous casting process, issues such as time lag in macroscopic inspection results and human interference factors hinder efficient quality assessment. Rapid and accurate identification of round bloom quality defects is critical for stabilising production. The study employs a U-Net neural network to preprocess image data from the continuous casting process, achieving a segmentation accuracy of 94.4% for round blooms. Metrics including precision, confusion matrix and ROC curve are used to evaluate the reliability and stability of different mathematical models in predicting defects. The convolutional neural network demonstrates superior performance with a defect identification accuracy of 97.12%, providing actionable guidance for on-site production optimisation.