Non-destructive ultrasonic testing and machine learning-assisted early detection of carburizing damage in HP steel pyrolysis furnace tubes

Abstract

During the operation of HP steel furnace tubes, structural deterioration occurs due to carburization mechanisms. Herein, machine learning models were employed to detect carburization damage in furnace tubes using ultrasonic signals. The microstructural and elemental analysis revealed phases like austenite, chromium carbide, and niobium carbide. Our volumetric fraction analysis showed that the harmful chromium carbide phase increased toward the tube wall thickness. Three machine learning models, namely Gaussian Naive Bayes (GNB), Kernel Naive Bayes (KNB), and Subspace Discriminant (SD), were used to analyze the ultrasound signals. The GNB model demonstrated the highest accuracy rate (99.2%) and high sensitivity for the dataset with 26 features and a K-fold cross-validation with K value = 5, arising as the most effective classifier for detecting carburization damage in HP steel. Our results underscore the efficacy of the combined use of ultrasonic testing and machine learning for detecting carburization in HP steel furnace tubes.