Frequency-Optimized Ultrasonic and Machine Learning Framework for Early Detection of Carburization in HP Steel Tubes

Abstract

Carburization is a critical degradation mechanism in high-performance (HP) steel furnace tubes, impairing structural integrity during prolonged high-temperature service. This study proposes a machine learning-assisted ultrasonic testing framework to classify four levels of carburization damage in Cr‒Ni‒Nb HP steel alloys. A total of 80 A-scan signals were acquired per frequency (2.25 and 5 MHz) across four distinct damage classes, with spectral features extracted via discrete cosine transform (DTC). Microstructural analysis confirmed a linear increase in the volumetric fraction of chromium carbides from 9.5% (SP01, low carburization) to 40.5% (SP04, severe carburization). Among the classifiers evaluated, the K-Nearest Neighbors (KNN) and Quadratic Support Vector Machine (QSVM) achieved 100% accuracy (AUC = 1.00) at 2.25 MHz for advanced damage levels. However, early-stage detection remained challenging, with GNB attaining only 83.1% accuracy and AUC = 0.91 for SP01. Classification performance deteriorated significantly at 5 MHz due to increased signal attenuation and noise, with accuracy falling to 47.3–53.5%. These findings underscore the influence of ultrasonic frequency on damage detectability and model reliability. The integration of frequency-optimized ultrasonic inspection with machine learning delivers a scalable approach for real-time, non-destructive monitoring of carburization in industrial HP steel components, offering critical insights for predictive maintenance and structural health assessment.