Machine learning techniques in ultrasonics-based defect detection and material characterization: A comprehensive review

Non-destructive evaluation (NDE) and structural health monitoring (SHM) play a critical role in ensuring the safety, reliability, and longevity of engineering structures and materials. Among the various NDE techniques, ultrasonic methods are widely regarded as the most effective for damage detection and material characterization due to their high sensitivity and versatility. However, conventional ultrasonic approaches face challenges in analyzing complex signals, limiting their accuracy and efficiency in certain applications. The advent of machine learning (ML) has revolutionized ultrasonic data analysis by utilizing advanced data mining and pattern recognition capabilities to decode intricate signal patterns. This review provides a comprehensive overview of ML techniques applied to ultrasonic-based damage detection and material characterization, including key processes such as data preprocessing and feature engineering. Emphasis is placed on case studies and real-world applications, highlighting ML's role in defect detection, localization, and material property assessment. Additionally, the paper addresses critical challenges, limitations, and future directions, offering insights into the transformative potential of ML in ultrasonic NDE and SHM.