Advances of Machine Learning in Phased Array Ultrasonic Non-Destructive Testing: A Review

作者：Yiming Na, Yunze He, Baoyuan Deng, Xinchao Lu, Hongjin Wang, Liwen Wang, Yi Cao · 发表于：AI · 年份：2025 · DOI：10.3390/ai6060124 · 被引用次数：18 · 研究领域：Ultrasonics and Acoustic Wave Propagation、Non-Destructive Testing Techniques、Flow Measurement and Analysis

Recent advancements in machine learning (ML) have led to state-of-the-art performance in various domain-specific tasks, driving increasing interest in its application to non-destructive testing (NDT). Among NDT techniques, phased array ultrasonic testing (PAUT) is an advanced extension of conventional ultrasonic testing (UT). This article provides an overview of recent research advances in ML applied to PAUT, covering key applications such as phased array ultrasonic imaging, defect detection and characterization, and data generation, with a focus on multimodal data processing and multidimensional modeling. The challenges and pathways for integrating the two techniques are examined. Finally, the article discusses the limitations of current methodologies and outlines future research directions toward more accurate, interpretable, and efficient ML-powered PAUT solutions.