The railway industry faces significant challenges in the early detection and characterization of sub-millimeter surface-breaking rail fatigue cracks. Resolving crack width, depth, and complex geometries such as clustered and branched networks is critical, as these defects compromise safety and lead to costly maintenance. Conventional ultrasonic inspection methods have limited ability to distinguish closely spaced defects and near-surface cracks. Consequently, multiple cracks may be misinterpreted as a single flaw, or cracks may remain undetected before they reach a detectable size or when obscured within the same transducer field.
To address these limitations, this work proposes a contrast-enhanced ultrafast ultrasound (CEUUS) imaging framework designed to provide the high resolution necessary to distinguish individual cracks within a cluster. The CEUUS technique images ultrasound contrast agents (UCAs) infiltrated into cracks to create high-resolution images. However, both UCA characteristics and transducer frequency influence the apparent size of the UCA’s ultrasound image. Experimental measurements in steel at 5 MHz show that a target with a physical size of approximately 0.4 mm produces an apparent size much larger than its true dimensions. This severe signal broadening leads to overlaps between adjacent features, ultimately degrading the ability to distinguish true boundaries.
Accordingly, this study investigates the influence of UCA properties including type, size, and concentration in relation to excitation frequency to optimize the detectability and separability of sub-millimeter cracks. Based on this analysis, optimal UCA and transducer configurations are identified to minimize the UCA signal overlap. Furthermore, plane-wave ultrasound imaging and compounding strategies are explored to improve angular sensitivity and accuracy in complex crack configurations. Experiments on steel blocks and rail sections in laboratory and field settings are expected to confirm that optimizing these parameters enables more accurate crack severity
assessment, supporting earlier detection of critical defects and improving overall rail safety.
By systematically evaluating and optimizing UCA parameters and imaging strategies across different frequencies in steel, this work aims to enable more accurate crack severity assessment, support earlier detection of critical defects, and contribute to improving rail safety and maintenance strategies.