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Authors: Minghui Li (University of Glasgow) , Gordon Hayward (Alba Ultrasound) , Minghui Li (University of Glasgow) , Gordon Hayward (Alba Ultrasound)
Robust defect detection in the presence of grain noise originating from material microstructures is a challenging yet essential problem in ultrasonic non-destructive evaluation (NDE). In this paper, a novel method is proposed to suppress the gain noise and enhance the defect detection and imaging. The defect echo and grain noise are distinguished through estimating the angle of arrival (AOA) of the returned echo and evaluating the likelihood that the echo is reflected from the point where the array is focused rather than from the random reflectors like the grain boundaries. The method explicitly addresses the statistical models of the defect echoes and the spatial noise across the array aperture, estimates the AOA and the likelihood in the dimension-reduced beam-space, and determines a weighting factor based on the likelihood. The factors are then normalized and utilized to correct and weight the NDE images. Experiments on industrial samples of austenitic stainless steel are conducted with a 5MHz transducer array, and the great benefits of the method on defect detection and imaging in ultrasonic NDE are validated.
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How to Cite: Li, M. . , Hayward, G. . , Li, M. . & Hayward, G. . (2019) “Improved defect detection for ultrasonic NDE and imaging based on angle of arrival (AOA) estimation in dimension-reduced beam-space”, Review of Progress in Quantitative Nondestructive Evaluation.(0).