Enhanced Ultrasonic Flaw Detection using an Ultra-high Gain and Time-dependent Threshold.

In an attempt to improve the ultrasonic testing capability of a conventional C-scan system, a flaw detection method using an ultra-high gain is developed in this paper. A time-dependent threshold for image segmentation is applied to identify automatically material anomalies present in the sample. A singly-scattered response (SSR) model is used with extreme value statistics to calculate the confidence bounds of grain noise. The result is a time-dependent threshold associated with the grain noise that can be used for segmentation. Ultrasonic imaging experiments show that the presented method has advantages over a traditional fixed threshold approach with respect to false positives and missed flaws. The results also show that a low gain is adverse to the detection of micro-flaws with subwavelength dimensions. The forward model is expected to serve as an effective tool for the probability of detection (POD) of flaws and the inspection of coarse-grained materials in the future.