Automated Ultrasonic Testing of Materials based on C-scan Flaw Classification

Abstract

The analysis of the data in non-destructive ultrasonic testing of materials is a very time-intensive task. To alleviate the aforementioned strain on the human expert inspectors, a plethora of assisted analysis methods based on deep learning have been developed recently. However, most of these methods are based on the automated detection of flaws in A-scans and B-scans and therefore we propose a method based on the detection of flaws in C-scans that can reduce the complexity of manual detection of flaws in B-scans. The proposed method classifies each row of the C-scan based on whether it contains any flaws or not. Afterward, the positively classified rows are forwarded for further automated (and manual) inspection. The results show that the developed method significantly reduces the number of B-scans that have to be further analyzed.