Automated Ultrasound Data Processing for Defect Detection and Characterization Through Machine Learning

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This project will explore the implementation of automated PAUT data interpretation for CFRPs through two approaches: I) developing a low latency Deep Neural Network (DNN) to analyze the A-scan data on the fly, while the scan is being performed, for geometrical feature recognition, automated gating of time series, and defect detection, and II) developing a Multitask Network (MN) [2] for image analysis, detection of geometrical features/defects on each B-scan, D-scan, and C-scan projection, and cross-validation of findings at the combination stage. The real-time DNN applied to the A-scan data will serve to provide warnings for defects flagged during the inspection while the MN, with a potentially improved learning through different related tasks empowered by the multi-view analysis of the data, should be able to detect the defects with higher confidence.