Paper
9 March 2014 3D assisted defect recognition for the ultrasonic waveform inspection of titanium components
Andrew F. Ferro, Patrick J. Howard
Author Affiliations +
Abstract
Titanium aircraft engine forgings are inspected at all material depths using the ultrasonic testing (UT) process called multizone inspection. This process creates a raster array of 1D A-Scan signals that image the material’s underlying characteristics, which in the case of forged titanium, result in highly variable noise. The 3D volume is analyzed as a stack of 2D depth slices that are gated into C-Scans. The intention is to accept or reject the part by examining for the presence of small anomalies in these images in terms of their signal to noise ratio (SNR). Ultrasonic testing inspectors determine that rejection decision by using specialized image software and a 2D assisted defect recognition (ADR) algorithm. This approach for calculating SNR greatly reduces inspector subjectivity compared to a manual approach, but in 2D, not all available information is used to determine a true detection. Enabling ADR to use the 3D volume for the forging inspection, an automated SNR calculation has been extended to address these issues. The 3D ADR algorithm’s life cycle from design to validation will be discussed. Validation testing results have shown that 3D ADR significantly increases probability of detection compared to 2D ADR while also reducing its false positive rate.
© (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Andrew F. Ferro and Patrick J. Howard "3D assisted defect recognition for the ultrasonic waveform inspection of titanium components", Proc. SPIE 9063, Nondestructive Characterization for Composite Materials, Aerospace Engineering, Civil Infrastructure, and Homeland Security 2014, 906305 (9 March 2014); https://doi.org/10.1117/12.2045263
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KEYWORDS
Inspection

Signal to noise ratio

Titanium

Ultrasonics

Detection and tracking algorithms

Image processing

Signal processing

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