Predicting Local Material Thickness from Steady-State Ultrasonic Wavefield Measurements Using a Convolutional Neural Network

Journal Article · Mon Jan 17 00:00:00 EST 2022 · Ultrasonics

DOI:https://doi.org/10.1016/j.ultras.2021.106661· OSTI ID:1841932

Eckels, Joshua David ^[1]; Jacobson, Erica Marie ^[1]; Cummings, Ian Thomas ^[1]; Fernandez, Isabel Faith ^[1]; Ho, Kelly ^[1]; Dervilis, Nikolaos ^[2]; Flynn, Eric Brian ^[1]; Wachtor, Adam Joseph ^[1]

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1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2. Univ. of Sheffield (United Kingdom); Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Acoustic steady-state excitation spatial spectroscopy (ASSESS) is a full-field, ultrasonic non-destructive evaluation (NDE) technique used to locate and characterize defects in plate-like structures. ASSESS generates a steady-state, single-tone ultrasonic excitation in a structure and a scanning laser Doppler Vibrometer (LDV) measures the resulting full-field surface velocity response. Traditional processing techniques for ASSESS data rely on wavenumber domain analysis. This paper presents the alternative use of a convolutional neural network (CNN), trained using simulated ASSESS data, to predict the local plate thickness at every pixel in the wavefield measurement directly. The defect detection accuracy of CNN-based thickness predictions are shown to improve for defects of greater size, and for defects with higher thickness reductions. The CNN demonstrates the ability to predict thickness accurately in regions where Lamb wave dispersion relations are complex or unknown, such as near the boundaries of a test specimen, so long as the CNN is trained on data that accounts for these regions. The CNN also shows generalizability to ASSESS experimental data, despite an entirely simulated training dataset.

• Research Organization: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: USDOE
• Grant/Contract Number: 89233218CNA000001
• OSTI ID: 1841932
• Report Number(s): LA-UR--21-21310
• Journal Information: Ultrasonics, Journal Name: Ultrasonics Vol. 123; ISSN 0041-624X
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY
acoustic steady-state excitation spatial spectroscopy
convolutional neural network
image segmentation
ultrasonic measurement