With the increased availability of digital data from nondestructive evaluation (NDE) systems, there is a natural inquisitiveness to explore the use of statistical regression and classification methods for NDE data. A continuous issue for Artificial Intelligence/Machine Learning (AI/ML) methods is a question of how much data is required to enable training and how high of fidelity is required for such training. The challenge of relevant NDE-based data for aviation applications is not trivial. There are limited data sets as typical areas with flaws, such as fatigue cracks or corrosion, are repaired as soon as they are detected. Another challenge with USAF specific aviation NDE data is the broad range of variables that affect the data. To address the limitations of available data, the approach taken by the US Air Force (USAF) NDE community is to integrate attributes of AI/ML with other algorithms for analysis of NDE data, plus integrating human analysis into the final decision making process. The combination of both statistical analysis of data combined with human analysis to determine if flaws are present has been named Intelligence Augmentation (IA). The USAF has a rich history of using IA to analyze large NDE data sets, typically acquired from inspections that use automated scanning to acquire data. USAF research continues in the area of IA for various applications. Future opportunities will include improved integration of models, especially as a function of their maturity through validation.
1. “IRS announces transition away from use of third-party verification involving facial recognition,” IRS News Release available at: https://www.irs.gov/newsroom/irs-announces-transition-away-from-use-of-third-party-verification-involving-facial-recognition
2. J. C. Aldrin, J. D. Achenbach, G. Andrew, C. P’an, B. Grills, R. T. Mullis, F. Spencer, and M. Golis, “Case Study for the Implementation of an Automated Ultrasonic Technique to Detect Fatigue Cracks in Aircraft Weep Holes,” Materials Evaluation, 59, n 11, p.1313-1319, (2001).
3. E. A. Lindgren, J. R. Mandeville, M. J. Concordia, T. J. MacInnis, J. J. Abel, J. C. Aldrin,
F. Spencer, D. B. Fritz, P. Christiansen, R. T. Mullis, and R. Waldbusser, "Probability of Detection Results and Deployment of the Inspection of the Vertical Leg of the C-130 Center Wing Beam/Spar Cap", Proceedings of the 8th Joint DoD/FAA/NASA Conference on Aging Aircraft, (2005).
4. J. C. Aldrin, D. S. Forsyth, J. T. Welter, “Design and Demonstration of Automated Data Analysis Algorithms for Ultrasonic Inspection of Complex Composite Panels with Bonds,” 42nd Annual Review of Progress in Quantitative Nondestructive Evaluation, AIP Conf. Proc., 1706, p. 020002 (2016).
5. E.A. Lindgren and C.F. Buynak, “Nondestructive Characterization of Cracks for Accelerated Disposition,” Proceedings of the 2021 Aircraft Structural Integrity Conference (2021).
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