Evaluating Model Robustness for Defect Identification and Classification in a Composite Aerostructure Material

Yunker, Austin; Lake, Rami; Kettimuthu, Rajkumar; Kral, Zachary

doi:10.1115/1.4065474

Evaluating Model Robustness for Defect Identification and Classification in a Composite Aerostructure Material

Journal Article · Fri Jul 26 00:00:00 EDT 2024 · Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems

DOI:https://doi.org/10.1115/1.4065474· OSTI ID:2563184

Yunker, Austin ^[1]; Lake, Rami ^[2]; Kettimuthu, Rajkumar ^[1]; Kral, Zachary ^[3]

Argonne National Laboratory (ANL), Argonne, IL (United States)
Northern Illinois University, Dekalb, IL (United States)
Spirit AeroSystems, Inc., Wichita, KS (United States)

Aircraft structures are required to have a high level of quality to satisfy their need for light weight, efficient flight, and withstanding high loads over their lifespan. These aerostructures are typically made from a composite material due to their good tensile strength and resistance to compression. To ensure their structural integrity, the composite material requires inspection for common flaws such as porosity, delaminations, voids, foreign object debris, and other defects. Ultrasonic testing (UT) is a popular non-destructive inspection (NDI) technique used for effectively evaluating the composite material. Current inspection methods rely heavily on human experience and are extremely time consuming. Therefore, there is a need for the development of techniques to reduce the manual inspection time. This work compares the performance of different deep learning-based methods in the identification and classification of defects. Deep learning has shown great promise in numerous fields, and we show its effectiveness in the evaluation of the composite aerostructure material. The methods developed here are both highly reliable with a top recall value of 98.64% as well as extremely efficient requiring an average of 4 s during the inferencing stage to evaluate new composites. Lastly, we investigate model robustness to concept drift by measuring its performance over time.

Research Organization:: Spirit AeroSystems, Inc., Wichita, KS (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE); USDOE Office of Science (SC)

Grant/Contract Number:: AC02-06CH11357; EE0009397

OSTI ID:: 2563184

Journal Information:: Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems, Journal Name: Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems Journal Issue: 1 Vol. 8; ISSN 2572-3901

Publisher:: ASMECopyright Statement

Country of Publication:: United States

Language:: English

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Related Subjects

36 MATERIALS SCIENCE
Non-destructive inspection (NDI)
composites
deep learning
defects
ultrasonic testing (UT)

Evaluating Model Robustness for Defect Identification and Classification in a Composite Aerostructure Material

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References (12)

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