Comparative Study on Deep Learning Methods for Defect Identification and Classification in Composite Aerostructure Material
- Argonne National Laboratory (ANL), Argonne, IL (United States); Spirit AeroSystems Inc
- Northern Illinois University, Dekalb, IL (United States)
- Argonne National Laboratory (ANL), Argonne, 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 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 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 composite aerostructure material. Furthermore, the methods developed here are both highly reliable with a top recall value of 98.63% as well as extremely efficient requiring an average of 4 seconds during the inferencing stage to evaluate new composites.
- 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)
- DOE Contract Number:
- EE0009397; AC02-06CH11357
- OSTI ID:
- 2562929
- Journal Information:
- 2023 50th Annual Review of Progress in Quantitative Nondestructive Evaluation, Journal Name: 2023 50th Annual Review of Progress in Quantitative Nondestructive Evaluation
- Country of Publication:
- United States
- Language:
- English
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