Document Type
Article
Publication Date
2019
Publication Source
45th Annual Review Of Progress in Quantitative Nondestructive Evaluation, Vol 38
Abstract
Quantitative characterization of impact damage in polymer matrix composites (PMCs) with ultrasonic inspection is desired to enable improved prediction of damage evolution for lifmg of composite structures. Post-processing of single-sided pulse-echo Ultrasonic Testing (UT) data produces 2D C-scan images that indicate the presence and 2D extent of delaminations, with very high depth resolution of the first reflector, while further damage below the first reflector is hidden. X-ray Computed Tomography (XCT) characterizes internal damage with a 3D voxel-based representation. Delaminations, matrix cracks, and surface-breaking cracks can be clearly visible in some XCT reconstructions. Modern damage evolution models take as input full 3D damage after impact and predict the growth of damage after loading, and need as accurate a representation of the full 3D damage as possible. This work discusses development of an approach for full 3D damage characterization using the desirable aspects of UT and XCT data Machine learning models were developed to take as input a collection of UT pulse-echo scans of an impacted PMC panel and predict as output the results of an XCT scan in the form of a 3D voxel-based representation of damage. The models were trained on UT and XCT data from previous impacted PMC panels. The approach, including UT and XCT inspection data collection, feature extraction, training of the models, and evaluation of the models on new UT data is presented. The accuracy of the damage characterization results and challenges with this approach will be discussed.
ISBN/ISSN
0094-243X
Document Version
Published Version
Publisher
AIP Conference Proceedings
Volume
2102
Peer Reviewed
yes
Sponsoring Agency
QNDE Programs, Ctr Nondestruct Evaluat
eCommons Citation
Sparkman, Daniel; Wallentine, Sarah; Flores, Mark; Wertz, John; Welter, John; Schehl, Norman; Dierken, Josiah; Zainey, David; Aldrin, John; and Uchie, Mike, "A Supervised Learning Approach for Prediction of X-Ray Computed Tomography Data from Ultrasonic Testing Data" (2019). Office for Research Publications and Presentations. 67.
https://ecommons.udayton.edu/ofr_pub/67
COinS
Comments
This open-access article is provided for download in compliance with the publisher’s policy on self-archiving. To view the version of record, use the DOI: https://doi.org/10.1063/1.5099748