Document Type
Article
Publication Date
2019
Publication Source
45Th Annual Review Of Progress In Quantitative Nondestructive Evaluation
Abstract
Errors introduced into data acquired for nondestructive evaluation due to suboptimal digitization rates, bandwidth, and signal processing settings can dominate the perceived noise in acquired data, leading to artifacts and erroneous interpretation. Furthermore, the presence of such errors incurred through the data acquisition process can also inhibit post-processing techniques utilized in multimodal data segmentation and registration efforts. This study illustrates the use of advanced signal processing techniques to limit the effects of quantization errors in normal-incidence ultrasonic inspection data, thereby optimizing the signals for further processing while maintaining the integrity of the data. In conjunction with signal processing methods, K-means and Expectation-Maximization algorithms are investigated for applications in automated data segmentation and multimodal spatial registration. Using results from segmented and registered data, techniques in constructing computer aided design (CAD) models are investigated for importing measured material property and flaw information into various modeling software platforms.
ISBN/ISSN
0094-243X
Document Version
Published Version
Publisher
AIP Publishing
Volume
2102
Issue
38
Peer Reviewed
yes
Sponsoring Agency
QNDE Programs, Ctr Nondestruct Evaluat
eCommons Citation
Dierken, Josiah; Sparkman, Daniel; Donegan, Sean; Wallentine, Sarah; Wertz, John; and Zainey, David, "Application of Signal Processing and Machine Learning Techniques for Segmentation and Spatial Registration of Material Property Data" (2019). Office for Research Publications and Presentations. 61.
https://ecommons.udayton.edu/ofr_pub/61
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.org10.1063/1.5099757