Cite
Advancing Carbon Fiber Composite Inspection: Deep Learning-Enabled Defect Localization and Sizing via 3-D U-Net Segmentation of Ultrasonic Data
MLA
McKnight, Shaun, et al. “Advancing Carbon Fiber Composite Inspection: Deep Learning-Enabled Defect Localization and Sizing via 3-D U-Net Segmentation of Ultrasonic Data.” IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, vol. 71, no. 9, Sept. 2024, pp. 1106–19. EBSCOhost, https://doi.org/10.1109/TUFFC.2024.3408314.
APA
McKnight, S., Tunukovic, V., Gareth Pierce, S., Mohseni, E., Pyle, R., MacLeod, C. N., & O’Hare, T. (2024). Advancing Carbon Fiber Composite Inspection: Deep Learning-Enabled Defect Localization and Sizing via 3-D U-Net Segmentation of Ultrasonic Data. IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, 71(9), 1106–1119. https://doi.org/10.1109/TUFFC.2024.3408314
Chicago
McKnight, Shaun, Vedran Tunukovic, S. Gareth Pierce, Ehsan Mohseni, Richard Pyle, Charles N. MacLeod, and Tom O’Hare. 2024. “Advancing Carbon Fiber Composite Inspection: Deep Learning-Enabled Defect Localization and Sizing via 3-D U-Net Segmentation of Ultrasonic Data.” IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control 71 (9): 1106–19. doi:10.1109/TUFFC.2024.3408314.