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Automatic Nondestructive Detection of Damages in Thermal Barrier Coatings Using Image Processing and Machine Learning

Published online by Cambridge University Press:  22 July 2022

Andrew Sprague ,
Pouya Tavousi ,
Sina Shahbazmohamadi  and
Zahra Shahbazi
Andrew Sprague
Affiliation:
Department of Mechanical Engineering, Manhattan College, Bronx, NY, United States
Pouya Tavousi
Affiliation:
Department of Biomedical Engineering, University of Connecticut, Storrs, CT, United States
Sina Shahbazmohamadi
Affiliation:
Department of Biomedical Engineering, University of Connecticut, Storrs, CT, United States
Zahra Shahbazi*
Affiliation:
Department of Mechanical Engineering, Manhattan College, Bronx, NY, United States
*
*Corresponding Author: Zahra Shahbazi

Abstract

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Type
Artificial Intelligence, Instrument Automation, And High-dimensional Data Analytics for Microscopy and Microanalysis
Information
Microscopy and Microanalysis , Volume 28 , Supplement S1 , August 2022 , pp. 3068 - 3072
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Copyright
Copyright © Microscopy Society of America 2022

References

Ahmadian, S., et al. “Three-dimensional X-ray micro-computed tomography of cracks in a furnace cycled air plasma sprayed thermal barrier coating”. (2014)10.1016/j.scriptamat.2014.10.026CrossRefGoogle Scholar
Kastner, J., Heinzl, C. “X-ray Computed Tomography for Non-destructive Testing and Materials Characterization”. (2015)10.1007/978-1-4471-6741-9_8CrossRefGoogle Scholar
Ahmadi, B., et al. “Non-destructive Automatic Die-Level Defect Detection of Counterfeit Microelectronics using Machine Vision.”Google Scholar
About Deep Learning. Dragonfly Deep Learning | About Deep Learning | ORS. (n.d.). Retrieved October 5, 2021, from https://www.theobjects.com/dragonfly/deep-learning.html.Google Scholar