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Deep Learning-Based Models for Porosity Measurement in Thermal Barrier Coating Images

Deep Learning-Based Models for Porosity Measurement in Thermal Barrier Coating Images

Yongjin Lu, Wei-Bang Chen, Xiaoliang Wang, Zanyah Ailsworth, Melissa Tsui, Huda Al-Ghaib, Ben Zimmerman
Copyright: © 2020 |Volume: 11 |Issue: 3 |Pages: 16
ISSN: 1947-8534|EISSN: 1947-8542|EISBN13: 9781799805915|DOI: 10.4018/IJMDEM.2020070102
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MLA

Lu, Yongjin, et al. "Deep Learning-Based Models for Porosity Measurement in Thermal Barrier Coating Images." IJMDEM vol.11, no.3 2020: pp.20-35. http://doi.org/10.4018/IJMDEM.2020070102

APA

Lu, Y., Chen, W., Wang, X., Ailsworth, Z., Tsui, M., Al-Ghaib, H., & Zimmerman, B. (2020). Deep Learning-Based Models for Porosity Measurement in Thermal Barrier Coating Images. International Journal of Multimedia Data Engineering and Management (IJMDEM), 11(3), 20-35. http://doi.org/10.4018/IJMDEM.2020070102

Chicago

Lu, Yongjin, et al. "Deep Learning-Based Models for Porosity Measurement in Thermal Barrier Coating Images," International Journal of Multimedia Data Engineering and Management (IJMDEM) 11, no.3: 20-35. http://doi.org/10.4018/IJMDEM.2020070102

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Abstract

This work trained convolutional neural networks (CNNs) to identify microstructure characteristics and then provide a measurement of porosity in the topcoat layer (TCL) of thermal barrier coatings using digital images captured by an inverted optical microscope. Porosity in a coating is related to thermal compensation and the longevity of the parts protected by the coating. The approach employs pixel-wise classification and transfer learning accompanied by data augmentation to expedite the training process and increase classification accuracy. The authors evaluate CNN-based models globally on the entire TCL of 159 high resolution raw images of three types (Type A, B, C) that are generated from three different types of powders and exhibit different physical and visual properties. The experimental results show that the CNN-based models outperform adaptive local thresholding-based porosity measurement (ALTPM) approach that this paper proposed in the previous work by 7.76%, 10.82%, and 12.10% respectively for Type A, Type B, and Type C images in terms of the average classification accuracy.

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