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 and Ben Zimmerman
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Yongjin Lu: Department of Mathematics and Economics, Virginia State University, Petersburg, USA
Wei-Bang Chen: Department of Engineering and Computer Science, Virginia State University, Petersburg, USA
Xiaoliang Wang: Department of Applied Engineering Technology, Virginia State University, Petersburg, USA
Zanyah Ailsworth: Department of Engineering and Computer Science, Virginia State University, Petersburg, USA
Melissa Tsui: Commonwealth Center for Advanced Manufacturing, USA
Huda Al-Ghaib: Commonwealth Center for Advanced Manufacturing, USA
Ben Zimmerman: Commonwealth Center for Advanced Manufacturing, USA

International Journal of Multimedia Data Engineering and Management (IJMDEM), 2020, vol. 11, issue 3, 20-35

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.

Date: 2020
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