1. Automatic Recognition of Microstructures of Air-Plasma-Sprayed Thermal Barrier Coatings Using a Deep Convolutional Neural Network.
- Author
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Shan, Xiao, Huang, Tianmeng, Luo, Lirong, Lu, Jie, Cai, Huangyue, Zhao, Junwei, Sheng, Gang, and Zhao, Xiaofeng
- Subjects
THERMAL barrier coatings ,CONVOLUTIONAL neural networks ,MICROSTRUCTURE ,SCANNING electron microscopy - Abstract
Either to obtain desirable microstructures by adjusting processing parameters or to predict the properties of a thermal barrier coating (TBC) according to its microstructure, fast and reliable quantitation of the microstructure is imperative. In this research, a machine-learning-based approach—a deep convolution neural network (DCNN)—was established to accurately quantify the microstructure of air-plasma-sprayed (APS) TBCs based on 2D images. Four scanning electron microscopy (SEM) images (view field: 150 μm × 150 μm, image size: 3072 pixel × 3072 pixel) were taken and labeled to train the DCNN. After training, the DCNN could recognize correctly 98.5% of the pixels in the SEM images of typical APS TBCs. This study demonstrated that a small dataset of SEM images could be enough to train a DCNN, making it a powerful and feasible method for quantitively characterizing the microstructure osf APS TBCs. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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