Automatic Recognition of Microstructures of Air-Plasma-Sprayed Thermal Barrier Coatings Using a Deep Convolutional Neural Network
Source abstract
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.
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Research context
OpenAlex topics: High-Temperature Coating Behaviors · Advanced materials and composites · Nuclear Materials and Properties
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Shanghai Key Laboratory of Advanced High-Temperature Materials and Precision Forming, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Engineering Research Center of Nano-Geo Materials of Ministry Education, Faculty of Materials Science and Chemistry, China University of Geosciences, Wuhan 430074, China
Chengdu Chengfa Taida Aviation Technology Co., Ltd., Chengdu 610000, China
Funding
National Natural Science Foundation of China
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