Automatic Recognition of Microstructures of Air-Plasma-Sprayed Thermal Barrier Coatings Using a Deep Convolutional Neural Network

نویسندگان

چکیده

Either to obtain desirable microstructures by adjusting processing parameters or predict the properties of a thermal barrier coating (TBC) according its microstructure, fast and reliable quantitation microstructure is imperative. In this research, machine-learning-based approach—a deep convolution neural network (DCNN)—was established accurately quantify air-plasma-sprayed (APS) TBCs based on 2D images. Four scanning electron microscopy (SEM) images (view field: 150 μm × μm, image size: 3072 pixel pixel) were taken labeled train DCNN. After training, DCNN could recognize correctly 98.5% pixels in SEM typical APS TBCs. This study demonstrated that small dataset be enough DCNN, making it powerful feasible method for quantitively characterizing osf

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ژورنال

عنوان ژورنال: Coatings

سال: 2022

ISSN: ['2079-6412']

DOI: https://doi.org/10.3390/coatings13010029