Gang Sheng
Gang Sheng has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Huangyue Cai
School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
1 shared publication - Jie Lu
Shanghai Key Laboratory of advanced High-temperature Materials and precision Forming, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
1 shared publication - Junwei Zhao
Chengdu Chengfa Taida Aviation Technology Co., Ltd., Chengdu 610000, China
1 shared publication - Lirong Luo
Shanghai Key Laboratory of advanced High-temperature Materials and precision Forming, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
1 shared publication - Tianmeng Huang
Shanghai Key Laboratory of advanced High-temperature Materials and precision Forming, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
1 shared publication - Xiao Shan
Shanghai Key Laboratory of advanced High-temperature Materials and precision Forming, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
1 shared publication
Latest Publications
- Open Access
Automatic Recognition of Microstructures of Air-Plasma-Sprayed Thermal Barrier Coatings Using a Deep Convolutional Neural Network
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.
Open AccessAutomatic Recognition of Microstructures of Air-Plasma-Sprayed Thermal Barrier Coatings Using a Deep Convolutional Neural Network
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.