Penetration/keyhole status prediction and model visualization based on deep learning algorithm in plasma arc welding

2021 ◽  
Author(s):  
Chuan-Bao Jia ◽  
Xin-Feng Liu ◽  
Guo-Kai Zhang ◽  
Yong Zhang ◽  
Chang-Hai Yu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Arc Welding ◽  
Learning Algorithm ◽  
Plasma Arc Welding ◽  
Plasma Arc ◽  
Deep Learning Algorithm ◽  
Model Visualization

Influence of Keyhole Mode of Plasma Arc Welding on Microstructure and Mechanical Properties of Similar Butt-Welded Incoloy 800H

2019 ◽  
Vol 45 (2) ◽  
pp. 1135-1142
Author(s):  
T. M. Harish ◽  
S. Jerome ◽  
B. Yadukrishna ◽  
Rishi S. Kumar ◽  
C. Midhun Suresh ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Arc Welding ◽  
Plasma Arc Welding ◽  
Plasma Arc ◽  
Incoloy 800H ◽  
Microstructure And Mechanical Properties

scholarly journals Study on Radar Echo-Filling in an Occlusion Area by a Deep Learning Algorithm

2021 ◽  
Vol 13 (9) ◽  
pp. 1779
Author(s):  
Xiaoyan Yin ◽  
Zhiqun Hu ◽  
Jiafeng Zheng ◽  
Boyong Li ◽  
Yuanyuan Zuo
Keyword(s):  
Deep Learning ◽  
Loss Function ◽  
Learning Algorithm ◽  
Weather Radar ◽  
Loss Functions ◽  
Training Dataset ◽  
Echo Intensity ◽  
Common Mean ◽  
Deep Learning Algorithm ◽  
Radar Beam

Radar beam blockage is an important error source that affects the quality of weather radar data. An echo-filling network (EFnet) is proposed based on a deep learning algorithm to correct the echo intensity under the occlusion area in the Nanjing S-band new-generation weather radar (CINRAD/SA). The training dataset is constructed by the labels, which are the echo intensity at the 0.5° elevation in the unblocked area, and by the input features, which are the intensity in the cube including multiple elevations and gates corresponding to the location of bottom labels. Two loss functions are applied to compile the network: one is the common mean square error (MSE), and the other is a self-defined loss function that increases the weight of strong echoes. Considering that the radar beam broadens with distance and height, the 0.5° elevation scan is divided into six range bands every 25 km to train different models. The models are evaluated by three indicators: explained variance (EVar), mean absolute error (MAE), and correlation coefficient (CC). Two cases are demonstrated to compare the effect of the echo-filling model by different loss functions. The results suggest that EFnet can effectively correct the echo reflectivity and improve the data quality in the occlusion area, and there are better results for strong echoes when the self-defined loss function is used.

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