Követés
Zhuang Zhao-赵壮
Cím
Hivatkozott rá
Hivatkozott rá
Év
Coordinated monitoring and control method of deposited layer width and reinforcement in WAAM process
Y Wang, X Xu, Z Zhao, W Deng, J Han, L Bai, X Liang, J Yao
Journal of Manufacturing Processes 71, 306-316, 2021
502021
Active disturbance rejection control of layer width in wire arc additive manufacturing based on deep learning
Y Wang, J Lu, Z Zhao, W Deng, J Han, L Bai, X Yang, J Yao
Journal of Manufacturing Processes 67, 364-375, 2021
502021
Quality monitoring in wire-arc additive manufacturing based on cooperative awareness of spectrum and vision
Z Zhao, Y Guo, L Bai, K Wang, J Han
Optik 181, 351-360, 2019
452019
Weld reinforcement analysis based on long-term prediction of molten pool image in additive manufacturing
Y Wang, C Zhang, J Lu, L Bai, Z Zhao, J Han
IEEE Access 8, 69908-69918, 2020
382020
Optimal imaging band selection mechanism of weld pool vision based on spectrum analysis
Z Zhao, L Deng, L Bai, Y Zhang, J Han
Optics & Laser Technology 110, 145-151, 2019
322019
Additive seam tracking technology based on laser vision
Z Zhao, J Luo, Y Wang, L Bai, J Han
The International Journal of Advanced Manufacturing Technology 116 (1), 197-211, 2021
312021
Multimodal-based weld reinforcement monitoring system for wire arc additive manufacturing
B Shen, J Lu, Y Wang, D Chen, J Han, Y Zhang, Z Zhao
Journal of Materials Research and Technology 20, 561-571, 2022
292022
Identification of butt welded joint penetration based on infrared thermal imaging
R Yu, J Han, L Bai, Z Zhao
Journal of Materials Research and Technology 12, 1486-1495, 2021
292021
Material-guided multiview fusion network for hyperspectral object tracking
Z Li, F Xiong, J Zhou, J Lu, Z Zhao, Y Qian
IEEE Transactions on Geoscience and Remote Sensing 62, 1-15, 2024
222024
Quantitative prediction for weld reinforcement in arc welding additive manufacturing based on molten pool image and deep residual network
J Lu, H He, Y Shi, L Bai, Z Zhao, J Han
Additive Manufacturing 41, 101980, 2021
202021
Monitoring of back bead penetration based on temperature sensing and deep learning
R Yu, H He, J Han, L Bai, Z Zhao, J Lu
Measurement 188, 110410, 2022
192022
Real-time prediction of welding penetration mode and depth based on visual characteristics of weld pool in GMAW process
R Yu, J Han, Z Zhao, L Bai
Ieee Access 8, 81564-81573, 2020
192020
Online weld pool contour extraction and seam width prediction based on mixing spectral vision
Y Zhang, Z Zhao, Y Zhang, L Bai, K Wang, J Han
Optical review 26, 65-76, 2019
182019
The 3D narrow butt weld seam detection system based on the binocular consistency correction
X Wang, T Chen, Y Wang, D Zheng, X Chen, Z Zhao
Journal of Intelligent Manufacturing 34 (5), 2321-2332, 2023
172023
Dual camera snapshot hyperspectral imaging system via physics-informed learning
H Xie, Z Zhao, J Han, Y Zhang, L Bai, J Lu
Optics and Lasers in Engineering 154, 107023, 2022
162022
Collaborative and quantitative prediction for reinforcement and penetration depth of weld bead based on molten pool image and deep residual network
J Lu, Y Shi, L Bai, Z Zhao, J Han
IEEE Access 8, 126138-126148, 2020
142020
Quantitative prediction of additive manufacturing deposited layer offset based on passive visual imaging and deep residual network
H He, J Lu, Y Zhang, J Han, Z Zhao
Journal of Manufacturing Processes 72, 195-202, 2021
132021
A seam tracking method based on an image segmentation deep convolutional neural network
J Lu, A Yang, X Chen, X Xu, R Lv, Z Zhao
Metals 12 (8), 1365, 2022
112022
TIG stainless steel molten pool contour detection and weld width prediction based on Res-Seg
Y Wang, J Han, J Lu, L Bai, Z Zhao
Metals 10 (11), 1495, 2020
112020
Visual texture-based 3-D roughness measurement for additive manufacturing surfaces
H Yu, C Peng, Z Zhao, L Bai, J Han
Ieee Access 7, 186646-186656, 2019
112019
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