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Journal Articles

Cross-Domain Transfer Learning for Galvanized Steel Strips Defect Detection and Recognition

Hao Chen, Hongbin Lin, Qingfeng Xu, Yaguan Li, Yiming Zheng, Jianghua Fei, Kang Yang, Wenhui Fan, Zhenguo Nie
Journal: Journal of Computing and Information Science in Engineering
Publisher: ASME
Article Type: Research Papers
J. Comput. Inf. Sci. Eng. January 2024, 24(1): 011006.
Paper No: JCISE-23-1026
https://doi.org/10.1115/1.4063102
Published Online: September 14, 2023
View Article titled, Cross-Domain Transfer Learning for Galvanized Steel Strips Defect Detection and Recognition
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Topics: Flaw detection, Manufacturing, Steel, Strips, Galvanized steel, Design, Feature extraction, Inspection
Journal Articles

Three-Dimensional-Slice-Super-Resolution-Net: A Fast Few Shooting Learning Model for 3D Super-Resolution Using Slice-Up and Slice-Reconstruction

Hongbin Lin, Qingfeng Xu, Handing Xu, Yanjie Xu, Yiming Zheng, Yubin Zhong, Zhenguo Nie
Journal: Journal of Computing and Information Science in Engineering
Publisher: ASME
Article Type: Research Papers
J. Comput. Inf. Sci. Eng. January 2024, 24(1): 011005.
Paper No: JCISE-23-1027
https://doi.org/10.1115/1.4063275
Published Online: September 14, 2023
View Article titled, Three-Dimensional-Slice-Super-Resolution-Net: A Fast Few Shooting Learning Model for 3D Super-Resolution Using Slice-Up and Slice-Reconstruction
Open the PDF for Three-Dimensional-Slice-Super-Resolution-Net: A Fast Few Shooting Learning Model for 3D Super-Resolution Using Slice-Up and Slice-Reconstruction in another window
Topics: Resolution (Optics)
Journal Articles

Intelligent Detection and Classification of Surface Defects on Cold-Rolled Galvanized Steel Strips Using a Data-Driven Faulty Model With Attention Mechanism

Hao Chen, Zhenguo Nie, Qingfeng Xu, Jianghua Fei, Kang Yang, Yaguan Li, Hongbin Lin, Wenhui Fan, Xin-Jun Liu
Journal: Journal of Computing and Information Science in Engineering
Publisher: ASME
Article Type: Research Papers
J. Comput. Inf. Sci. Eng. August 2023, 23(4): 041001.
Paper No: JCISE-22-1207
https://doi.org/10.1115/1.4055672
Published Online: December 27, 2022
View Article titled, Intelligent Detection and Classification of Surface Defects on Cold-Rolled Galvanized Steel Strips Using a Data-Driven Faulty Model With Attention Mechanism
Open the PDF for Intelligent Detection and Classification of Surface Defects on Cold-Rolled Galvanized Steel Strips Using a Data-Driven Faulty Model With Attention Mechanism in another window
Topics: Flaw detection, Galvanized steel, Steel, Strips
Proceedings Papers

Classification of Surface Defects on Galvanized Cold-Rolled Steel Sheets Using Data-Driven Fault Model With Attention Mechanism

Hao Chen, Zhenguo Nie, Qingfeng Xu, Jianghua Fei, Kang Yang, Yaguan Li, Wenhui Fan, Xin-Jun Liu
Proc. ASME. IDETC-CIE2022, Volume 2: 42nd Computers and Information in Engineering Conference (CIE), V002T02A075, August 14–17, 2022
Publisher: American Society of Mechanical Engineers
Paper No: DETC2022-91218
https://doi.org/10.1115/DETC2022-91218
View Paper titled, Classification of Surface Defects on Galvanized Cold-Rolled Steel Sheets Using Data-Driven Fault Model With Attention Mechanism
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Topics: Steel sheet, Assembly lines, Galvanized steel, Inspection, Metal stamping, Quality control, Steel
Journal Articles

SuperMeshing: Boosting the Mesh Density of Stress Field in Plane-Strain Problems Using Deep Learning Method

Handing Xu, Zhenguo Nie, Qingfeng Xu, Yaguan Li, Fugui Xie, Xin-Jun Liu
Journal: Journal of Computing and Information Science in Engineering
Publisher: ASME
Article Type: Technical Briefs
J. Comput. Inf. Sci. Eng. June 2023, 23(3): 034501.
Paper No: JCISE-21-1402
https://doi.org/10.1115/1.4054687
Published Online: August 8, 2022
View Article titled, SuperMeshing: Boosting the Mesh Density of Stress Field in Plane-Strain Problems Using Deep Learning Method
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Topics: Density, Stress, Plane strain, Finite element methods, Resolution (Optics)
Proceedings Papers

SuperMeshing: A Deep Learning Method for Boosting Mesh Density in Numerical Computation Within 2D Domain

Handing Xu, Zhenguo Nie, Qingfeng Xu, Xinjun Liu
Proc. ASME. IDETC-CIE2021, Volume 3B: 47th Design Automation Conference (DAC), V03BT03A037, August 17–19, 2021
Publisher: American Society of Mechanical Engineers
Paper No: DETC2021-68112
https://doi.org/10.1115/DETC2021-68112
View Paper titled, SuperMeshing: A Deep Learning Method for Boosting Mesh Density in Numerical Computation Within 2D Domain
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Topics: Computation, Density, Stress, Finite element methods, Resolution (Optics), Artificial neural networks, Errors, Interpolation, Plane strain
Proceedings Papers

SuperMeshing: A New Deep Learning Architecture for Increasing the Mesh Density of Metal Forming Stress Field With Attention Mechanism And Perceptual Features

Qingfeng Xu, Zhenguo Nie, Handing Xu, Haosu Zhou, Xinjun Liu
Proc. ASME. IDETC-CIE2021, Volume 3B: 47th Design Automation Conference (DAC), V03BT03A041, August 17–19, 2021
Publisher: American Society of Mechanical Engineers
Paper No: DETC2021-71158
https://doi.org/10.1115/DETC2021-71158
View Paper titled, SuperMeshing: A New Deep Learning Architecture for Increasing the Mesh Density of Metal Forming Stress Field With Attention Mechanism And Perceptual Features
Open the PDF for SuperMeshing: A New Deep Learning Architecture for Increasing the Mesh Density of Metal Forming Stress Field With Attention Mechanism And Perceptual Features in another window
Topics: Density, Metalworking, Stress, Finite element analysis, Errors, Finite element methods, Interpolation, Sheet metal work, Simulation results
Journal Articles

SuperMeshing: A New Deep Learning Architecture for Increasing the Mesh Density of Physical Fields in Metal Forming Numerical Simulation

Qingfeng Xu, Zhenguo Nie, Handing Xu, Haosu Zhou, Hamid Reza Attar, Nan Li, Fugui Xie, Xin-Jun Liu
Journal: Journal of Applied Mechanics
Publisher: ASME
Article Type: Research Papers
J. Appl. Mech. January 2022, 89(1): 011002.
Paper No: JAM-21-1240
https://doi.org/10.1115/1.4052195
Published Online: September 13, 2021
View Article titled, SuperMeshing: A New Deep Learning Architecture for Increasing the Mesh Density of Physical Fields in Metal Forming Numerical Simulation
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Topics: Density, Finite element methods, Metalworking, Interpolation, Resolution (Optics)
Journal Articles

A Study on Using Image-Based Machine Learning Methods to Develop Surrogate Models of Stamp Forming Simulations

Haosu Zhou, Qingfeng Xu, Zhenguo Nie, Nan Li
Journal: Journal of Manufacturing Science and Engineering
Publisher: ASME
Article Type: Research Papers
J. Manuf. Sci. Eng. February 2022, 144(2): 021012.
Paper No: MANU-21-1029
https://doi.org/10.1115/1.4051604
Published Online: August 6, 2021
View Article titled, A Study on Using Image-Based Machine Learning Methods to Develop Surrogate Models of Stamp Forming Simulations
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Topics: Design, Engineering simulation, Finite element analysis, Machine learning, Multilayer perceptron, Scalars, Simulation, Interpolation, Metalworking, Simulation results