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Journal Articles

Special Issue: Machine Learning and Representation Issues in CAD/CAM

Anurag Purwar, Kaushalkumar A. Desai, Stephen Canfield, Rahul Rai, Zhenguo Nie
Journal: Journal of Computing and Information Science in Engineering
Publisher: ASME
Article Type: Guest Editorial
J. Comput. Inf. Sci. Eng. January 2024, 24(1): 010301.
Paper No: JCISE-23-1502
https://doi.org/10.1115/1.4064059
Published Online: December 7, 2023
View Article titled, Special Issue: Machine Learning and Representation Issues in CAD/CAM
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Topics: Computer-aided design, Machine learning, Manufacturing, Design, Artificial neural networks
Journal Articles

An Automatic High-Precision Calibration Method of Legs and Feet for Quadruped Robots Using Machine Vision and Artificial Neural Networks

Yaguan Li, Handing Xu, Yanjie Xu, Qingxue Huang, Xin-Jun Liu, 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): 011009.
Paper No: JCISE-23-1107
https://doi.org/10.1115/1.4063891
Published Online: November 21, 2023
View Article titled, An Automatic High-Precision Calibration Method of Legs and Feet for Quadruped Robots Using Machine Vision and Artificial Neural Networks
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Topics: Artificial neural networks, Calibration, Errors, Kinematics, Machinery, Robots, Error compensation
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
Journal Articles

StressGAN: A Generative Deep Learning Model for Two-Dimensional Stress Distribution Prediction

Haoliang Jiang, Zhenguo Nie, Roselyn Yeo, Amir Barati Farimani, Levent Burak Kara
Journal: Journal of Applied Mechanics
Publisher: ASME
Article Type: Research Papers
J. Appl. Mech. May 2021, 88(5): 051005.
Paper No: JAM-20-1553
https://doi.org/10.1115/1.4049805
Published Online: February 11, 2021
View Article titled, StressGAN: A Generative Deep Learning Model for Two-Dimensional Stress Distribution Prediction
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Topics: Stress, Stress concentration, Geometry, Boundary-value problems
Journal Articles

TopologyGAN: Topology Optimization Using Generative Adversarial Networks Based on Physical Fields Over the Initial Domain

Zhenguo Nie, Tong Lin, Haoliang Jiang, Levent Burak Kara
Journal: Journal of Mechanical Design
Publisher: ASME
Article Type: Research Papers
J. Mech. Des. March 2021, 143(3): 031715.
Paper No: MD-20-1434
https://doi.org/10.1115/1.4049533
Published Online: February 3, 2021
View Article titled, TopologyGAN: Topology Optimization Using Generative Adversarial Networks Based on Physical Fields Over the Initial Domain
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Topics: Generators, Optimization, Stress, Topology, Boundary-value problems, Design, Displacement
Proceedings Papers

StressGAN: A Generative Deep Learning Model for 2D Stress Distribution Prediction

Haoliang Jiang, Zhenguo Nie, Roselyn Yeo, Amir Barati Farimani, Levent Burak Kara
Proc. ASME. IDETC-CIE2020, Volume 11B: 46th Design Automation Conference (DAC), V11BT11A023, August 17–19, 2020
Publisher: American Society of Mechanical Engineers
Paper No: DETC2020-22682
https://doi.org/10.1115/DETC2020-22682
View Paper titled, StressGAN: A Generative Deep Learning Model for 2D Stress Distribution Prediction
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Topics: Stress concentration, Stress, Boundary-value problems, Geometry, Artificial neural networks, Computation, Neural network models, Performance, Physics, Resolution (Optics)
Proceedings Papers

TopologyGAN: Topology Optimization Using Generative Adversarial Networks Based on Physical Fields Over the Initial Domain

Zhenguo Nie, Tong Lin, Haoliang Jiang, Levent Burak Kara
Proc. ASME. IDETC-CIE2020, Volume 11A: 46th Design Automation Conference (DAC), V11AT11A008, August 17–19, 2020
Publisher: American Society of Mechanical Engineers
Paper No: DETC2020-22675
https://doi.org/10.1115/DETC2020-22675
View Paper titled, TopologyGAN: Topology Optimization Using Generative Adversarial Networks Based on Physical Fields Over the Initial Domain
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Topics: Optimization, Topology, Boundary-value problems, Errors, Generators, Design, Excitation, Network models, Stress
Journal Articles

Optimization of Part Consolidation for Minimum Production Costs and Time Using Additive Manufacturing

Zhenguo Nie, Sangjin Jung, Levent Burak Kara, Kate S. Whitefoot
Journal: Journal of Mechanical Design
Publisher: ASME
Article Type: Research Papers
J. Mech. Des. July 2020, 142(7): 072001.
Paper No: MD-19-1214
https://doi.org/10.1115/1.4045106
Published Online: December 12, 2019
View Article titled, Optimization of Part Consolidation for Minimum Production Costs and Time Using Additive Manufacturing
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Topics: Additive manufacturing, Design, Filters, Manufacturing, Optimization, Tradeoffs, Shades and shadows, Genetic algorithms, Metals
Includes: Supplementary data
Proceedings Papers

Stress Field Prediction in Cantilevered Structures Using Convolutional Neural Networks

Zhenguo Nie, Haoliang Jiang, Levent Burak Kara
Proc. ASME. IDETC-CIE2019, Volume 1: 39th Computers and Information in Engineering Conference, V001T02A011, August 18–21, 2019
Publisher: American Society of Mechanical Engineers
Paper No: DETC2019-98472
https://doi.org/10.1115/DETC2019-98472
View Paper titled, Stress Field Prediction in Cantilevered Structures Using Convolutional Neural Networks
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Topics: Artificial neural networks, Stress
Proceedings Papers

Optimization of Parts Consolidation for Minimum Production Costs and Time Using Additive Manufacturing

Zhenguo Nie, Sangjin Jung, Levent Burak Kara, Kate S. Whitefoot
Proc. ASME. IDETC-CIE2019, Volume 2A: 45th Design Automation Conference, V02AT03A032, August 18–21, 2019
Publisher: American Society of Mechanical Engineers
Paper No: DETC2019-97649
https://doi.org/10.1115/DETC2019-97649
View Paper titled, Optimization of Parts Consolidation for Minimum Production Costs and Time Using Additive Manufacturing
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Topics: Additive manufacturing, Optimization, Manufacturing, Filters, Redundancy (Engineering), Aircraft, Cathode ray oscilloscopes, Electron beams, Energy consumption, Genetic algorithms
Journal Articles

Stress Field Prediction in Cantilevered Structures Using Convolutional Neural Networks

Zhenguo Nie, Haoliang Jiang, Levent Burak Kara
Journal: Journal of Computing and Information Science in Engineering
Publisher: ASME
Article Type: Research Papers
J. Comput. Inf. Sci. Eng. February 2020, 20(1): 011002.
Paper No: JCISE-19-1077
https://doi.org/10.1115/1.4044097
Published Online: September 10, 2019
View Article titled, Stress Field Prediction in Cantilevered Structures Using Convolutional Neural Networks
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Topics: Stress, Artificial neural networks, Geometry, Testing
Journal Articles

A Coupled Thermomechanical Modeling Method for Predicting Grinding Residual Stress Based on Randomly Distributed Abrasive Grains

Zhenguo Nie, Gang Wang, Liping Wang, Yiming (Kevin) Rong
Journal: Journal of Manufacturing Science and Engineering
Publisher: ASME
Article Type: Research-Article
J. Manuf. Sci. Eng. August 2019, 141(8): 081005.
Paper No: MANU-19-1006
https://doi.org/10.1115/1.4043799
Published Online: June 10, 2019
View Article titled, A Coupled Thermomechanical Modeling Method for Predicting Grinding Residual Stress Based on Randomly Distributed Abrasive Grains
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Topics: Grinding, Stress, Thermomechanics, Temperature, Simulation