Articole cu mandate pentru acces public - Nan LiAflați mai multe
Nu sunt disponibile nicăieri: 5
An experimental investigation of the drawability of AA6082 sheet under different elevated temperature forming processes
K Zheng, L Zhu, J Lin, TA Dean, N Li
Journal of Materials Processing Technology 273, 116225, 2019
Mandate: UK Engineering and Physical Sciences Research Council, European Commission
SuperMeshing: A new deep learning architecture for increasing the mesh density of physical fields in metal forming numerical simulation
Q Xu, Z Nie, H Xu, H Zhou, HR Attar, N Li, F Xie, XJ Liu
Journal of Applied Mechanics 89 (1), 011002, 2022
Mandate: National Natural Science Foundation of China
A novel quench-form and in-die creep age process for hot forming of 2219 thin aluminum sheets with high precision and efficiency
K Zheng, Z He, H Qu, F Chen, Y Han, JH Zheng, N Li
Journal of Materials Processing Technology 315, 117931, 2023
Mandate: National Natural Science Foundation of China
Springback prediction for sheet metal cold stamping using convolutional neural networks
L Zhu, N Li
2022 Workshop on Electronics Communication Engineering 12720, 278-283, 2023
Mandate: UK Research & Innovation
Deep Learning Enabled Tool Compensation for Addressing Shape Distortion in Sheet Metal Stamping
HR Attar, L Zhu, N Li
International Conference on the Technology of Plasticity, 48-58, 2023
Mandate: UK Engineering and Physical Sciences Research Council, UK Research & Innovation
Disponibile undeva: 16
Experimental investigation of forming limit curves and deformation features in warm forming of an aluminium alloy
Z Shao, Q Bai, N Li, J Lin, Z Shi, M Stanton, D Watson, T Dean
Proceedings of the Institution of Mechanical Engineers, Part B: Journal of …, 2016
Mandate: National Natural Science Foundation of China
Rapid feasibility assessment of components to be formed through hot stamping: A deep learning approach
HR Attar, H Zhou, A Foster, N Li
Journal of Manufacturing Processes 68, 1650-1671, 2021
Mandate: UK Engineering and Physical Sciences Research Council
A new design guideline development strategy for aluminium alloy corners formed through cold and hot stamping processes
HR Attar, N Li, A Foster
Materials & Design 207, 109856, 2021
Mandate: UK Engineering and Physical Sciences Research Council
Deformation and thinning field prediction for HFQ® formed panel components using convolutional neural networks
HR Attar, H Zhou, N Li
IOP Conference Series: Materials Science and Engineering 1157 (1), 012079, 2021
Mandate: UK Engineering and Physical Sciences Research Council
An investigation of involute and lead deflection in hot precision forging of gears
B Zuo, B Wang, Z Li, N Li, J Lin
The International Journal of Advanced Manufacturing Technology 88, 3017-3030, 2017
Mandate: National Natural Science Foundation of China
An analytical investigation on the wrinkling of aluminium alloys during stamping using macro-scale structural tooling surfaces
K Zheng, J Lee, DJ Politis, N Li, J Lin, TA Dean
The International Journal of Advanced Manufacturing Technology 92, 481-495, 2017
Mandate: European Commission
Implicit neural representations of sheet stamping geometries with small-scale features
HR Attar, A Foster, N Li
Engineering Applications of Artificial Intelligence 123, 106482, 2023
Mandate: UK Engineering and Physical Sciences Research Council, UK Research & Innovation
Investigation of the feasibility of a novel heat stamping process for producing complex-shaped Ti-6Al-4V panel components
F Tian, N Li
Procedia Manufacturing 47, 1374-1380, 2020
Mandate: UK Engineering and Physical Sciences Research Council
Development of a deep learning platform for sheet stamping geometry optimisation under manufacturing constraints
HR Attar, A Foster, N Li
Engineering Applications of Artificial Intelligence 123, 106295, 2023
Mandate: UK Engineering and Physical Sciences Research Council, UK Research & Innovation
Integrating convolutional neural network and constitutive model for rapid prediction of stress-strain curves in fibre reinforced polymers: a generalisable approach
Z Ding, HR Attar, H Wang, H Liu, N Li
Materials & Design 241, 112849, 2024
Mandate: UK Engineering and Physical Sciences Research Council
A review of graph neural network applications in mechanics-related domains
Y Zhao, H Li, H Zhou, HR Attar, T Pfaff, N Li
Artificial Intelligence Review 57 (11), 315, 2024
Mandate: UK Engineering and Physical Sciences Research Council, UK Research & Innovation
Optimisation of panel component regions subject to hot stamping constraints using a novel deep-learning-based platform
HR Attar, A Foster, N Li
IOP Conference Series: Materials Science and Engineering 1270 (1), 012123, 2022
Mandate: UK Engineering and Physical Sciences Research Council
Optimisation of deep drawn corners subject to hot stamping constraints using a novel deep-learning-based platform
HR Attar, N Li
IOP Conference Series: Materials Science and Engineering 1238 (1), 012066, 2022
Mandate: UK Engineering and Physical Sciences Research Council
A method for determining equivalent hardening responses to approximate sheet metal viscoplasticity
HR Attar, N Li, A Foster
MethodsX 8, 101554, 2021
Mandate: UK Engineering and Physical Sciences Research Council
An integrated convolutional neural network-based surrogate model for crashworthiness performance prediction of hot-stamped vehicle panel components
H Li, H Zhou, N Li
MATEC Web of Conferences 401, 03013, 2024
Mandate: UK Research & Innovation
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