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Haosu Zhou
Tytuł
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A study on using image-based machine learning methods to develop surrogate models of stamp forming simulations
H Zhou, Q Xu, Z Nie, N Li
Journal of Manufacturing Science and Engineering 144 (2), 021012, 2022
402022
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
322021
An improved numerically-stable equivalent static loads (ESLs) algorithm based on energy-scaling ratio for stiffness topology optimization under crash loads
YC Bai, HS Zhou, F Lei, HS Lei
Structural and Multidisciplinary Optimization 59, 117-130, 2019
252019
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
172022
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
162021
On the feasibility of small-data learning in simulation-driven engineering tasks with known mechanisms and effective data representations
H Zhou, HR Attar, Y Pan, X Li, PRN Childs, N Li
NeurIPS 2021 AI for Science Workshop, 2021
72021
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
22024
Image-based Artificial Intelligence empowered surrogate model and shape morpher for real-time blank shape optimisation in the hot stamping process
H Zhou, N Li
arXiv preprint arXiv:2212.05885, 2022
22022
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
2024
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