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 | 40 | 2022 |
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 | 32 | 2021 |
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 | 25 | 2019 |
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 | 17 | 2022 |
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 | 16 | 2021 |
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 | 7 | 2021 |
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 | 2 | 2024 |
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 | 2 | 2022 |
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 |