5 citations · 12 across the 5 of their papers we have counts for
5 papers
Image-based Artificial Intelligence empowered surrogate model and shape morpher for real-time blank shape optimisation in the hot stamping process
Haosu Zhou, Nan Li
As the complexity of modern manufacturing technologies increases, traditional trial-and-error design, which requires iterative and expensive simulations, becomes unreliable and tim…
Development of a deep learning platform for optimising sheet stamping geometries subject to manufacturing constraints
Hamid Reza Attar, Alistair Foster, Nan Li
The latest sheet stamping processes enable efficient manufacturing of complex shape structural components that have high stiffness to weight ratios, but these processes can introdu…
Rapid feasibility assessment of components formed through hot stamping: A deep learning approach
Hamid Reza Attar, Haosu Zhou, Alistair Foster +1
The novel non-isothermal Hot Forming and cold die Quenching (HFQ) process can enable the cost-effective production of complex shaped, high strength aluminium alloy panel components…
Optimization of graded filleted lattice structures subject to yield and buckling constraints
Xiaoyang Wang, Lei Zhu, Liao Sun +1
To reduce the stress concentration and ensure the structural safety for lattice structure designs, in this paper, a new optimization framework is developed for the optimal design o…
A study on using image based machine learning methods to develop the surrogate models of stamp forming simulations
Haosu Zhou, Qingfeng Xu, Nan Li
In the design optimization of metal forming, it is increasingly significant to use surrogate models to analyse the finite element analysis (FEA) simulations. However, traditional s…