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Cited article:

Recurrent U-Net-based Graph Neural Network (RUGNN) for accurate deformation predictions in sheet material forming

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https://doi.org/10.1016/j.aei.2025.104021

Rapid prediction of material deformation in hot stamping of battery box geometries using graph neural network

Yingxue Zhao, Haoran Li, Haosu Zhou, Hamid Reza Attar, Tobias Pfaff and Nan Li
Journal of Physics: Conference Series 3104 (1) 012057 (2025)
https://doi.org/10.1088/1742-6596/3104/1/012057

Development of a Deep Learning Platform for Sheet Stamping Geometry Optimisation under Manufacturing Constraints

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https://doi.org/10.1016/j.engappai.2023.106295

Hot Sheet Metal Forming Strategies for High‐Strength Aluminum Alloys: A Review—Fundamentals and Applications

Emad Scharifi, Victoria A. Yardley, Ursula Weidig, Damian Szegda, Jianguo Lin and Kurt Steinhoff
Advanced Engineering Materials 25 (16) (2023)
https://doi.org/10.1002/adem.202300141

Rapid feasibility assessment of components to be formed through hot stamping: A deep learning approach

Hamid Reza Attar, Haosu Zhou, Alistair Foster and Nan Li
Journal of Manufacturing Processes 68 1650 (2021)
https://doi.org/10.1016/j.jmapro.2021.06.011

Advances in FEM Simulation of HFQ® AA6082 tailor welded blanks for automotive applications

Mohamed Mohamed, David Norman, Aura Petre, Federico Melotti and Damian Szegda
IOP Conference Series: Materials Science and Engineering 418 012036 (2018)
https://doi.org/10.1088/1757-899X/418/1/012036