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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Lafon, Pascal
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Publications (9/9 displayed)
- 2021Homogenization Method to Calculate the Stiffness Matrix of Laminated Compositescitations
- 2020Transverse Isotropic Behavior Identification using Digital Image Correlation of a Pre-structured Material Manufactured by 3D Printingcitations
- 2019Multi-objective optimization approach in design for additive manufacturing for fused deposition modelingcitations
- 2018Toward integrated design of additive manufacturing through a process development model and multi-objective optimizationcitations
- 2017In-plane permeability prediction model for non-crimp and 3D orthogonal fabricscitations
- 2016Surrogate models for sheet metal stamping problem based on the combination of Proper Orthogonal Decomposition and Radial Basis Function.
- 2013Forging tool shape optimization using pseudo inverse approach and adaptive incremental approach
- 2012Optimization of Forging Preforms by Using Pseudo Inverse Approachcitations
- 2010Parameters Optimization of Metal Forming Process
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document
Surrogate models for sheet metal stamping problem based on the combination of Proper Orthogonal Decomposition and Radial Basis Function.
Abstract
In this work, a combination of Proper Orthogonal Decomposition (POD) and Radial Basis Function (RBF) is proposed to represent the physics-base surrogate model. The method is tested on the deterministically parameterized process of deep drawing of sheet, which is a critical process in the problem of multi-parametric sheet metal forming. The basic idea is to consider design variables and/or uncertain parameters as additional dimensions for the solution of displacement. The order of the resultant high-fidelity model is reduced through the use of POD method. The POD basis, constructed from the snapshots of final displacements fields of FEM simulation, is used to determine the POD coefficients. The function coefficient is interpolated from these POD coefficients using RBF.