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Abdessalem Chamekh
Researcher at King Abdulaziz University
Publications - 11
Citations - 158
Abdessalem Chamekh is an academic researcher from King Abdulaziz University. The author has contributed to research in topics: Finite element method & Mixed finite element method. The author has an hindex of 6, co-authored 11 publications receiving 136 citations. Previous affiliations of Abdessalem Chamekh include École Normale Supérieure.
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Multiscale approach including microfibril scale to assess elastic constants of cortical bone based on neural network computation and homogenization method
TL;DR: In this paper, a multiscale hierarchical approach including microfibril scale based on hybrid neural network (NN) computation and homogenization equations was developed to link nanoscopic and macroscopic scales to estimate the elastic properties of human cortical bone.
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Inverse identification using the bulge test and artificial neural networks
TL;DR: In this article, an approach based on artificial neural networks to identify the material parameters of a stainless steel material was described, which was trained using finite element simulations of the bulge test and corresponding material parameters.
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Multiscale approach including microfibril scale to assess elastic constants of cortical bone based on neural network computation and homogenization method
TL;DR: A novel multiscale hierarchical approach including microfibril scale based on hybrid neural network (NN) computation and homogenization equations was developed to link nanoscopic and macroscopic scales to estimate the elastic properties of human cortical bone.
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Identification of Constitutive Parameters using Hybrid ANN multi-objective optimization procedure
TL;DR: This work proposes a hybrid approach where Artificial Neural Networks (ANN) are trained by finite element results and the multi objective procedure calls the ANN function in place of the finite element code.
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An optimization strategy based on a metamodel applied for the prediction of the initial blank shape in a deep drawing process
TL;DR: In this article, an automatic procedure for the quick sheet metal forming optimization is proposed, in which a metamodel is built based on artificial neural networks which is coupled with an optimization procedure in order to predict the initial blank shape in a rectangular cup deep drawing operation.