A benchmark for elasto-plasticity in finite strain
Résumé
Finite strain elasto-plastic simulations are critical in fields such as materials science (metal forming, forging, additive manufacturing) and automotive engineering (crash simulations). These simulations are traditionally carried out using computationally intensive finite element analysis (FEA), which limits their use in optimization tasks (e.g., optimal control, design processes) and real-time applications (e.g., tele-operation, personnel training). In this work, we introduce a benchmark dedicated to highly non-linear elasto-plastic simulations, designed to evaluate and develop neural network models tailored for solving elasto-plastic problems under finite strain conditions, ultimately unlocking the potential for real-time optimization and interactive simulations. The datasets include simulations of 1D and 3D elements, featuring quasi-static sequences of applied loads on complex geometries, and the resulting computed quantities: displacements fields, plastic flow coefficient field, stresses. To specifically evaluate the impact of plasticity on different neural networks, the datasets also feature simulations with identical inputs but employing a purely elastic constitutive law.
Origine | Fichiers produits par l'(les) auteur(s) |
---|