Bayesian inference of high-dimensional finite-strain visco-elastic-visco-plastic model parameters for additive manufactured polymers and neural network based material parameters generator
Wu, Ling; Anglade, Cyrielle; Cobian, Luciaet al.
2023 • In International Journal of Solids and Structures, 283, p. 112470
Applied Mathematics; Mechanical Engineering; Mechanics of Materials; Condensed Matter Physics; General Materials Science; Modeling and Simulation
Résumé :
[en] In this work, the parameters of a finite-strain visco-elastic-visco-plastic formulation with pressure dependency in both the visco-elastic and visco-plastic parts are identified using as observations experimental data obtained from tension and compression tests at different strain rates ranging from 10^-4 s^-1 to 10^3 s^-1 . Because of the high number of parameters of the model, a sequential Bayesian Inference (SBI) framework with data augmentation, which presents several advantages, is developed. First the sequential nature reduces the difficulty of selecting the appropriate prior distributions by considering only parts of the observations at a time. Second, the sequential nature prevents dealing with low likelihood values by considering only a part of the experimental observations at a time, but also subsets of the material parameters to be identified, improving the convergence of the Markov Chain Monte Carlo (MCMC) random walk. Third, the data augmentation allows considering different number of experimental tests in tension and in compression while preserving the identified model accuracy for both loading modes.
This SBI is carried out to infer the properties of Polyamide 12 (PA12) processed by Selective Laser Sintering (SLS) for two different printing directions and it is shown that the models fed by their respective set of inferred parameters can reproduce the different experimental tests.
Finally, in order for upcoming structural simulations to benefit from the information related to the uncertainties due to the measurement errors, the identification process and the model limitations, we introduce a Generative Adversarial Network (GAN), which is trained using the data obtained from MCMC random walk. This generators can then serve to produce a synthetic data-set of arbitrary size of the material parameters to be used in finite-element simulations.
Centre/Unité de recherche :
A&M - Aérospatiale et Mécanique - ULiège
Disciplines :
Science des matériaux & ingénierie Ingénierie mécanique
Auteur, co-auteur :
Wu, Ling ; Université de Liège - ULiège > Département d'aérospatiale et mécanique > Computational & Multiscale Mechanics of Materials (CM3)
Anglade, Cyrielle
Cobian, Lucia
Monclus, Miguel
Segurado, Javier
Karayagiz, Fatma
Freitas, Ubiratan
Noels, Ludovic ; Université de Liège - ULiège > Département d'aérospatiale et mécanique > Computational & Multiscale Mechanics of Materials (CM3)
Langue du document :
Anglais
Titre :
Bayesian inference of high-dimensional finite-strain visco-elastic-visco-plastic model parameters for additive manufactured polymers and neural network based material parameters generator
NOTICE: this is the author’s version of a work that was accepted for publication in International Journal of Solids and Structures. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Solids and Structures 283 (2023) 112470, DOI: 10.1016/j.ijsolstr.2023.112470
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