Interaction-based material network: a general framework for (porous) microstructured materials

Nguyen, Van Dung; Noels, Ludovic

doi:10.1016/j.cma.2021.114300

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Interaction-based material network: a general framework for (porous) microstructured materials

Nguyen, Van Dung; Noels, Ludovic

2022 • In Computer Methods in Applied Mechanics and Engineering, 389, p. 114300

Peer Reviewed verified by ORBi

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https://hdl.handle.net/2268/264726

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10.1016/j.cma.2021.114300

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NOTICE: this is the author’s version of a work that was accepted for publication in Computer Methods in Applied Mechanics and Engineering. 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 Computer Methods in Applied Mechanics and Engineering 389 (2022) 114300, DOI: 10.1016/j.cma.2021.114300

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Keywords :

Deep Material Network; Porous material; Machine learning; Inelastic training

Abstract :

[en] A material network consisting of discrete material nodes and their interactions can represent complex microstructure responses. Under this interaction viewpoint, the material network can be viewed as a trainable system involving fitting parameters including not only the weights of the material nodes but also the parameters characterizing their interactions. As opposed to the other existing works, this interaction-based material network does not rely on the micromechanics of multiple-phase laminates but on constraining all requirements of a truly microscopic boundary value problem including the stress and strain averaging principles and the Hill-Mandel energetically consistent condition. Consequently, the proposed framework can be applied to microstructures with the presence of voids, which is not achievable with the laminate theory. To make a material network become a surrogate of a full-field microscopic model, this work proposes two different training procedures to calibrate its fitting parameters. On the one hand, a nonlinear training procedure is proposed considering sequential data collected from finite element simulations on the full-field model subjected to proportional loading paths. On the other hand, a linear elastic training procedure considers only the elastic response of the heterogeneous material. The accuracy and efficiency of the proposed framework for microstructures with the presence of voids are demonstrated by comparing the predictions of the trained material networks with the ones of the direct numerical simulations in both contexts of virtual testing and multiscale simulations. It is also shown that the linear elastic training procedure requires a lower computational cost but could lead to less accurate predictions in comparison with the nonlinear training procedure.

Research Center/Unit :

A&M - Aérospatiale et Mécanique - ULiège

Disciplines :

Mechanical engineering
Materials science & engineering

Author, co-author :

Nguyen, Van Dung ; Université de Liège - ULiège > Département d'aérospatiale et mécanique > Computational & Multiscale Mechanics of Materials (CM3)

Noels, Ludovic ; Université de Liège - ULiège > Département d'aérospatiale et mécanique > Computational & Multiscale Mechanics of Materials (CM3)

Language :

English

Title :

Interaction-based material network: a general framework for (porous) microstructured materials

Publication date :

01 February 2022

Journal title :

Computer Methods in Applied Mechanics and Engineering

ISSN :

0045-7825

eISSN :

1879-2138

Publisher :

Elsevier, Amsterdam, Netherlands

Volume :

389

Pages :

114300

Peer reviewed :

Peer Reviewed verified by ORBi

Tags :

CÉCI : Consortium des Équipements de Calcul Intensif

Additional URL :

https://doi.org/10.5281/zenodo.5568832

Funders :

F.R.S.-FNRS - Fonds de la Recherche Scientifique

Commentary :

Data can be downloaded on: https://doi.org/10.5281/zenodo.5568832

Available on ORBi :

since 04 November 2021

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