Hybrid approach to predict the effective properties of heterogeneous materials using artificial neural networks and micromechanical models
| dc.rights.license | open | en_US |
| hal.structure.identifier | Institut de Mécanique et d'Ingénierie [I2M] | |
| dc.contributor.author | DANOUN, Aymen
IDREF: 256486999 | |
| hal.structure.identifier | Institut de Mécanique et d'Ingénierie [I2M] | |
| dc.contributor.author | PRULIÉRE, Etienne | |
| hal.structure.identifier | Institut de Mécanique et d'Ingénierie [I2M] | |
| dc.contributor.author | CHEMISKY, Yves
IDREF: 143502301 | |
| dc.date.accessioned | 2021-12-21T10:16:52Z | |
| dc.date.available | 2021-12-21T10:16:52Z | |
| dc.date.issued | 2022-02-15 | |
| dc.identifier.issn | 0029-5981 | en_US |
| dc.identifier.uri | oai:crossref.org:10.1002/nme.6877 | |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/124284 | |
| dc.description.abstractEn | In this article, an investigation was carried out to verify hybrid models capabilities to predict the effective properties of heterogeneous materials. A hybrid model (Formula presented.) is developed by combining artificial neural networks and micromechanical modeling. The homogenization approach used in this study is mainly based on Eshelby's inclusion problem. The (Formula presented.) model, once trained on an Eshelby's tensors database, showed an excellent predictive capabilities of the effective mechanical behavior and local stresses in heterogeneous materials. The obtained results with (Formula presented.) are compared to numerical estimations which are often costly in terms of computational time. The results presented in this work show that the developed hybrid model can provide a significant computational time saving by a factor up to 2000 for (Formula presented.) phases while maintaining its accuracy and reliability. | |
| dc.language.iso | EN | en_US |
| dc.source | crossref | |
| dc.subject.en | artificial neural network | |
| dc.subject.en | effective properties | |
| dc.subject.en | Eshelby tensor | |
| dc.subject.en | heterogeneous materials | |
| dc.subject.en | homogenization | |
| dc.subject.en | inclusion problems | |
| dc.title.en | Hybrid approach to predict the effective properties of heterogeneous materials using artificial neural networks and micromechanical models | |
| dc.type | Article de revue | en_US |
| dc.identifier.doi | 10.1002/nme.6877 | en_US |
| dc.subject.hal | Sciences de l'ingénieur [physics]/Matériaux | en_US |
| bordeaux.journal | International Journal for Numerical Methods in Engineering | en_US |
| bordeaux.hal.laboratories | Institut de Mécanique et d’Ingénierie de Bordeaux (I2M) - UMR 5295 | en_US |
| bordeaux.institution | Université de Bordeaux | en_US |
| bordeaux.institution | Bordeaux INP | en_US |
| bordeaux.institution | CNRS | en_US |
| bordeaux.institution | INRAE | en_US |
| bordeaux.institution | Arts et Métiers | en_US |
| bordeaux.peerReviewed | oui | en_US |
| bordeaux.inpress | non | en_US |
| bordeaux.import.source | dissemin | |
| hal.identifier | hal-03498851 | |
| hal.version | 1 | |
| hal.date.transferred | 2021-12-21T10:16:55Z | |
| hal.export | true | |
| workflow.import.source | dissemin | |
| dc.rights.cc | Pas de Licence CC | en_US |
| bordeaux.COinS | ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=International%20Journal%20for%20Numerical%20Methods%20in%20Engineering&rft.date=2022-02-15&rft.eissn=0029-5981&rft.issn=0029-5981&rft.au=DANOUN,%20Aymen&PRULI%C3%89RE,%20Etienne&CHEMISKY,%20Yves&rft.genre=article |
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