Hybrid constitutive modeling: data-driven learning of corrections to plasticity models

Ibáñez, R. ; Abisset-Chavanne, E. ; González, D. (Universidad de Zaragoza) ; Duval, J.L. ; Cueto, E. (Universidad de Zaragoza) ; Chinesta, F.

Resumen: In recent times a growing interest has arose on the development of data-driven techniques to avoid the employ of phenomenological constitutive models. While it is true that, in general, data do not fit perfectly to existing models, and present deviations from the most popular ones, we believe that this does not justify (or, at least, not always) to abandon completely all the acquired knowledge on the constitutive characterization of materials. Instead, what we propose here is, by means of machine learning techniques, to develop correction to those popular models so as to minimize the errors in constitutive modeling.

Idioma: Inglés

DOI: 10.1007/s12289-018-1448-x

Año: 2018

Publicado en: International journal of material forming 12 (2018), 717 – 725
ISSN: 1960-6206

Factor impacto JCR: 1.75 (2018)

Categ. JCR: METALLURGY & METALLURGICAL ENGINEERING rank: 27 / 76 = 0.355 (2018) - Q2 - T2
Categ. JCR: ENGINEERING, MANUFACTURING rank: 36 / 49 = 0.735 (2018) - Q3 - T3
Categ. JCR: MATERIALS SCIENCE, MULTIDISCIPLINARY rank: 190 / 293 = 0.648 (2018) - Q3 - T2

Factor impacto SCIMAGO: 0.638 - Materials Science (miscellaneous) (Q2)

Financiación: info:eu-repo/grantAgreement/ES/DGA/T24-17R
Financiación: info:eu-repo/grantAgreement/EC/H2020/675919/EU/Empowered decision-making in simulation-based engineering: Advanced Model Reduction for real-time, inverse and optimization in industrial problems/AdMoRe
Financiación: info:eu-repo/grantAgreement/ES/MINECO/DPI2015-72365-EXP
Financiación: info:eu-repo/grantAgreement/ES/MINECO/DPI2017-85139-C2-1-R

Tipo y forma: Artículo (PostPrint)

Área (Departamento): Área Mec.Med.Cont. y Teor.Est. (Dpto. Ingeniería Mecánica)