Modelling the Shear Banding in Gradient Nano-Grained Metals.
Nanomaterials (Basel)
; 11(10)2021 Sep 22.
Article
em En
| MEDLINE
| ID: mdl-34684909
ABSTRACT
Extensive experiments have shown that gradient nano-grained metals have outstanding synergy of strength and ductility. However, the deformation mechanisms of gradient metals are still not fully understood due to their complicated gradient microstructure. One of the difficulties is the accurate description of the deformation of the nanocrystalline surface layer of the gradient metals. Recent experiments with a closer inspection into the surface morphology of the gradient metals reported that shear bands (strain localization) occur at the surface of the materials even under a very small, applied strain, which is in contrast to previously suggested uniform deformation. Here, a dislocation density-based computational model is developed to investigate the shear band evolution in gradient Cu to overcome the above difficulty and to clarify the above debate. The Voronoi polygon is used to establish the irregular grain structure, which has a gradual increase in grain size from the material surface to the interior. It was found that the shear band occurs at a small applied strain in the surface region of the gradient structure, and multiple shear bands are gradually formed with increasing applied load. The early appearance of shear banding and the formation of abundant shear bands resulted from the constraint of the coarse-grained interior. The number of shear bands and the uniform elongation of the gradient material were positively related, both of which increased with decreasing grain size distribution index and gradient layer thickness or increasing surface grain size. The findings are in good agreement with recent experimental observations in terms of stress-strain responses and shear band evolution. We conclude that the enhanced ductility of gradient metals originated from the gradient deformation-induced stable shear band evolution during tension.
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Base de dados:
MEDLINE
Tipo de estudo:
Prognostic_studies
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article