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On strain hardening mechanism in gradient nanostructures

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Li,  Jianjun
Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

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Li, J., Weng, G. J., Chen, S., & Wu, X. (2017). On strain hardening mechanism in gradient nanostructures. International Journal of Plasticity, 88, 89-107. doi:10.1016/j.ijplas.2016.10.003.


Cite as: https://hdl.handle.net/21.11116/0000-0001-71BF-9
Abstract
Experiments have shown that a gradient design, in which grain size spans over four orders of magnitude, can make strong nanomaterials ductile. The enhanced ductility is attributed to the considerable strain hardening capability obtained in the gradient metals. A non-uniform deformation on the lateral sample surface is also observed. This might inject geometrically necessary dislocations (GNDs) into the sample. However, no direct evidence has been provided. Therefore the issues remain: why can the gradient structure generate high strain hardening, and how does it reconcile the strength-ductility synergy of gradient nanostructures? Here for the first time we quantitatively investigate the strain hardening of a gradient interstitial-free steel by developing a dislocation density-based continuum plasticity model, in which the interaction of the component layers in the gradient structure is represented by incorporating GNDs and back stress. It is demonstrated that both the surface non-uniform deformation and the strain-hardening rate up-turn can be quantitatively well predicted. The results also show that the strain hardening rate of the gradient sample can reach as high as that of the coarse-grained counterpart. A strength-ductility map is then plotted, which clearly show that the gradient samples perform much more superior to their homogeneous counterparts in strength-ductility synergy. The predicted map has been verified by a series of experimental data. A detailed analysis on GNDs distribution and back stress evolution at the end further substantiates our view that the good strain hardening capability results from the generation of abundant GNDs by the surface non-uniform deformation into the nano-grained layers of the gradient sample. (C) 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).