期刊
JOURNAL OF MAGNESIUM AND ALLOYS
卷 11, 期 10, 页码 3657-3672出版社
KEAI PUBLISHING LTD
DOI: 10.1016/j.jma.2023.09.014
关键词
Mg alloys; Microstructure-property relations; Grain-size effect; Crystal plasticity; Damage micromechanics
This work systematically investigates the microstructure-property relationship in Mg alloys, particularly focusing on the impact of grain size and texture on material strengthening, hardening, plastic anisotropy, and tension-compression asymmetry. High resolution crystal plasticity modeling is used to understand these effects. The study performs 528 three-dimensional finite element calculations with varied textures, grain sizes, loading orientations, and loading states. The results show that the grain size effect follows the Hall-Petch relation and is influenced by both loading orientation and initial texture. The study also predicts non-monotonic effects of grain size and texture on material ductility.
This work systematically investigates the microstructure-property relationship in Mg alloys. Emphasis is placed on understanding, through high resolution crystal plasticity modeling, how grain size and texture collectively impact material strengthening and hardening, net plastic anisotropy, and tension-compression asymmetry. To achieve this, 528 fully three-dimensional finite element calculations are performed, which comprise eleven textures, four grain sizes, six loading orientations, and two uniaxial loading states (tension and compression). The grain size effect follows Hall-Petch relation that depends on both, loading orientation and initial texture. The reduction in extension twinning with grain size refinement is influenced by texture as well. Below a threshold textural strength, grain size refinement leads to an appreciable reduction in the net plastic anisotropy at yield, quantified using Hill anisotropy, and reduced tension-compression asymmetry. Using a micromechanical basis, the effect of grain size and texture on material ductility is predicted to be non-monotonic. The computational predictions serve as synthetic data sets for experimental validation and reduced-order modeling.(c) 2023 Chongqing University. Publishing services provided by Elsevier B.V. on behalf of KeAi Communications Co. Ltd.This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ) Peer review under responsibility of Chongqing University
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