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Microstructure-dependent deformation behaviour of a low γ′ volume fraction Ni-base superalloy studied by in-situ neutron diffraction

材料科学 体积分数 微观结构 高温合金 中子衍射 硬化(计算) 应变分配 剪切(物理) 变形机理 可塑性 加工硬化 复合材料 应变硬化指数 冶金 结晶学 晶体结构 古生物学 化学 图层(电子) 生物 构造学
作者
Nitesh Raj Jaladurgam,Hongjia Li,Joe Kelleher,Christer Persson,A. Steuwer,Magnus Hörnqvist Colliander
出处
期刊:Acta Materialia [Elsevier]
卷期号:183: 182-195 被引量:36
标识
DOI:10.1016/j.actamat.2019.11.003
摘要

Ni-base superalloys are critical materials for numerous demanding applications in the energy and aerospace sectors. Their complex chemistry and microstructure require detailed understanding of the operating deformation mechanisms and interaction between the matrix and the hardening phase during plastic deformation. Here we use in-situ neutron diffraction to show that the dependence of the deformation mechanisms and load redistribution on γ′ particle size in a Ni-base superalloy with a γ′ volume fraction of around 20 % can exhibit distinct differences compared to their high volume fraction counterparts. In particular, the load redistribution in the coarse microstructure occurs immediately upon yielding in the present case, whereas high γ′ volume fractions have been observed to initially lead to shear mediated co-deformation before work hardening allows looping to dominate and cause load partitioning at higher stresses. The fine microstructure, on the other hand, behaved similar to high volume fraction alloys, exhibiting co-deformation of the phases due to particle shearing. A recently developed elasto-plastic self-consistent (EPSC) crystal plasticity model, specifically developed for the case of coherent multi-phase materials, could reproduce experimental data with good accuracy. Furthermore, the finite strain formulation of the EPSC model allowed deformation induced texture predictions. The correct trends were predicted by the simulations, but the rate of lattice rotation was slower than experimentally observed. The insights point towards necessary model developments and improvements in order to accurately predict e.g. texture evolution during processing and effect of texture and microstructure on component properties.
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