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A unified static and dynamic recrystallization Internal State Variable (ISV) constitutive model coupled with grain size evolution for metals and mineral aggregates
International Journal of Plasticity ( IF 9.4 ) Pub Date : 2019-01-01 , DOI: 10.1016/j.ijplas.2018.08.009
H.E. Cho , Y. Hammi , A.L. Bowman , Shun-ichiro Karato , J.R. Baumgardner , M.F. Horstemeyer

Abstract A history dependent and physically-motivated Internal State Variable (ISV) constitutive model is presented that simultaneously accounts for the effects of static recrystallization, dynamic recrystallization, and grain size with respect to the mechanical behavior under different strain rates, temperatures, and pressures. A unique aspect of our ISV constitutive model is that grain size and recrystallized volume fraction can be directly included along with its associated rate of change under deformation and time in a coupled manner. The present ISV constitutive model was calibrated to several metals (oxygen-free high conductivity copper, AZ31 magnesium alloy, pure nickel, and 1010 low carbon steel) and geological materials (olivine and clinopyroxene). The model calibration shows good agreement with the experimental stress-strain behavior and average grain size data. Validation of the ISV constitutive model was accomplished by applying complex and history sensitive thermomechanical problems once the model was calibrated: i) sequential transitions of different loading conditions and ii) a multistage tubing process. The history dependence naturally provided by ISVs enabled the present model to effectively capture the complex boundary value problems with changing boundary conditions.

中文翻译:

统一的静态和动态再结晶内部状态变量 (ISV) 本构模型与金属和矿物聚集体的晶粒尺寸演变相结合

摘要 提出了一种历史相关和物理驱动的内部状态变量 (ISV) 本构模型,该模型同时考虑了静态再结晶、动态再结晶和晶粒尺寸对不同应变率、温度和压力下机械行为的影响。我们的 ISV 本构模型的一个独特方面是晶粒尺寸和再结晶体积分数可以直接包括在变形和时间下的相关变化率,以耦合方式。目前的 ISV 本构模型已针对几种金属(无氧高电导率铜、AZ31 镁合金、纯镍和 1010 低碳钢)和地质材料(橄榄石和斜辉石)进行校准。模型校准显示出与实验应力应变行为和平均晶粒尺寸数据的良好一致性。ISV 本构模型的验证是通过在模型校准后应用复杂且历史敏感的热机械问题来完成的:i) 不同加载条件的顺序转换和 ii) 多级管道过程。ISV 自然提供的历史依赖性使本模型能够有效地捕获边界条件变化的复杂边界值问题。
更新日期:2019-01-01
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