A high-accuracy dynamic constitutive relation of die-cast Alâ¿¿Si aluminium alloy

流动应力 材料科学 合金 本构方程 铝 模具(集成电路) 应变率 铝合金 变形(气象学) 拉伸试验 压力(语言学) 冶金 极限抗拉强度 铸造 压铸 复合材料 结构工程 工程类 有限元法 纳米技术 语言学 哲学
作者
Xueqiang Wang,Lingyang Yuan,Gang Xiao,Liming Peng,Siping Li
出处
期刊:International Journal of Mechanical Sciences [Elsevier BV]
卷期号:251: 108304-108304 被引量:9
标识
DOI:10.1016/j.ijmecsci.2023.108304
摘要

Die-cast Al–Si aluminium alloys are increasingly used in the lightweight design of automobiles; their mechanical properties under dynamic loading are crucial and thus must be investigated. The original Johnson–Cook (J–C) constitutive model does not accurately predict the mechanical properties of this alloy, which further affects the reliability of subsequent analysis and calculations. To improve the predictive accuracy of the constitutive model and investigate the dynamic mechanical properties of this alloy, high-speed tensile tests of die-casting aluminium alloy Al–10Si–yCu–xMn–zFe are conducted. The tests are executed at room temperature using a universal electrical testing machine and a high-velocity testing system in a wide strain rate range of 1–800 s−1. The tensile deformation behaviour of this alloy at various strain rates is investigated. The experiments show that the flow stress of the alloy gradually increases with strain and varies significantly with the strain rate. The J–C model is unsuitable for predicting the flow stress of this alloy owing to its unsatisfactory goodness-of-fit. To accurately predict the flow stress of die-cast Al–Si aluminium alloys, we propose a new model based on experiments. Unlike other models, the proposed model incorporates the critical strain and flow stress limit value, which allows the flow stress to be predicted more precisely at various strain rates over a wide strain range of 0–0.17. The experimental data of other die-cast Al–Si alloys validate our model. The goodness-of-fit of the proposed model for this alloy is higher than that of the J–C model, which demonstrates that our model outperforms existing models in terms of estimation accuracy.
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