粉末冶金Al-5.6Zn-2Mg合金在热压缩过程中加工硬化特性及本构模型的比较  

Characteristics of work hardening and constitutive models comparison of powder metallurgy Al-5.6Zn-2Mg alloy during hot compression

作　　者：KATIKA Harikrishna DAMODA R.K. DAVIDSON M.J. SEETHARAM R. KASAGANI Veera Venkata Nagaraju 

机构地区：[1]Department of Mechanical Engineering,National Institute of Technology,Warangal 506004,India [2]Department of Mechanical Engineering,The Indian Institute of Information Technology Design and Manufacturing,Kurnool 518007,India

出　　处：《Journal of Central South University》2024年第2期346-368,共23页中南大学学报（英文版）

摘　　要：在300~5000℃、应变速率为0.1~0.0001 s−1的条件下,在万能试验机上对Al-5.6Zn-2Mg铝合金进行热压缩试验,以确定动态再结晶开始时的加工硬化速率曲线σ_(c)(ε_(c))l以及关键特征σ_(c)(ε_(c))l、σ_(p)(ε_(p))和σ_(ss)与Z系数之间的相关性。使用了四个本构模型,Arrhenius模型、改进的Johnson-Cook模型(MJC)、改进的Zerilli-Armstrong模型(MZA)和开发的人工神经网络(ANN)。结果表明,ANN型模型和Arrhenius模型的相对误差的绝对平均值最低,分别为0.486%和3.36%,MZA型模型和MJC型模型的相对误差的绝对平均值较高,分别为8.84%和3.93%。由于Arrhenius模型能够处理各种因素之间的非线性关系,因此它被认为是最合适的预测模型,但在材料性质未知或实验数据有限的情况下,MJC模型可能是一种更简单的替代方法。MZA模型不适合估计热压缩时的流变应力。此外,训练最好的神经网络模型的预测性能最好,相对误差的绝对平均值为0.486%,R值为0.99。The hot compression tests of Al-5.6Zn-2Mg aluminum alloy were conducted on a universal testing machine at temperature of 300−500℃and strain rate of 0.1−0.0001 s−1.The work hardening rate curves for theσc andεc for the onset of dynamic recrystallization were identified.The correlation among the key featuresσ_(c)(ε_(c))l,σ_(p)(ε_(p))andσ_(SS),and the Z coefficient are determined.Four constitutive models include the Arrhenius-type model,modified Johnson Cook(MJC),modified Zerilli-Armstrong(MZA),and an artificial neural network(ANN)developed.The results showed that the ANN and Arrhenius-type models had the lowest AARE values of 0.486%and 3.36%,while the MZA and MJC models had higher AARE values of 8.84%and 3.93%,respectively.The Arrhenius-type model was found to be the most appropriate prediction model due to its ability to handle the nonlinear relationship among factors,but the MJC model could be a simpler alternative in cases where material properties are unknown or experimental data are limited.The MZA model was found to be unsuitable for estimating flow stress in hot compression.In addition,the highest predictive performance is seen in the best-trained ANN model,with an AARE of 0.486%and an R value of 0.99.

关 键 词：加工硬化速率曲线 本构建模 热压缩 EBSD分析 流变曲线 

分 类 号：TF125.22[冶金工程—粉末冶金]