首页|基于SCA与BPANN模型的近βTi-55511合金热压缩流变行为对比

基于SCA与BPANN模型的近βTi-55511合金热压缩流变行为对比

Comparison of flow behaviors of near beta Ti-55511 alloy during hot compression based on SCA and BPANN models

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通过热压缩实验研究Ti-55511合金在变形温度973～1123 K、应变速率0.01～10 s-1条件下的流变行为.采用应变补偿Arrhenius(SCA)和反向传播人工神经网络(BPANN)方法对该热变形过程本构关系进行建模,并通过统计分析和交叉验证对模型进行评估.将两种模型扩展的应力、应变数据植入有限元,仿真热压缩实验过程.结果表明:流变应力对温度和应变速率敏感,随应变速率的增加或温度的降低而增加.尽管5次多项式拟合的SCA模型和12个神经元的BPANN模型均能描述合金流变行为,但BPANN拟合精度高于SCA.16次交叉验证测试也证实BPANN模型具有较高的预测精度.两种模型应用于仿真均有效可行,但BPANN模型在仿真精度上有较大优势.
The flow behavior of Ti-55511 alloy was studied by hot compression tests at temperatures of 973-1123 K and strain rates of 0.01-10 s-1. Strain-compensated Arrhenius (SCA) and back-propagation artificial neural network (BPANN) methods were selected to model the constitutive relationship, and the models were further evaluated by statistical analysis and cross-validation. The stress-strain data extended by two models were implanted into finite element to simulate hot compression test. The results indicate that the flow stress is sensitive to deformation temperature and strain rate, and increases with increasing strain rate and decreasing temperature. Both the SCA model fitted by quintic polynomial and the BPANN model with 12 neurons can describe the flow behaviors, but the fitting accuracy of BPANN is higher than that of SCA. Sixteen cross-validation tests also confirm that the BPANN model has high prediction accuracy. Both models are effective and feasible in simulation, but BPANN model is superior in accuracy.

Ti-55511 alloyflow stressArrhenius constitutive equationback-propagation artificial neural networkfinite element

史双喜、刘秀生、张晓泳、周科朝

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中南大学 粉末冶金国家重点实验室,长沙 410083

武汉材料保护研究所,武汉 430030

中南大学 深圳研究院,深圳 518057

Ti-55511合金 流变应力 Arrhenius本构方程 反向传播人工神经网络 有限元

authors are grateful for the financial supports from the National Natural Science Foundation of ChinaGuang-dong Province Key-Area Research and Develop-ment Program,China

518712422019B010943001

2021

10.1016/S1003-6326(21)65606-6

中国有色金属学报(英文版)
中国有色金属学会

中国有色金属学报(英文版)

CSTPCDCSCDSCI
影响因子：1.183
ISSN：1003-6326
年,卷(期)：2021.31(6)
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