基于物理参数和BP神经网络的9310钢本构模型研究
Research on constitutive model of 9310 steel based on physical parameters and BP neural network
王宇航 1罗拴谋 2董显娟 1徐勇 3黄龙 1涂泽立 1李佳俊1
作者信息
- 1. 南昌航空大学 航空制造工程学院,江西 南昌 330063
- 2. 西安圣泰金属材料有限公司,陕西 西安 712033
- 3. 南昌航空大学 通航学院,江西 南昌 330063
- 折叠
摘要
采用Gleeble-3800 热模拟试验机对 9310 钢进行了变形量为 70%的等温恒应变速率压缩实验,在变形温度为 800～1200℃、应变速率为 0.01～50 s-1 的范围内研究了 9310 钢的热变形行为.通过不同热变形参数对自扩散系数D和杨氏模量E的影响,建立了基于物理参数的本构模型,同时基于实验数据构建了BP神经网络本构模型.结果表明:9310 钢为负温度正应变速率敏感性材料,且流动应力随变形温度的升高和应变速率的降低而减小.基于不同条件构建的物理本构模型和BP 神经网络模型的相关系数r均大于 0.98,但BP神经网络模型的r值可达 0.996,平均绝对相对误差为 3.1%.经过流动应力曲线、相关系数和平均绝对相对误差的综合对比,得出BP神经网络模型对预测 9310 钢的流动行为具有较好的适用性.
Abstract
The Gleeble-3800 thermal simulator was used to conduct isothermal constant strain rate compression experiments on 9310 steel with deformation amount of 70%.The hot deformation behavior of 9310 steel was studied within the range of deformation temperature of 800-1200℃and strain rate of 0.01-50 s-1.Through the influence of different thermal deformation parameters on self-diffusion coeffi-cient D and Young's modulus E,the constitutive model based on physical parameters was established,and the BP neural network consti-tutive model was constructed based on experimental data.The results show that 9310 steel is negative temperature and positive strain rate sensitive material,and the flow stress decreases with the increase of deformation temperature and the decrease of strain rate.The correla-tion coefficient r of the physical constitutive model and the BP neural network model constructed under different conditions is greater than 0.98,but r value of the BP neural network model can reach 0.996,and the average absolute relative error is 3.1%.After comprehensive comparison of flow stress curve,correlation coefficient and average absolute relative error,it is concluded that the BP neural network mod-el has good applicability for predicting the flow behavior of 9310 steel.
关键词
9310钢/热变形行为/物理本构模型/BP神经网络模型Key words
9310 steel/hot deformation behavior/physical constitutive model/BP neural network model引用本文复制引用
基金项目
航空科学基金资助项目(2020Z047056003)
江西省重点研发计划项目(20202BBEL53012)
出版年
2024
NETL
NSTL
万方数据