基于神经网络的Haynes 282合金高温流动行为表征及其有限元应用
作者:
作者单位:

1.西京学院机电技术系,西安 710021;2.咸阳师范学院物理与电子工程学院,咸阳 712000

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中图分类号:

TG132.3

基金项目:

陕西省重点研发计划项目(2018NY-158);西京学院校基金(XJ170131)


Characterization of Hot Flow Behavior of Haynes 282 Alloy Based on Artificial Neural Network and Its Finite Element Application
Author:
Affiliation:

1.Department of Mechatronics, Xijing University, Xi’an 710021;2.College of physics and Electronic Engineering, Xianyang Normal University,Xianyang 712000

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    摘要:

    通过在热/力学模拟试验机上开展等温压缩试验获得了Haynes 282合金的真应力-应变数据。Haynes 282合金在高温变形过程中表现出显著的动态再结晶特性,其流动应力对热力参数敏感度较高,且与热力参数呈复杂的非线性关系。为了准确地描述和预测Haynes 282合金的真应力-应变关系,将热变形参数作为输入,将流动应力作为输出构建了反向传播神经网络。对神经网络的评估结果表明所构建的神经网络能够精确地表征Haynes 282合金的高温流动行为。通过将构建的神经网络以材料子程序的形式植入有限元软件中,建立等温压缩试验有限元模型,实现了Haynes 282合金高温流动行为的精确仿真。

    Abstract:

    In this paper, the true stress-strain data of Haynes 282 alloy were obtained by conducting isothermal compression tests on a thermal-mechanical simulator.Haynes 282 alloy shows typical dynamic recrystallization characteristic during the deformation process at elevated temperature.Moreover, the flow stress was quite sensitive to the thermodynamic parameters and represents complicated highly-nonlinear relationship with the thermodynamic parameters. In order to accurately describe and predict the true stress-strain relationship of Haynes 282 alloy, a back-propagation neural network was constructed by employing hot deformation parameters as the inputs, and employing flow stress as the output. The evaluation results of the constructed neural network show that the constructed neural network in this research can accurately characterize the hot flow behavior of Haynes 282 alloy. Accurate simulation of the hot flow behavior of Haynes 282 alloy is achieved by implanting the neural network into a finite element software in the form of material subroutine and constructing the finite element model of isothermal compression test.

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引用本文

叶青,陈博,倪恒,寇晨.基于神经网络的Haynes 282合金高温流动行为表征及其有限元应用[J].宇航材料工艺,2022,52(2):116-122.

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历史
  • 收稿日期:2020-12-11
  • 最后修改日期:2022-03-04
  • 录用日期:2021-02-18
  • 在线发布日期: 2022-04-20
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