摘要
本构方程是描述材料变形的基本信息和有限元模拟中不可缺少的数学模型,反映了流动应力与应变、应变速率以及温度之间的相互关系。文章运用Gleeble-1500热模拟机对Ti-22Al-25Nb钛合金试样进行等温压缩变形试验,以试验所得数据(变形温度940℃～1030℃,应变速率0.001s-1～1s-1)为基础,采用BP神经网络的方法建立了该合金的高温本构关系,并与传统回归拟合的方法计算出的结果进行了对比。结果表明,BP神经网络本构关系模型的预测精度明显优于传统公式的计算结果,而且模型还可以很好地描述该合金在高温变形时,各热力学参数之间的复杂非线性关系,为该合金本构关系方程模型的建立,提供了一种便捷有效的方法。
Constitutive equation reflected the highly nonlinear relationship of flow stress as function of strain, strain rate and temperature is a necessary mathematical model used to describe basic information of materials deformation and finite simulation. In this paper, hot compression experiments on Ti-22Al-25Nb alloy were conducted with Gleeble 1500 thermal simulator at different temperatures and strain rats. High temperature constitutive predicting model is developed using BP neural network method based on the data obtained from experiments （deformation temperature 940℃ -1030℃ and strain rate 0. 001s^-1 -1s^-1 ）, and compared with the traditional regression method. It is found that the neural network model provides a better representation of the test data than the commonly used traditional mathematics model. Moreover, in that the complicated nonlinear relationship of thermodynamical parameters can well be described by the network model when the alloy is deformed at high temperature, it is not only a convenient but also effective way to establish the model of constitutive equations for alloys.
出处
《塑性工程学报》
CAS
CSCD
北大核心
2009年第3期126-129,共4页
Journal of Plasticity Engineering
基金
国家“973”计划(2007CB613807)
新世纪优秀人才支持计划(NCET-07-0696)
作者简介
孙宇E-mail：sunyu．npu@gmail．com孙宇，男，1983年生，博士，黑龙江哈尔滨人，主要从事钛合金材料数据库专家系统的开发与研究
