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运用Gleeble-1500热模拟机对Ti600合金的圆柱试样进行等温压缩变形试验,以试验所得数据(变形温度800～1100℃,应变速率0.01～10s-1)为基础,基于BP神经网络方法建立了该合金的高温本构关系模型。结果表明:BP神经网络本构关系模型具有很高的预测精度,可以很好地描述Ti600合金在高温变形时各热力学参数之间高度非线性的复杂关系,为本构关系模型的建立提供了一种更加准确有效的方法。
The isothermal compressive deformation test of Ti600 alloy cylindrical specimens was carried out by using Gleeble-1500 thermal simulator. Based on the experimental data (deformation temperature 800 ~ 1100 ℃, strain rate 0.01 ~ 10s-1) and BP neural network method The high temperature constitutive model of the alloy. The results show that the constitutive model of BP neural network has a high prediction accuracy and can well describe the highly nonlinear complex relationship between the thermodynamic parameters of Ti600 alloy under high temperature deformation. It provides a model of constitutive relation A more accurate and effective method.