Ti-17合金本构关系的人工神经网络模型

开发了一个基于神经网络的Ｔｉ１７ 合金的本构关系模型。首先利用ＴｈｅｒｍｅｃｍａｓｔｏｒＺ 型热模拟机等温压缩Ｔｉ１７ 合金， 研究在不同变形温度、变形程度和应变速率等工艺参数条件下流动应力的变化情况。然后用实验所得的热变形工艺参数与性能间的数据训练人工神经网络。训练结束后的神经网络变成为一个知识基的本构关系模型。利用该模型预测的流动应力的值与实验结果间的误差较小。

A constitutive relationship model of Ti 17 alloy based on artificial neural network

Artificial neural networks have been applied to acquire the constitutive relationships of a Ti 17 alloy at elevated temperature, using data obtained from homogeneous compression experiments carried out on a Thermecmastor Z hot simulator. During building up the neural network model of the constitutive relationship for the alloy, deformation temperature, equivalent strain rate and equivalent strain were taken as the inputs and flow stress was taken as the output. At the same time, four layers were constructed, twelve neurons were used in the first hidden layer and eight neurons were used in the second hidden layer. The activation function in the output layer of the model obeyed a linear function, while the activation function in the hidden layer was a sigmoid function. The neural network became stable after 31 530 repetitions in training. Comparison of the predicted and experimental results shows that the nural network model used to predict the constitutive relationship of the Ti 17 alloy has good learning precision and good generalization. Meanwhile, the neural network methods are found to show much better agreement than the statistical regression methods in dealing with the experimental data.