(上海交通大学国家模具工程研究中心，上海 200030)
摘 要: 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。
关键字: 人工神经网络；Ti-17合金；本构关系；BP算法
（National Die and Mold CAD Engineering Research Center,
Shanghai Jiao Tong University, Shanghai 200030, P. R. China）
Abstract: 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 31530 repetitions in training . Comparison of the predicted and experimental results shows that the neural 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.
Key words: artificial neural network; Ti-17 alloy; constitutive relationship; BP algorithm
