摘要
在预设实验条件下,利用Gleeble-3500D热模拟机,完成了钛合金TC4高温超塑性拉伸试验。然后利用处理高度非线性问题的高斯回归技术,借助MATLAB语言编程,对高温超塑性拉伸过程中的流变应力进行了预测,其平均绝对误差0.67 MPa,平均相对误差2.91%。与神经网络预测结果相比,其预测精度更高且简单易行,是钛合金超塑性变形过程中参数预测和优化的可行工具。
Under the prepared experimental conditions, high temperature superplastic tensile experiments of TC4 were finished using Gleeble-3500D thermal simulation instrument. The flow stress of TC4 alloy was forecasted using of Gaussian regress technology which is appropriate for highly nonlinear problems, and with the help of the programme of MATLAB language. The results show that the mean absolute error is 0.67 MPa, the mean relative error is 2.91%. Compared with neural network, GP prediction accuracy is higher and simpler, it＇s good for forecasting and optimizing the parameters of superplastic deformation process of titanium alloy.
出处
《热加工工艺》
CSCD
北大核心
2011年第20期34-36,42,共4页
Hot Working Technology
关键词
钛合金超塑性
流变应力
高斯回归技术
GP模型
superplasticity of titanium alloy
flow stress
Gaussian regress technology
GP model
