高温变形过程中Ti-6Al-2Zr-2Sn-2Mo-1.5Cr-2Nb合金的流动应力和晶粒尺寸模型

Modelling of Flow Stress and Grain Size in the High Temperature Deformation of Ti-6Al-2Zr-2Sn-2Mo-1.5Cr-2Nb Alloy

摘要:

金相和SEM观察后,采用定量分析软件测量初生α相晶粒尺寸,并且研究了变形温度和应变速率对微观组织的影响。本文采用前向型模糊神经网络模型预测Ti-6Al-2Zr-2Sn-2Mo-1.5Cr-2Nb合金等温压缩过程中的流动应力和晶粒尺寸。部分流动应力和晶粒尺寸作为样本数据用于训练模型,另一部分流动应力和晶粒尺寸作为非样本数据用于测试模型的可靠性。结果表明：模型的预测精度较高,该模型较好地描述了钛合金在高温变形过程中的流动行为和微观组织演变。

Abstract:

After the optical micrography (OM) and scanning electron microscopy (SEM) observations, the grain size of primary α phase was measured via a quantitative metallography image analysis software. The effect of deformation temperature and strain rate on the microstructure was discussed. A Pi-sigma fuzzy neural network (FNN), in which the layers of neural networks were organized into a feed-forward system, was used to predict the flow stress and the grain size during isothermal compression of Ti-6Al-2Zr-2Sn-2Mo-1.5Cr-2Nb alloy. The comparisons of the predicted flow stress and grain size for the sample data or the non-sample data with the experimental results were given to train the models and confirm the validity in present study. The results show that the accuracy of prediction from the Pi-sigma FNN models is much high, and the Pi-sigma FNN approach can efficiently describe the non-linear and complex relationship of titanium alloys.