应用人工神经网络研究化学元素对钛合金相变点的影响

Study on Effects of Alloying Elements on β Transus Temperature of Titanium Alloys Using Artificial Neural Network

摘要:

钛合金的化学元素与相变点之间具有复杂的非线性关系，人工神经网络(artificial neural network, ANN)是解决非线性映射关系的一种有效可行的方法。本工作以钛合金相变点与化学元素的关系为研究对象，建立钛合金相变点的BP神经网络预测模型，运用训练好的网络模型研究典型化学元素对相变点的影响规律，并与传统经验公式进行比较。结果发现：神经网络模型的预测结果精度较高，误差小。各合金元素对相变点的影响并不是传统经验公式表现出来的单调线性关系，而是由于各元素之间的交互作用引起的复杂非线性关系

Abstract:

Artificial Neural Network (ANN) is a feasible method to reflect the complicated nonlinear relationship between β transus temperature and the alloy composition. In this paper, back propagation neural network (BP neural network) was developed and trained using data from various sources of published literature. The influence of aluminum, molybdenum and zirconium on β transus temperature in titanium alloys was assessed on the base of the trained neural network. It is found that the predicted results are in good agreement with experimental values. The effect of element contents on β transus temperature simulated by ANN model presents nonlinear relationship caused by the interaction among the elements, which is different from the results of the traditional equations.