应用人工神经网络模型预测Ti＋10V-2Fe-3A合金的力学性能

采用人工神经网络方法建立了Ti-10V-2Fe-3Al合金机械性能预测的神经网络模型。模型的输入参数包括变形温度、变形程度、固溶温度、时效温度等热加工工艺参数和热处理制度。模型的输出为钛合金最重要的5个机械性能指标，即抗拉强度、屈服强度、延伸率、断面收缩率和断裂韧性。与传统回归拟合公式相比，该模型具有容错性好、通用性强等优点。该模型可以预测Ti-10V-2Fe-3Al合金在不同热加工工艺参数和热处理制度下的机械性能，也可以用于优化热加工参数和热处理制度。

Artificial Neural Network Model for the Prediction of Mechanical Properties of Ti-10V-2Fe-3Al Titanium Alloy

An artificial neural network (ANN) model is proposed to predict mechanical properties of Ti-10V-2Fe-3Al titanium alloys. The input parameters of the neural network (NN) model are deformation temperature, degree of reduction, cooling rate, solution temperature and aging temperature. The outputs of the NN model are five most important mechanical properties namely ultimate tensile strength, tensile yield strength, elongation, reduction of area, and fracture toughness. Extensive experiments for correlating forging technology to mechanical properties were conducted in Ti-10V-2Fe-3Al alloy to train the NN. Compared to the traditional regression method, the ANN model has a better compatibility and adaptability. The model can be used for the prediction of properties of Ti-10V-2Fe-3Al alloy as functions of processing parameters and heat treatment cycle. It can also be used for the optimization of the processing and heat treatment parameters.