【摘要】 以Gleeble-1500热模拟试验机获得的Ti40钛合金压缩试验数据为基础,应用人工神经网络对数据进行训练和预测,建立该合金的高温流动应力与应变、应变速率和温度对应关系的预测模型,其中,应变、应变速率(对数形式)和变形温度作为模型的输入参数,流动应力作为模型的输出参数。结果发现,运用BP反向传播算法进行训练的神经网络模型具有良好的预测功能,其预测值与实验测量值基本吻合。同时,采用神经网络模型预测的数据构造Ti40合金的加工图,其安全区和失稳区的范围与实测数据获得的加工图基本相符,并对各自区域的相应组织状态进行金相观察。更多还原

【Abstract】 Based on the experimental data of Ti40 alloy obtained from Gleeble-1500 thermal simulator,an artificial neural network model of high temperature flow stress as a function of strain,strain rate and temperature was established.In the network model,the input parameters of the model are strain,logarithm strain rate and temperature while flow stress is the output parameter.Multilayer perceptron(MLP) architecture with back-propagation algorithm is utilized.The present study achieves a good performance of the artificial neural network(ANN) model,and the predicted results are in agreement with experimental values.A processing map of Ti40 alloy is obtained with the flow stress predicted by the trained neural network model.The processing map developed by ANN model can efficiently track dynamic recrystallization and flow localization regions of Ti40 alloy during deforming.Subsequently,the safe and instable domains of hot working of Ti40 alloy are identified and validated through microstructural investigations.更多还原