在Gleeble?3500热模拟仪上进行热压缩实验,研究在变形温度为623~773 K、应变速率为0.01~20 s?1时均匀化状态下Al?6.2Zn?0.70Mg?0.30Mn?0.17Zr合金的热变形行为.实验结果表明:变形过程中流变应力值随应变速率的减小或变形温度的升高而减小.为研究热压缩过程合金的流变行为,同时建立了应变补偿本构模型与人工神经网络模型.计算结果表明:热压缩过程中各个材料常数与应变之间的关系可分别用6次多项式描述;隐含层含有16个神经元的神经网络模型具有好的预测效果.采用应变补偿本构模型和神经网络模型对流变应力进行预测,预测值平均绝对误差分别为3.49%和1.03%,神经网络模型预测精度与效率均高于应变补偿本构模型.
The hot deformation behavior of Al?6.2Zn?0.70Mg?0.30Mn?0.17Zr alloy was investigated by isothermal compression test on a Gleeble?3500 machine in the deformation temperature range between 623 and 773 K and the strain rate range between 0.01 and 20 s?1. The results show that the flow stress decreases with decreasing strain rate and increasing deformation temperature. Based on the experimental results, Arrhenius constitutive equations and artificial neural network (ANN) model were established to investigate the flow behavior of the alloy. The calculated results show that the influence of strain on material constants can be represented by a 6th-order polynomial function. The ANN model with 16 neurons in hidden layer possesses perfect performance prediction of the flow stress. The predictabilities of the two established models are different. The errors of results calculated by ANN model were more centralized and the mean absolute error corresponding to Arrhenius constitutive equations and ANN model are 3.49% and 1.03%, respectively. In predicting the flow stress of experimental aluminum alloy, the ANN model has a better predictability and greater efficiency than Arrhenius constitutive equations.
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