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利用Gleeble1500D热模拟实验机对Al-0.62Mg-0.73Si合金进行等温压缩实验,分析热变形参数(温度、应变速率、应变)对流变曲线特性的影响规律,并利用BP人工神经网络模型构建了合金热变形过程中动态再结晶行为动力学模型。实验结果表明:BP人工神经网络模型能够较好的描述Al-0.62Mg-0.73Si合金热变形时的动态再结晶行为,此外,通过对BP人工神经网络模型中不同隐含层节点数条件下的预测精度分析得到,当隐含层节点数大于等于9时,BP人工神经网络模型的预测效果最佳。本研究结果可用于优化Al-0.62Mg-0.73Si合金热变形工艺参数,并为全面地研究铝合金热变形行为提供理论依据。
The isothermal compression experiments of Al-0.62Mg-0.73Si alloy were carried out on a Gleeble1500D thermal simulation machine to analyze the influence of thermal deformation parameters (temperature, strain rate and strain) on the characteristics of the rheological curve. The BP artificial neural network model was used to build the alloy Dynamic recrystallization behavior dynamics model during hot deformation. The experimental results show that the BP artificial neural network model can describe the dynamic recrystallization behavior of Al-0.62Mg-0.73Si alloy better during thermal deformation. In addition, by using BP artificial neural network model with different hidden layer nodes The prediction accuracy analysis shows that when the number of hidden layer nodes is greater than or equal to 9, BP artificial neural network model has the best prediction effect. The results of this study can be used to optimize the thermal deformation process parameters of Al-0.62Mg-0.73Si alloy, and provide a theoretical basis for a comprehensive study of the thermal deformation behavior of aluminum alloy.