应用基于人工神经网络建立的新型物理图形预测Al-Zn-Mg-Cu合金固溶过程的组织演变（英文）
发布时间：2018-08-19 15:26
【摘要】：采用原位电阻测试法、金相显微镜观察、扫描电镜观察、透射电镜观察和拉伸测试技术研究固溶条件对Al-Zn-Mg-Cu合金显微组织和拉伸性能的影响。基于实验数据建立人工神经网络模型,将该模型用于预测实验合金在固溶过程中的电阻率变化。结果表明,所建立的模型能很好地预测合金在固溶过程中的电阻率变化。预测结果与实验值的相关系数为0.9958,相对误差为0.33%。采用预测数据可以建立一种新型的"固溶-电阻率"物理图形。该图形显示,实验合金的最佳固溶温度区间为465~475℃,保温时间为50~60 min;在该区间内第二相的溶解与再结晶对合金性能的影响将达到平衡。
[Abstract]:The effect of solution conditions on the microstructure and tensile properties of Al-Zn-Mg-Cu alloy was investigated by in-situ resistance test, metallographic microscope, scanning electron microscopy, transmission electron microscopy and tensile test. An artificial neural network model was established based on the experimental data, and the model was used to predict the resistivity change of the experimental alloy during the solution process. The results show that the model can well predict the resistivity change of the alloy during the solution process. The correlation coefficient between the predicted results and the experimental values is 0.9958, and the relative error is 0.33. A new physical pattern of solid solution-resistivity can be established by using prediction data. The figure shows that the optimum solution temperature range is 465 鈩，
本文编号：2192073
[Abstract]:The effect of solution conditions on the microstructure and tensile properties of Al-Zn-Mg-Cu alloy was investigated by in-situ resistance test, metallographic microscope, scanning electron microscopy, transmission electron microscopy and tensile test. An artificial neural network model was established based on the experimental data, and the model was used to predict the resistivity change of the experimental alloy during the solution process. The results show that the model can well predict the resistivity change of the alloy during the solution process. The correlation coefficient between the predicted results and the experimental values is 0.9958, and the relative error is 0.33. A new physical pattern of solid solution-resistivity can be established by using prediction data. The figure shows that the optimum solution temperature range is 465 鈩，
本文编号：2192073
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