通过对实验Al-Zn-Mg-Cu-Zr-Ag合金不同温度下(90℃～150℃)时效得到的硬度和导电率数据进行了神经网络建模,发现在目标函数为0.3,隐层节点数为5,学习率为0.15时,系统误差较小.利用所建立的网络模型预测不同时效状态下材料的硬度和导电率值,发现预测数据与实验数据吻合良好(总误差3.5%),为铝合金时效性能预测和控制提供了1条新途径.
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