(中南大学 材料科学与工程学院， 长沙 410083)
摘 要: 利用人工神经网络对7055铝合金二次时效热处理工艺参数与时效性能样本集进行训练和学习， 采用改进的BP网络算法Levenberg-Marquardt算法， 建立7055铝合金二次时效热处理工艺BP神经网络模型。 针对二次时效工艺特点， 研究的工艺参数包括: 预时效温度、 预时效时间、 二次时效温度和二次时效时间。 结果表明: 神经网络预测值与实验值吻合较好， 说明神经网络模型具有较高的精度及良好的泛化能力, 可有效地用于预测和分析二次时效工艺参数对7055铝合金时效性能的影响。
关键字: 7055铝合金； 二次时效； 人工神经网络； Levenberg-Marquardt算法
（School of Materials Science and Engineering, Central South University,
Changsha 410083, China）
Abstract: A model was developed for modeling the correlation between process parameters of second aging treatment and properties of 7055 Al alloy by applying the artificial neural networks (ANN). According to the feature of second aging, the process parameters were preliminary aging temperature, preliminary aging time, second aging temperature and second aging time. The model was based on error back-propagation (BP) algorithm and trained by Levenberg-Marquardt training algorithm. After the ANN model was trained successfully, the model achieved a very good performance. The results show that the model has high precision and good generalization performance, and can be successfully used to predict and analyze the influence of secondary aging treatment on the mechanical properties of 7055 Al alloy.
Key words: 7055 Al alloy; secondary aging; neural networks; Levenberg-Marquardt algorithm
