摘要
采用常温拉伸实验,研究了热暴露对欠时效态Al-Cu-Mg-Ag合金力学性能与微观组织的影响。使用BP神经网络方法,建立了热暴露温度、时间与合金力学性能的模型。基于实验相关数据,使用建立的BP神经网络模型对合金的力学性能进行预测。预测结果表明,BP神经网络能够很好的反映工艺参数与力学性能的关系,预测精度高,具有很强推广能力。
The effect of long thermal exposure on the mechanical properties and microstructures of an underaged Al-Cu-Mg-Ag alloy was studied by tensile test at room temperature. Using BP neural network method, a model of the heat exposure temperature, time and alloy mechanical properties was established. Based on experimental data, the mechanical properties of the alloy were forecasted by the BP neural network model. The results show that the BP neural network can better reflect the relationship between process parameters and experiment results with a high prediction accuracy and a strong generalization ability.
出处
《铸造技术》
CAS
北大核心
2013年第9期1147-1150,共4页
Foundry Technology
