冷拉拔对Al-Zr-(RE)合金硬度的影响及人工神经网络预测

Effect of cold drawing on Vickers hardness of Al-Zr-(RE) alloys and artificial neural network prediction

通过维氏硬度测量和透射电镜(TEM)观察研究冷拉拔对Al-Zr-(RE)合金组织与性能的影响。结果表明:铝锆合金中Sc、Er的添加可以有效细化晶粒,改善第二相的析出,且析出的弥散Al3(Sc,Zr)相能够抑制再结晶,钉扎在冷拉拔过程中产生的位错阻碍位错运动,提高材料的硬度。在实测得到的维氏硬度值的基础上,采用误差反向传播(BP)算法训练人工神经网络,建立以变形量和稀土元素添加量为输入参数和维氏硬度为目标函数的网络。网络训练值与实验值较吻合,相关系数R达到0.992 1,用建立的网络进行仿真,仿真的相关系数为0.979 3,证明了网络的可靠性与良好的泛化推广能力。

The effects of cold drawing on the microstructure and properties of Al-Zr-（RE） alloys were studied by the Vickers hardness measurement and transmission electron microscope （TEM） observation. The results show that the elements Sc and Er have the ability of refining grains and promote the precipitation of the Al3（Sc, Zr） particles. This dispersed precipitates can pin the dislocations forming during cold drawing and hinder the movement of dislocations, thereby improve the hardness of alloys. By measuring the Vickers hardness of different alloys under different deformations, an artificial neural network （ANN） based on the error back propagation is built to find the relationship of them. The results of ANN model have a good agreement with the experimental values. The correlation coefficient of observed values and training ones is 0.992 1, and the correlation coefficient of observed values and simulation ones is 0.979 3, showing a good generalization ability and outreach capacity.