An artificial neural network (ANN) model was used for the simulation and prediction of the mechanical properties of Sn-9Zn-Cu solder alloys. Sn-9Zn-Cu solder alloys containing different Cu contents (0, 1, 2, 3, 4 and 5 wt%) were successfully prepared by permanent mold casting. The specimens were heated in a protective argon atmosphere at 433 K for 24 h, followed by water quenching at 298 K. Finally, the heat-treated samples were aged at 373 K for different time intervals (t_a = 2, 4, 8, 16 and 32 h), followed by water quenching at 298 K. The phases present in the current alloys were detected by X-ray diffraction analysis. For morphological characterization, a scanning electron microscope operated at 20 kV was tilized. The mechanical properties of the samples were studied using hardness measurements. The variations in the hardness data with increasing aging time were determined based on the structural transformations that take place in the alloys. The ANN model was applied to the hardness measurements to simulate and predict the Vickers hardness of Sn-Zn-Cu alloys with mean square error values equal 9.55E-06 and 9.44E-06 for training and validation data respectively after 281 epochs. The simulated and predicted results were consistent with the experimental results.

Graphical abstract

中文翻译：

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基于人工神经网络的Sn-9Zn-Cu锡合金维氏硬度数学模型

人工神经网络（ANN）模型用于模拟和预测Sn-9Zn-Cu焊料合金的力学性能。通过永久模铸成功地制备了具有不同Cu含量（0、1、2、3、4和5 wt％）的Sn-9Zn-Cu焊料合金。将样品在保护性氩气气氛中以433 K加热24小时，然后以298 K进行水淬。最后，将经过热处理的样品在373 K上老化不同的时间间隔（t _a = 2、4、8、16和32 h），然后在298 K进行水淬。通过X射线衍射分析检测当前合金中的相。为了进行形态表征，使用在20kV下操作的扫描电子显微镜。使用硬度测量来研究样品的机械性能。硬度数据随着时效时间的增加而变化，这是根据合金中发生的结构转变确定的。将ANN模型应用于硬度测量，以模拟和预测281个周期后分别具有9.55E-06和9.44E-06均方误差值的Sn-Zn-Cu合金的维氏硬度，以训练和验证数据。仿真和预测结果与实验结果一致。

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