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A. F. Abd El‑Rehim,D. M. Habashy,H. Y. Zahran,H. N. Soliman 대한금속·재료학회 2021 METALS AND MATERIALS International Vol.27 No.10
An artificial neural network (ANN) model was used for the simulation and prediction of the mechanical properties ofSn-9Zn-Cu solder alloys. Sn-9Zn-Cu solder alloys containing different Cu contents (0, 1, 2, 3, 4 and 5 wt%) were successfullyprepared 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 (ta = 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-raydiffraction analysis. For morphological characterization, a scanning electron microscope operated at 20 kV was tilized. Themechanical properties of the samples were studied using hardness measurements. The variations in the hardness data withincreasing aging time were determined based on the structural transformations that take place in the alloys. The ANN modelwas applied to the hardness measurements to simulate and predict the Vickers hardness of Sn-Zn-Cu alloys with mean squareerror values equal 9.55E-06 and 9.44E-06 for training and validation data respectively after 281 epochs. The simulated andpredicted results were consistent with the experimental results.