Effect of Graphitic Carbon Nitride Nanosheets Addition on the Microstructure and Mechanical Properties of Sn-3.5Ag-0.5Cu Solder Alloy

Modelling the Effect of Cu Content on the Microstructure and Vickers Microhardness of Sn-9Zn Binary Eutectic Alloy Using an Artificial Neural Network

The present study aims to clarify the impact of Cu addition and aging conditions on the microstructure development and mechanical properties of Sn-9Zn binary eutectic alloy. The Sn-9Zn alloys with varying Cu content (0, 1, 2, 3, and 4 wt.%) were fabricated by permanent mold casting. X-ray diffraction (XRD) and scanning electron microscopy (SEM) techniques were utilized to investigate the influence of Cu concentration on the microstructure of pre-aged Sn-9Zn-Cu alloys. The main phases are the primary β-Sn phase, eutectic α-Zn/β-Sn phases, and γ-Cu5Zn8/η-Cu6Sn5/ε-Cu3Sn intermetallic compounds. Vickers microhardness values of Sn-9Zn alloys increased with additions of 1 and 2 wt.% Cu. When the concentration of Cu exceeds 2 wt.%, the values of microhardness declined. Besides, the increase in the aging temperature caused a decrease in the microhardness values for all the investigated alloys. The variations in the microhardness values with Cu content and/or aging temperature were interpreted on the basis of development, growth, and dissolution of formed phases. The alterations of the lattice strain, dislocation density, average crystallite size, and stacking fault probability were evaluated from the XRD profiles of the investigated alloys. Their changes with Cu content and/or aging temperature agree well with the Vickers hardness results. An artificial neural network (ANN) model was employed to simulate and predict the Vickers microhardness of the present alloys. To check the adequacy of the ANN model, the calculated results were compared with experimental data. The results confirm the high ability of the ANN model for simulating and predicting the Vickers microhardness profile for the investigated alloys. Moreover, an equation describing the experimental results was obtained mathematically depending on the ANN model.