Abstract
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 (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-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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.