Implementation of specifically designed deep neural networks for the prediction and optimization of tensile properties of aluminum-copper alloy

Alloys are engineered materials aimed at enhancing mechanical properties. Extensive research has focused on identifying the optimal metal composition for alloys with superior tensile strength. This study validates the stiffness and strength values of an aluminum-copper alloy through a comparison with a molecular dynamics simulation. Subsequently, 100 data points were obtained from the simulation, and a deep neural network (DNN) with three hidden layers was employed. The DNN was trained, tested, and its structure optimized using the Taguchi design of experiment. The proposed DNN structures successfully predicted the maximum values of the stiffness and strength, which were further verified using molecular dynamics simulation. Notably, the results demonstrated the complete reliability of the Taguchi-designed DNN algorithm in this application.