Data-driven multi-objective property model prediction in Al-Cu alloys

Cast aluminum alloys are widely used in aerospace, automotive and other industries due to their excellent mechanical properties. However, traditional alloy design faces challenges such as vast composition space, high costs of trial-and-error experiments and difficulty in predicting the nonlinear relationship between composition and properties. This paper proposes a machine learning model that combines backpropagation neural networks, principal component analysis, and genetic algorithms for multi-objective property prediction of cast aluminum alloys. The model establishes the relationship between alloy composition and properties through the nonlinear mapping of backpropagation neural networks, reduces dimensionality via principal component analysis, and optimizes network parameters using genetic algorithms-thereby improving prediction accuracy and training efficiency. The results show that the optimized model has mean squared error of 36.28, correlation coefficient of 0.91, and mean absolute error of 2.44. In the experimental verification of ultimate strength, yield strength, and elongation after fracture, the error between experimental values and predicted values is controlled within the range of ±5%. This high prediction accuracy demonstrates the efficiency and reliability of the proposed model.