Elongation Prediction of Die-Cast Aluminum Alloy Based on 3D Convolutional Neural Network Model

This study aims to predict the impact of porosities on the variability of elongation in the casting Al-10Si-0.3Mg alloy using machine learning methods. Based on the dataset provided by finite element method (FEM) modeling, two machine learning algorithms including artificial neural network (ANN) and 3D convolutional neural network (3D CNN) were trained and compared to determine the optimal model. The results showed that the mean squared error (MSE) and determination coefficient (R²) of 3D CNN on the validation set were 0.01258/0.80, while those of ANN model were 0.28951/0.46. After obtaining the optimal prediction model, 3D CNN model was used to predict the elongation of experimental specimens. The elongation values obtained by experiments and FEM simulation were compared with that of 3D CNN model. The results showed that for samples with elongation smaller than 9.5%, both the prediction accuracy and efficiency of 3D CNN model surpassed those of FEM simulation.