Abstract
To avoid the necessity of constitutional models, computational intensity, and the time-consuming nature inherent in numerical simulations, a pioneering approach utilizing deep learning techniques has been adopted to swiftly predict temperature fields during the solidification phase of casting processes. This methodology involves the development of rapid prediction models based on modified U-net network architectures, augmented by the integration of Inception and CBAM (Convolutional Block Attention Module) modules. The construction of the training set involved utilizing 200 diverse geometric models with each containing three kinds of components (casting, mold, and chill), where the temperature fields at a specific time, t(i), were input data, while that of the subsequent time point, t(i+1), served as the corresponding labels. The geometric models were generated by the erosion of 2D arbitrary shapes through an erosion method, and then their associated temperature fields were obtained via FDM-based numerical simulation. The trained deep learning models exhibit proficiency in promptly forecasting temperature fields during the solidification process for arbitrarily shaped castings at different times. The average accuracy of the predicted outcomes reaches 94.5% as the absolute temperature error set as 7 ℃ and the prediction just takes one second for a time step. Notably, these models are adept at handling multi-component with multi-materials within a geometry model, such as casting, chill, and mold corresponding to the intricate casting process.