Deep learning based heat transfer simulation of the casting process.

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

SUBMITTER: Kang J 

PROVIDER: S-EPMC11585552 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature