Real-time 3D temperature field reconstruction for aluminum alloy forging die using Swin Transformer integrated deep learning framework

Temperature field distribution in forging dies is crucial for quality control and defect prevention, particularly for aluminum alloys. Current methods are limited to discrete points or surface measurements, making real-time three-dimensional temperature field acquisition challenging. In this paper, a novel Swin Transformer- integrated deep learning framework is proposed for real-time 3D temperature field reconstruction of forging dies, pioneering the application of transformer architecture in physical field prediction. In this framework, numerical simulations are first conducted to provide ground truth and fundamental insights into the temperature evolution, and then limited sparse thermal sensors are utilized to offer corrected real-time input parameters. The model for 3D temperature field reconstruction is developed through the combination of Swin Transformers with the U-shaped encoder-decoder structure, which is trained and tested with various sensor configurations, initialization methods, and datasets, including actual experiments. The results demonstrate that the proposed Swin-UNETR model achieves 3D temperature field prediction with time cost of 0.98 s per frame, mean absolute error of 0.8658 degrees C, showing a 17.23 % improvement over the next best CNN-based model (ResUNet3D at 1.0461 degrees C), and a 4.63 % improvement over the next best machine learning model (LightGBM at 0.9078 degrees C), which can be attributed to the Swin Transformer's ability to capture both local and global contextual information and shifted window mechanism. The proposed method holds significant implications for ensuring the forming quality of forgings and propelling the development of digital twin technology in forging processes.