Physics-informed machine learning-based real-time long-horizon temperature fields prediction in metallic additive manufacturing
Guardado en:
| 发表在: | Communications Engineering vol. 4, no. 1 (Dec 2025), p. 168 |
|---|---|
| 主要作者: | |
| 其他作者: | , , , , |
| 出版: |
Springer Nature B.V.
|
| 主题: | |
| 在线阅读: | Citation/Abstract Full Text Full Text - PDF |
| 标签: |
没有标签, 成为第一个标记此记录!
|
| 摘要: | Real-time long-horizon temperature prediction in wire arc additive manufacturing is critical for process control and quality assurance. However, finite element methods are computationally expensive, and the existing data-driven models suffer from error accumulation and poor adaptability. Here we propose a physics-informed geometric recurrent neural network that integrates geometric characteristics and physical constraints, captures spatiotemporal characteristics via convolutional long short-term memory cells, and enforces physical consistency through hard-encoding initial/boundary conditions and physics-informed loss function. The model can predict the temperature field for future 1.25 s based on current 1.25 s data, and has also been evaluated for more long-horizon predictions. Transfer learning was used to enhance the model’s efficiency in practical applications. Results demonstrate that the proposed model achieves 4.5−13.9% maximum prediction error in simulations and experimental data. Including geometric characteristics and physical information reduces maximum error by about 1%, while the integrated model lowers it by 4%. Furthermore, transfer learning reduces the training time by approximately 50% while achieving the same loss level.Real-time long-horizon temperature prediction in metal additive manufacturing is critical for process control and quality assurance. Mingxuan Tian and colleagues propose a physics-informed machine learning model to predict temperature field for future 1.25 s. |
|---|---|
| ISSN: | 2731-3395 |
| DOI: | 10.1038/s44172-025-00501-7 |
| Fuente: | Science Database |