Two-dimensional temperature field prediction with in-situ data in metal additive manufacturing using physics-informed neural networks
Pouyan Sajadi, Mostafa Rahmani Dehaghani, Yifan Tang, G. Gary Wang
Abstract
Accurately predicting the temperature field in metal additive manufacturing (AM) processes is critical for preventing overheating, adjusting process parameters, and ensuring process stability. While physics-based computational models offer precision, they are often time-consuming and unsuitable for real-time predictions. Machine learning models, on the other hand, rely heavily on high-quality datasets, which can be costly and difficult to obtain in the metal AM domain. Existing studies on physics-informed neural networks (PINNs) have made progress in integrating physics with machine learning but often lack in-situ data integration, which is essential for capturing real-time thermal dynamics. Additionally, their methodologies are typically heavily dependent on specific process characteristics, limiting their flexibility. Our work addresses these gaps by introducing a PINN-based framework specifically designed for temperature field prediction in metal AM. The framework incorporates in-situ temperature data gathered during the manufacturing process, combining it with physics-informed inputs and a custom loss function. The approach is demonstrated through two case studies. In the first case, using a small set of experimental data, the model achieves an error below 3 % with a mean absolute error (MAE) of 11 °C. In the second case, using simulation data, the model achieves an error below 1 % with an MAE of 7 °C. In addition, the framework shows promising adaptability for different metal AM scenarios with different geometries, deposition patterns, and process parameters.