  Accurately predicting the temperature field in metal additive manufacturing
(AM) processes is critical to 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 and online control in iterative design scenarios.
Conversely, machine learning models rely heavily on high-quality datasets,
which can be costly and challenging to obtain within the metal AM domain. Our
work addresses this by introducing a physics-informed neural network framework
specifically designed for temperature field prediction in metal AM. This
framework incorporates a physics-informed input, physics-informed loss
function, and a Convolutional Long Short-Term Memory (ConvLSTM) architecture.
Utilizing real-time temperature data from the process, our model predicts 2D
temperature fields for future timestamps across diverse geometries, deposition
patterns, and process parameters. We validate the proposed framework in two
scenarios: full-field temperature prediction for a thin wall and 2D temperature
field prediction for cylinder and cubic parts, demonstrating errors below 3%
and 1%, respectively. Our proposed framework exhibits the flexibility to be
applied across diverse scenarios with varying process parameters, geometries,
and deposition patterns.
