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Accurately predicting the temperature field in metal additive manufacturing(AM) processes is critical to preventing overheating, adjusting processparameters, and ensuring process stability. While physics-based computationalmodels offer precision, they are often time-consuming and unsuitable forreal-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. Ourwork addresses this by introducing a physics-informed neural network frameworkspecifically designed for temperature field prediction in metal AM. Thisframework incorporates a physics-informed input, physics-informed lossfunction, and a Convolutional Long Short-Term Memory (ConvLSTM) architecture.Utilizing real-time temperature data from the process, our model predicts 2Dtemperature fields for future timestamps across diverse geometries, depositionpatterns, and process parameters. We validate the proposed framework in twoscenarios: full-field temperature prediction for a thin wall and 2D temperaturefield prediction for cylinder and cubic parts, demonstrating errors below 3%and 1%, respectively. Our proposed framework exhibits the flexibility to beapplied across diverse scenarios with varying process parameters, geometries,and deposition patterns.

Real-Time 2D Temperature Field Prediction in Metal Additive Manufacturing Using Physics-Informed Neural Networks