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This paper introduces a parametric physics-informed neural network (PINN) for zero-shot thermal modeling in metal additive manufacturing (AM) that generalizes across different materials without retraining or labeled data. The framework uses a decoupled architecture to encode material properties and spatiotemporal coordinates separately, along with physics-guided output scaling and a hybrid optimization strategy. Experiments on laser powder bed fusion (LPBF) across diverse metal alloys show a 64.2% reduction in relative L2 error compared to a non-parametric baseline, achieved with significantly improved training efficiency.
Predict thermal behavior across novel metal alloys in additive manufacturing without any material-specific training data, opening the door to rapid process optimization for new materials.
Accurate thermal modeling in metal additive manufacturing (AM) is essential for understanding the process-structure-performance relationship. While prior studies have explored generalization across unseen process conditions, they often require extensive datasets, costly retraining, or pre-training. Generalization across different materials also remains relatively unexplored due to the challenges posed by distinct material-dependent thermal behaviors. This paper introduces a parametric physics-informed neural network (PINN) framework for zero-shot generalization across arbitrary materials without labeled data, retraining, or pre-training. The framework adopts a decoupled parametric PINN architecture that separately encodes material properties and spatiotemporal coordinates, fusing them through conditional modulation to better align with the multiplicative role of material parameters in the governing equation and boundary conditions. Physics-guided output scaling derived from Rosenthal's analytical solution and a hybrid optimization strategy are further incorporated to enhance physical consistency, training stability, and convergence. Experiments on bare plate laser powder bed fusion (LPBF) across diverse metal alloys, including both in-distribution and out-of-distribution cases, demonstrate effective zero-shot generalizability along with superior training efficiency. Specifically, the proposed framework achieved up to a 64.2% reduction in relative L2 error compared to the non-parametric baseline while surpassing its performance within only 4.4% of the baseline training epochs. Ablation studies confirm that the proposed framework's components are broadly applicable to other PINN-based approaches. Overall, the proposed framework provides an efficient and scalable material-agnostic solution for zero-shot thermal modeling, contributing to more flexible and practical deployment in metal AM.
