Material-agnostic temperature field prediction for metal additive manufacturing via a parametric PINN framework

Abstract:Accurate temperature field prediction in metal additive manufacturing (AM) is essential for understanding the process-structure-performance relationship. While prior studies have explored generalization to 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 generalization across unseen 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 with numerical simulations across diverse metal alloys, including both in-distribution and out-of-distribution cases, demonstrate effective 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 clarify each component's contribution and scrutinize the severe training instability prevalent in conventional parametric PINNs. Overall, the proposed framework provides an efficient and scalable material-agnostic solution for temperature field modeling, contributing to more flexible and practical deployment in metal AM.

Machine Learning (cs.LG); Applied Physics (physics.app-ph); Computational Physics (physics.comp-ph)

arXiv:2604.14562 [cs.LG]

(or arXiv:2604.14562v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2604.14562 Focus to learn more arXiv-issued DOI via DataCite

https://doi.org/10.1016/j.addma.2026.105289 Focus to learn more DOI(s) linking to related resources

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