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Meeting

2026 TMS Annual Meeting & Exhibition

Symposium

AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling

Presentation Title

Physics-Constrained Convolutional Neural Networks with Gradient-Aware Optimization for Real-Time Thermal Prediction in PBF Metal AM

Author(s)

Hamed Hosseinzadeh

On-Site Speaker (Planned)

Hamed Hosseinzadeh

Abstract Scope

Accurate thermal modeling in Powder Bed Fusion (PBF) is vital for predicting melt pool behavior and minimizing defects, yet conventional solvers remain too slow for real-time control. We introduce a Physics-Constrained Convolutional Neural Network (PC-CNN) equipped with a novel Gradient-Aware Optimization scheme—an enhanced Adam optimizer integrating temperature-dependent learning rates and Laplacian-based physics regularization. This method embeds thermal diffusion physics directly into the training process to produce spatially consistent temperature fields. Among 27 tested configurations, the optimal setup (T03: α = 1.0, β = 2.0, γ = 0.01) achieved ~50% RMSE reduction over baseline CNNs and preserved fine melt pool gradients. The PC-CNN delivers high-fidelity predictions at over 6000× speedup versus finite difference simulations, enabling real-time inference and digital twin integration. This physics-augmented deep learning approach demonstrates the power of domain-informed optimization for intelligent AM process modeling.

Proceedings Inclusion?

Planned:

Keywords

Additive Manufacturing, Modeling and Simulation, ICME

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