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Meeting 2026 TMS Annual Meeting & Exhibition
Symposium Additive Manufacturing Modeling, Simulation and Artificial Intelligence
Presentation Title Microstructure Validation of 316L Stainless Steel Additive Manufacturing Using CAFE with MOOSE/MALAMUTE Thermal Fields
Author(s) Tsu-Chun Teng, Dewen Yushu, Luis Nuņez, Wen Jiang
On-Site Speaker (Planned) Tsu-Chun Teng
Abstract Scope Predicting microstructure evolution in metal additive manufacturing (AM) is critical for linking process conditions to performance. This work presents a physics-based framework that combines high-fidelity thermal simulation and microstructure modeling, applied to 316L stainless steel fabricated by directed energy deposition (DED). Temperature fields generated by MOOSE/MALAMUTE were coupled to a cellular automaton (CA) model to simulate grain evolution under realistic thermal gradients and cooling rates. Grain size and aspect ratio were quantified using Python-based scikit-image and the MTEX toolbox. Simulated results were validated against EBSD measurements, showing agreement in grain morphology across regions of the printed structure. The model captures key features observed in DED AM, particularly the cellular grain structure near the top surface and columnar growth at the melt pool bottom and provides a basis for calibrating solidification models. Future work will extend the approach to multi-track simulations and incorporate adaptive thermal boundary conditions.
Proceedings Inclusion? Planned:
Keywords Additive Manufacturing, Computational Materials Science & Engineering, Iron and Steel

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