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Meeting 2026 TMS Annual Meeting & Exhibition
Symposium Additive Manufacturing Modeling, Simulation and Artificial Intelligence
Presentation Title Computational Prediction of Grain Structure Evolution in Thermoelectric Bulk Parts Processed by Laser Powder Bed Fusion
Author(s) Bengisu Sisik, Theron Rodgers, Paul Chao, Andrew Polonsky, Saniya LeBlanc
On-Site Speaker (Planned) Bengisu Sisik
Abstract Scope Additive manufacturing continues to unlock the potential for producing complex geometries with enhanced capabilities. Predictive modeling is essential to fully leverage laser powder bed fusion (PBF-LB) for thermoelectric materials that convert waste heat into electricity. This study presents the first computational prediction of microstructure formation in bulk bismuth telluride, a room-temperature thermoelectric material processed via PBF-LB. We simulate grain structure evolution during PBF-LB using the finite difference Monte Carlo method within the SPPARKS code, developed by Sandia National Laboratories. The model integrates key processing parameters, including laser power, scan speed, hatch spacing, spot size, scanning strategy, and powder layer thickness. Complex scanning strategies such as bi-directional paths and 90° inter-layer rotations are evaluated. Results show that grain size increases with volumetric energy density, while thermal gradients govern grain orientation. These insights directly link process parameters to microstructure formation, paving the way for tailored microstructure in thermoelectric parts to optimize performance.
Proceedings Inclusion? Planned:
Keywords Additive Manufacturing, Modeling and Simulation, Computational Materials Science & Engineering

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