Optimizing the cellular automata finite element model for additive manufacturing to simulate large microstructures

This study proposes an implementation of the cellular automata finite element (CAFE) model in order to optimize its performance for simulating the solidification of additively manufactured (AM) materials. The translating melt pool in AM leads to a highly localized region of activity at any given time. The time scale associated with the evolving temperature field is much larger than the time scale associated with grain solidification. A separation of temporal scales is proposed such that solidification analysis is treated independently as sub-cycles within each discrete time step of the scanning laser. At each laser time step, all of the domain is idle except a small portion, which is the region where the material is undercooled but has yet to be solidified. This region is identified, and a second partition of the computational resources is established such that the solidification computations are evenly balanced. Numerical studies demonstrate that the proposed implementation achieves dramatic improvement in parallel scalability than previously reported and thus advances the state-of-the-art of CAFE modeling for AM in terms of computational efficiency. This improved computational performance is necessary to simulate the large 3D polycrystalline microstructures required to evaluate texture and grain morphology, and two of such simulations are presented. The model is validated for laser powder bed fusion 316L stainless steel, where the polycrystalline grain morphology and crystallographic texture of the simulated microstructure closely match experimental characterization data. A second simulation is performed for a different scan pattern in order to highlight and evaluate its effect on microstructural features. Published by Elsevier Ltd on behalf of Acta Materialia Inc.

Automated summary

This study introduces the implementation of the cellular automata finite element (CAFE) model for simulating the solidification of additively manufactured materials. The proposed separation of temporal scales improves the computational efficiency and scalability of the model, leading to more accurate simulations of polycrystalline microstructures. The model is validated for 316L stainless steel and further evaluated for different scan patterns to assess their effects on microstructural features.