Computational study of laser beam shaping in directed energy deposition using a thermal-fluid – Microstructure model

Abstract

Laser beam shaping significantly influences solidification microstructure evolution in directed energy deposition (DED-LB), with distinct effects on grain morphology and crystallographic texture. To enable quantitative prediction and mechanistic understanding of these beam shaping effects on solidification microstructure evolution in both welding and metal additive manufacturing, an optimized thermal-fluid – microstructure coupling framework was developed. The integrated model incorporates novel features, including spatiotemporal optimization, efficient thermal-to-microstructural data interpolation (158x faster), CPU-parallelized grain growth algorithms (3.17x speedup), and adaptive time-step size calculation. Single-track experiments and corresponding simulations were performed for both laser-induced melting and laser-based directed energy deposition (DED-LB) using uniform circular and uniform square laser beam intensity profiles. The resulting crystallographic texture and grain morphology were quantitatively characterized through cross-sectional analysis, pole figures, and statistical distributions. Excellent agreement was achieved between experiments and simulations, with texture index deviations below 10.8% and accurate reproduction of grain size distributions demonstrating the model's fidelity. For the chosen process parameters, the two beam shapes have measurable but limited influence on texture development, with variations ranging from −5.3% to +5.1%. However, beam shape had a much stronger impact on grain morphology than on texture: circular beams refined the bulk grain-area and aspect-ratio distributions relative to square beams, while square beams yielded smaller mean grain areas, highlighting the need for distribution-level metrics beyond simple averages. By linking these morphological trends to beam-shape-dependent variations, the presented framework serves as a predictive tool for microstructure-aware process optimization in laser-based additive manufacturing.