TY - JOUR
T1 - An integrated multi-scale numerical framework for predicting spatially varying anisotropy in LPBF AlSi10Mg
T2 - From molten pool dynamics to dislocation-mediated plasticity
AU - Dai, Shi
AU - Grilli, Nicolò
AU - Lian, Yanping
AU - Chen, Jiawei
AU - Li, Mingjian
AU - Dong, Chunying
N1 - Publisher Copyright:
Copyright © 2026. Published by Elsevier B.V.
PY - 2026/8/25
Y1 - 2026/8/25
N2 - This study focuses on the mechanisms underlying the anisotropic behavior of additively manufactured AlSi10Mg at the microscale. To this end, a multi-scale numerical framework is developed to predict the local mechanical properties within specific regions of the molten pool. This framework integrates a thermo-fluid flow model, a cellular automaton model, and a dislocation-based crystal plasticity finite element model. Simulations based on the predicted fusion-zone microstructures reveal significant spatial heterogeneity in mechanical properties across the molten pool. Specifically, under loading along the build direction, the material at the molten pool boundaries (sides) exhibits higher flow stress compared to the center and bottom regions. Stress–strain analyses indicate that these side regions contain a higher fraction of oblique grains oriented in “hard” crystallographic directions, resulting from the local temperature gradient and preferred growth orientations. Consistently, the evolution of dislocation density confirms a higher dislocation multiplication rate at the molten pool sides. Furthermore, a parametric study involving various processing conditions elucidates how process parameters influence the resulting mechanical response. The results indicate that the anisotropic behavior originates from the preferential distribution of crystallographic orientations induced by thermal-gradient-driven grain growth during the LPBF process. These findings suggest a process optimization strategy aimed at tailoring the grain morphology specifically by promoting and preserving inclined grains at the molten pool boundaries to enhance performance.
AB - This study focuses on the mechanisms underlying the anisotropic behavior of additively manufactured AlSi10Mg at the microscale. To this end, a multi-scale numerical framework is developed to predict the local mechanical properties within specific regions of the molten pool. This framework integrates a thermo-fluid flow model, a cellular automaton model, and a dislocation-based crystal plasticity finite element model. Simulations based on the predicted fusion-zone microstructures reveal significant spatial heterogeneity in mechanical properties across the molten pool. Specifically, under loading along the build direction, the material at the molten pool boundaries (sides) exhibits higher flow stress compared to the center and bottom regions. Stress–strain analyses indicate that these side regions contain a higher fraction of oblique grains oriented in “hard” crystallographic directions, resulting from the local temperature gradient and preferred growth orientations. Consistently, the evolution of dislocation density confirms a higher dislocation multiplication rate at the molten pool sides. Furthermore, a parametric study involving various processing conditions elucidates how process parameters influence the resulting mechanical response. The results indicate that the anisotropic behavior originates from the preferential distribution of crystallographic orientations induced by thermal-gradient-driven grain growth during the LPBF process. These findings suggest a process optimization strategy aimed at tailoring the grain morphology specifically by promoting and preserving inclined grains at the molten pool boundaries to enhance performance.
KW - Additive manufacturing
KW - Anisotropic behaviors
KW - Cellular automaton
KW - Crystal plasticity
KW - Numerical simulation
UR - https://www.scopus.com/pages/publications/105044442007
U2 - 10.1016/j.commatsci.2026.114878
DO - 10.1016/j.commatsci.2026.114878
M3 - Article
AN - SCOPUS:105044442007
SN - 0927-0256
VL - 273
JO - Computational Materials Science
JF - Computational Materials Science
M1 - 114878
ER -