Abstract:A hybrid approach combining machine learning and microstructure analysis was proposed to investigate the relationship between microstructure and hardness of AA2099 Al-Li alloy through nano-indentation, X-ray diffraction (XRD) and electron backscatter diffraction (EBSD) technologies. Random forest regression (RFR) model was employed to predict hardness based on microstructural features and uncover influential factors and their rankings. The results show that the increased hardness correlates with a smaller distance from indentation to grain boundary (D_dis) or a shorter minimum grain axis (D_min), a lower Schmidt factor in friction stir weld direction (SF_FD), and higher sine values of the angle between {111} slip plane and surface (sin θ_min). D_dis and D_min emerge as pivotal determinants in hardness prediction. High-angle grain boundaries imped dislocation slip, thereby increasing hardness. Crystallographic orientation also significantly influences hardness, especially in the presence of T₁ phases along {111}_Al habit planes. This effect is attributable to the variation in encountered T₁ variants during indenter loading. Consequently, the importance ranking of microstructural features shifts depending on T₁ phase abundance: in samples with limited T₁ phases, D_dis or D_min > SF_FD > sin θ_min, while in samples with abundant T₁ phases, D_dis or D_min > sin θ_min > SF_FD.

Key words: machine learning; T₁ phase; hardness; Al-Li alloy