Hybrid machine learning and microstructure-based approach for modeling relationship between microstructure and hardness of AA2099 Al−Li alloy

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(sinθ_(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 T_(1) phases along{111}Al habit planes.This effect is attributable to the variation in encountered T_(1) variants during indenter loading.Consequently,the importance ranking of microstructural features shifts depending on T_(1) phase abundance:in samples with limited T_(1) phases,D_(dis) or D_(min)>SF_(FD)>sinθ_(min),while in samples with abundant T_(1) phases,D_(dis) or D_(min)>sinθ_(min)>SF_(FD).

均匀化热处理对新型Al-Cu-Li-Ce合金的微观结构的影响（英文）

研究了一种添加Ce并具有高Cu/Li的新型Al-Cu-Li-Ce合金在双极均匀化热处理过程中微观结构的演化过程,并重点探讨了该合金中t1(Al8Cu4Ce)相的形成过程及其形成机制。在凝固过程中Ag,Mg富集的粗大的T_B(Al_7Cu_4Li)相和原生的Al Cu Ce相共同形成。双极均匀化热处理后有2种类型的t1相出现。该相的形成模式包括,通过Ce不断的由Al基体向在T B相上新形核的t1相扩散并导致该粒子不断长大,以及原生的Al Cu Ce相通过不断缩水并伴随Cu,Ce向各个方向扩散,由之转变为细小弥散的t_1相。同时分析发现在合金凝固过程中产生的这种先析Al Cu Ce相,不但能够促进晶粒形核,同时能够阻止晶粒进一步长大,最终导致含铈的铸态合金晶粒得到细化。