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

基于机器学习与显微组织的AA2099铝锂合金显微组织与硬度关系建模

作　　者：Xiang-hui ZHU Xu-sheng YANG Wei-jiu HUANG Miao GONG Xin WANG Meng-di LI 祝祥辉;杨绪盛;黄伟九;龚苗;汪鑫;李梦迪(昆明理工大学材料科学与工程学院,昆明650093;重庆文理学院材料科学与工程学院,重庆402160;重庆理工大学材料科学与工程学院,重庆400044;重庆大学材料科学与工程学院,重庆400044)

机构地区：[1]Faculty of Materials Science and Engineering,Kunming University of Science and Technology,Kunming 650093,China [2]College of Materials Science and Engineering,Chongqing University of Arts and Sciences,Chongqing 402160,China [3]College of Materials Science and Engineering,Chongqing University of Technology,Chongqing 400044,China [4]College of Materials Science and Engineering,Chongqing University,Chongqing 400044,China

出　　处：《Transactions of Nonferrous Metals Society of China》2024年第11期3504-3520,共17页中国有色金属学报（英文版）

基　　金：financially supported by the National Natural Science Foundation of China (No.51871038);the Natural Science Foundation of Chongqing,China (Nos.CSTB2022NSCQ-LZX0002,cstc2021jcyjmsxm X0960)。

摘　　要：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).提出一种将机器学习和显微组织分析相结合的方法,通过纳米压痕、X射线衍射和电子背散射衍射(EBSD)技术,研究AA2099铝锂合金显微组织与硬度的关系。利用随机森林回归(RFR)模型,基于显微组织特征对硬度进行预测,揭示硬度的影响因素并对其重要性进行排序。研究表明,压痕到晶界的距离(D_(dis))越小、最短晶粒轴(D_(min))越短、Schmidt因子(SF_(FD))越小以及{111}滑移面和表面夹角的正弦值(sinθ_(min))越大,硬度越高。在硬度预测中,D_(dis)和D_(min)为关键因素。大角度晶界能阻碍位错滑移,从而提高材料硬度。此外,晶体学取向对硬度也具有显著影响,特别是在{111}Al惯习面上析出T_(1)相时。这种影响归因于在压痕加载过程中所遇到的不同类型的T_(1)相变体。因此,显微组织特征的重要性排序取决于T_(1)相,在T_(1)相有限的样品中,排序为D_(dis)或D_(min)>SF_(FD)>sinθ_(min);而在具有大量T_(1)相的样品中,排序变为D_(dis)或D_(min)>sinθ_(min)>SF_(FD)。

关 键 词：machine learning T_(1)phase HARDNESS Al−Li alloy 

分 类 号：TG146.21[一般工业技术—材料科学与工程] TP181[金属学及工艺—金属材料]