Effect of Aluminum Content on Microstructure and Tribological Properties of CoCrFeNi_(2)-Based High-Entropy Alloys  

Al含量对CoCrFeNi2基高熵合金的微观结构及摩擦性能的影响

作　　者：Zhang Mengdi Zhang Gaimei Luo Chongwei Xu Hanqing 张梦迪;张改梅;罗崇玮;徐汉清(河北大学质量技术监督学院,河北保定071002)

机构地区：[1]School of Quality and Technical Supervision,Hebei University,Baoding 071002,China

出　　处：《稀有金属材料与工程》2025年第2期343-353,共11页Rare Metal Materials and Engineering

基　　金：National Natural Science Foundation of China(52201177);Hebei Province Department of Education Fund(QN2024264);Natural Science Foundation of Hebei Province(E2022201010)。

摘　　要：Four machine learning algorithms were used to predict the solid solution phases of high-entropy alloys(HEAs).To improve the model accuracy,the K-fold cross validation was adopted.Results show that the K-nearest neighbor algorithm can effectively distinguish body-centered cubic(bcc)phase,face-centered cubic(fcc)phase,and mixed(fcc+bcc)phase,and the accuracy rate is approximately 93%.Thereafter,CoCrFeNi_(2)Al_(x)(x=0,0.1,0.3,1.0)HEAs were prepared and characterized by X-ray diffractometer and energy disperse spectrometer.It is found that their phases are transformed from fcc phase to fcc+bcc phase,which is consistent with the prediction results of machine learning.Furthermore,the influence of Al content on the microstructure and tribological properties of CoCrFeNi_(2)Al_(x)(x=0,0.1,0.3,1.0)HEAs was evaluated.Results reveal that with the increase in Al content,the nanohardness and microhardness increase by approximately 45%and 75%,respectively.The elastic limit parameter H/Er increases from 0.0216 to 0.030,whereas the plastic deformation resistance parameter H^(3)/E_(r)^(2) increases from 0.0014 to 0.0045,which demonstrates an improvement in nanohardness with the increase in Al addition amount.In addition,the wear rate decreases by 35%with the increase in Al addition amount.This research provides a new idea with energy-saving and time-reduction characteristics to prepare HEAs.使用了4种机器学习算法来预测高熵合金(HEA)的固溶体相。为了提高模型的准确率,采用了K折交叉验证。结果表明,K近邻(KNN)算法可以有效地区分体心立方(bcc)相、面心立方(fcc)相和混合(fcc+bcc)相,准确率为93%。随后,制备了CoCrFeNi_(2)Al_(x)(x=0、0.1、0.3和1.0)HEA,并采用X射线衍射仪和能量色散光谱仪对其进行了表征,其相由单一的fcc相转变为fcc+bcc相,这与机器学习相预测的结果一致。此外,还评估了Al含量对CoCrFeNi_(2)Al_(x)(x=0、0.1、0.3和1.0)HEAs的微观结构及摩擦性能的影响。结果表明,随着Al含量的增加,纳米硬度和显微硬度分别增加了约45%和75%。弹性极限参数H/Er从0.0216增加到0.030,而抗塑性形变参数H^(3)/E_(r)^(2)从0.0014增加到0.0045,这表明随着Al含量的增加,纳米硬度得到了改善。此外,随着Al含量的增加,磨损率降低了35%。本研究为设计节能和省时的HEA制备方法提供了新思路。

关 键 词：machine learning high-entropy alloy HARDNESS wear resistance 

分 类 号：TG139[一般工业技术—材料科学与工程]