原子半径
高熵合金
电负性
固溶体
共晶体系
价电子
材料科学
合金
熔点
相(物质)
热力学
焓
混合焓
电子探针
分析化学(期刊)
冶金
化学
复合材料
电子
物理
有机化学
色谱法
量子力学
作者
Yupeng Zhang,Xizhang Chen,S. Jayalakshmi,R. Arvind Singh,V. B. Deev,E. S. Prusov
标识
DOI:10.1016/j.jallcom.2020.157625
摘要
In high entropy alloys (HEAs), the formation of solid solution phase is governed by main factors such as mixing entropy, mixing enthalpy, atomic radii, and atomic size difference. However, factors such as electronegativity, valence electron concentration, and melting point, also significantly influence the formation of the solid solution phase, individually or in combination in specific alloys, and are often less studied and reported. In this work, CoCrFeNiX0.4 (X = Al, Nb, Ta, elements with equi-atomic radii) high entropy alloys have been prepared by powder plasma arc additive manufacturing (PPA-AM). The effect of equi-atomic radii element addition on the microstructural evolution was studied. The results showed that although Al, Nb, and Ta were equi-atomic radii element additions, the resulting HEAs had variations in their phase formation and mechanical properties. Following observations were made: (i) Al addition: FCC + BCC +Sigma phase formation with lowest hardness and (ii) Nb and Ta addition: FCC + Laves phase + Eutectic phases formation with relatively high hardness (>85%–~110% increase). Considering other factors that determine the formation of solid solution phase, it has been identified that: (i) high melting point of the metal has a superior influence on the formation of topologically closed packed phases (TCP) solid solution phase and (ii) elements with large electronegativity differences tends to be rich in the second solid solution phase. The HEAs deposited by PPA-AM have similar/better mechanical stability when compared to the as-cast alloys. Using an innovative AM technology to fabricate HEAs, this work emphasizes the importance of the control of multiple variables in manipulating the solid solution phase formation and mechanical properties of HEAs.
科研通智能强力驱动
Strongly Powered by AbleSci AI