摘要
为了探讨AlCrFeNiTi高熵合金的热稳定性,采用氩气保护下真空电弧熔炼法制备铸态合金,在不同加热温度和保温时间下对合金进行热处理并测定其硬度变化曲线,研究合金的硬度与显微组织之间的关系;最后利用反向传播人工神经网络建立合金的加热温度时间-硬度关系网络,实现对合金硬度的预测。结果表明:铸态AlCrFeNiTi高熵合金由两个简单体心立方结构固溶体组成,显微组织是由枝晶、枝晶间和(α+β)共晶组织组成的典型高熵合金枝晶组织;合金经过400~900℃的加热和0.5~10h的保温处理后空冷,合金能在一定程度上保持原始铸态下的组织和枝晶相含量,硬度(HV)依旧保持在4000MPa以上,表现出良好的热稳定性;建立的AlCrFeNiTi高熵合金加热温度时间-硬度神经网络有着良好的精度和适用性,对工业应用具有指导作用。
In order to explore the thermal stability of A1CrFeNiTi high entropy alloy, the as-cast A1CrFeNiTi high entropy alloy was synthesized by the vacuum arc melting under an argon atmosphere, and the hardness change of the alloy in different heat treatments, including heating temperature and holding time was evaluated, and then the relationship between the hardness and microstructures of AICrFeNiTi alloy was further discussed. Finally, the network model between heat treatments and hardness of A1CrFeNiTi alloy was built by the back propagation artificial neural network (BP-ANN) to predict the hardness of the alloy. The results show that the as-cast AlCrFeNiTi high entropy alloy is composed of two body centered cubic structures, and typical dendrite, interdendrite and eutectic structure a+fl microstructures are observed. Compared with as-cast alloy, the microstructures and content of dendrite of the alloy at different heating temperatures (from 400 to 900 ~C) and holding time (from 0.5 to 10 h) have no significant changes, and the hardness of these heat treated samples are all higher than 4000 MPa (HV), which indicates the A1CrFeNiTi high entropy alloy has a good thermal stability. Furthermore, the established BP-ANN between heat treatments and hardness of A1CrFeNiTi alloy shows better precision and applicability and can be used to guide the industrial applications.
出处
《稀有金属材料与工程》
SCIE
EI
CAS
CSCD
北大核心
2018年第1期191-196,共6页
Rare Metal Materials and Engineering
基金
辽宁省博士启动基金(201501079)
辽宁省高校本科人才培养模式改革项目(018-1502153601)