Thermodynamic calculation and machine learning aided composition design of new nickel-based superalloys

高温合金 材料科学 合金 冶金 相(物质) 相图 化学 有机化学
作者
Qingshuang Ma,Xintong Li,Ruifeng Xin,Enyu Liu,Qiuzhi Gao,Linlin Sun,Xuming Zhang,Chengxian Zhang
出处
期刊:Journal of materials research and technology [Elsevier BV]
卷期号:26: 4168-4178 被引量:11
标识
DOI:10.1016/j.jmrt.2023.08.139
摘要

At present, the operating temperature of the fourth-generation of nickel-based powder superalloys has not reach the target of 815°C, and the traditional superalloy design and optimization methods are costly and inefficient. To overcome these challenges, phase diagram is used to calculate the thermodynamic parameters of the typical first three generations of nickel-based powder superalloys and representative fourth-generation superalloys. From strengthening processes including solid solution strengthening and γ' phase strengthening, the effects of alloy compositions on alloy characteristics are examined. Based on this analysis, the thermodynamic standards that satisfy the performance requirements of the fourth-generation nickel-based powder superalloys are established. Then, the standards are applied to JMatPro and machine learning techniques as the foundation for determining the composition selection range, and several new nickel-based superalloys are created. The machine learning aided alloy design concept and theoretical results will provide significant guidance for further study of new nickel-based superalloys.
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