无关键原材料的多主体合金设计,以实现净零未来
Swati Singh1, Mingwen Bai2, Allan Matthews3
1Department of Mechanical Engineering, Indian Institute of Technology Guwahati, Guwahati, 781039, India.
Scientific reports
|January 24, 2025
概括
本研究引入了一种机器学习方法,以发现新的耐火高合金 (RHEAs),避免关键原材料 (CRMs). 开发的合金与含有CRM的合金的硬度相匹配,解决供应链风险和环境问题.
科学领域:
- 材料科学与工程 材料科学与工程
- 计算材料设计设计 计算材料设计
- 合金开发 合金开发
背景情况:
- 与传统的超级合金相比,耐火高合金 (RHEAs) 为发动机组件提供了优越的高温性能.
- RHEA的开发往往依赖于关键原材料 (CRM),造成供应链风险,并阻碍净零目标,因为循环排放的排放.
- 现有的RHEA研究严重依赖于 (Nb) 和 (Ta) 等材料.
研究的目的:
- 开发一种用于新型多元合金组合的逆预测方法,从而消除对CRM的需求.
- 在不使用CRM的情况下,可以达到与含有CRM的多主元素合金 (MPEA) 相当的硬度水平.
- 加快发现减少CRM的MPEA (R-CRM-MPEA),以减轻供应链的漏洞和环境问题.
主要方法:
- 一个机器学习 (ML) 模型在3,608个单元和二元材料的计算数据库上使用Thermo-Calc 2024a进行了训练.
- 额外树木回归器 (ETR) 模型表现出卓越的性能,并与元启发式优化技术集成,特别是Cuckoo搜索优化 (CSO).
- 该CSO方法确定了具有减少CRM含量的新型MPEA组合物,与热计算相比,在±20%的误差范围内实现预测.
主要成果:
- 一种新的没有CRM的合金组合,Ti0.01111NiFe0.4Cu0.4,经过计算设计,表现出488HV的维克尔硬度,与含CRM的合金,如CoCrFeNb0.309Ni (480HV) 相当.
- 该研究的重点是促进R-CRM-MPEA的开发,而不仅仅是高硬度合金.
- 一种FCC相合金 (Al6.25Cu18.75Fe25Co25Ni25) 的实验合成和验证显示了热计算,ML预测和实验维克尔硬度值之间的优异一致.
结论:
- 这项开创性的工作为加速发现新型R-CRM-MPEA提供了一个强大的框架.
- 开发的基于机器学习的方法有效地解决了与CRM供应链漏洞,进口依赖和相关环境影响相关的挑战.
- 该研究验证了计算逆向设计在制造高性能合金中的有效性,减少了对关键原材料的依赖.
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