Tailoring nanoprecipitates for ultra-strong high-entropy alloys via machine learning and prestrain aging

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

5 Scopus Citations
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Author(s)

  • Tao Zheng
  • Xiaobing Hu
  • Feng He
  • Qingfeng Wu
  • Bin Han
  • Junjie Li
  • Jincheng Wang
  • Zhenhai Xia

Detail(s)

Original languageEnglish
Pages (from-to)156-167
Journal / PublicationJournal of Materials Science and Technology
Volume69
Online published8 Aug 2020
Publication statusPublished - 10 Apr 2021

Abstract

The multi-principal-component concept of high-entropy alloys (HEAs) generates numerous new alloys. Among them, nanoscale precipitated HEAs have achieved superior mechanical properties and shown the potentials for structural applications. However, it is still a great challenge to find the optimal alloy within the numerous candidates. Up to now, the reported nanoprecipitated HEAs are mainly designed by a trial-and-error approach with the aid of phase diagram calculations, limiting the development of structural HEAs. In the current work, a novel method is proposed to accelerate the development of ultra-strong nanoprecipitated HEAs. With the guidance of physical metallurgy, the volume fraction of the required nanoprecipitates is designed from a machine learning of big data with thermodynamic foundation while the morphology of precipitates is kinetically tailored by prestrain aging. As a proof-of-principle study, an HEA with superior strength and ductility has been designed and systematically investigated. The newly developed γ′-strengthened HEA exhibits 1.31 GPa yield strength, 1.65 GPa ultimate tensile strength, and 15% tensile elongation. Atom probe tomography and transmission electron microscope characterizations reveal the well-controlled high γ′ volume fraction (52%) and refined precipitate size (19 nm). The refinement of nanoprecipitates originates from the accelerated nucleation of the γ′ phase by prestrain aging. A deeper understanding of the excellent mechanical properties is illustrated from the aspect of strengthening mechanisms. Finally, the versatility of the current design strategy to other precipitation-hardened alloys is discussed.

Research Area(s)

  • High-entropy alloys, Machine learning, Mechanical properties, Prestrain aging, Strengthening mechanisms

Citation Format(s)

Tailoring nanoprecipitates for ultra-strong high-entropy alloys via machine learning and prestrain aging. / Zheng, Tao; Hu, Xiaobing; He, Feng; Wu, Qingfeng; Han, Bin; Chen, Da; Li, Junjie; Wang, Zhijun; Wang, Jincheng; Kai, Ji-jung; Xia, Zhenhai; Liu, C.T.

In: Journal of Materials Science and Technology, Vol. 69, 10.04.2021, p. 156-167.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review