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Accelerated design of novel W-free high-strength Co-base superalloys with extremely wide γ/γʹ region by machine learning and CALPHAD methods

  • Jingjing Ruan
  • , Weiwei Xu
  • , Tao Yang
  • , Jinxin Yu
  • , Shuiyuan Yang*
  • , Junhua Luan
  • , Toshihiro Omori
  • , Cuiping Wang*
  • , Ryosuke Kainuma
  • , Kiyohito Ishida
  • , Chain Tsuan Liu
  • , Xingjun Liu*
  • *Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

Abstract

Since half a century ago, researchers have continuously focused on developing γʹ-strengthened Co-base superalloys to achieve an increased power and efficiency; these alloys can supposedly operate at higher temperatures than Ni-base superalloys. However, the yielded results have failed to meet the expectations. Herein, we successfully design novel W-free Co-V-Ta-base alloys by employing machine learning algorithm and CALPHAD methods, which exhibit low mass density (8.67–8.86 g/cm3), an extremely wide γ/γʹ region, a high γʹ solvus temperature (up to 1044 °C), and a high strength. The atom probe tomography results show that titanium is an extremely strong γʹ-former; therefore, it is expected to improve the thermodynamic stability of the γʹ phase. Furthermore, besides the very high tensile strength (18.7 GPa) of γʹ phase, indicated by first-principles calculations, the strength of Ti-incorporated alloy is higher than that of γʹ-strengthened Co-base superalloys; especially, the reported strength value is higher than that of the well-known Co-9Al-9 W alloy by approximately 322 MPa at 750 °C, which is comparable to that of a few commercial Ni-base superalloys. Therefore, the possibility of the Co-V-Ta-base system being a candidate for developing novel Co-base superalloys is strongly suggested in this study.
Original languageEnglish
Pages (from-to)425-433
JournalActa Materialia
Volume186
Online published7 Jan 2020
DOIs
Publication statusPublished - Mar 2020

Research Keywords

  • Atom probe tomography (APT)
  • Cobalt-base superalloys
  • Grain-boundary segregation-induced phase transformation
  • L12 compound
  • Machine learning
  • Mechanical property

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