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

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

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
Number of pages9
JournalActa Materialia
Volume186
DOIs
Publication statusPublished - 2020 Mar

Keywords

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

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Ceramics and Composites
  • Polymers and Plastics
  • Metals and Alloys

Fingerprint Dive into the research topics of 'Accelerated design of novel W-free high-strength Co-base superalloys with extremely wide γ/γʹ region by machine learning and CALPHAD methods'. Together they form a unique fingerprint.

Cite this