Frontiers in Materials (May 2022)
Accelerated Design of High γ′ Solvus Temperature and Yield Strength Cobalt-Based Superalloy Based on Machine Learning and Phase Diagram
- Cuiping Wang,
- Cuiping Wang,
- Xin Chen,
- Xin Chen,
- Yuechao Chen,
- Yuechao Chen,
- Jinxin Yu,
- Jinxin Yu,
- Wensu Cai,
- Wensu Cai,
- Zhongfeng Chen,
- Zhongfeng Chen,
- Xiang Yu,
- Xiang Yu,
- Yingju Li,
- Yuansheng Yang,
- Xingjun Liu,
- Xingjun Liu,
- Xingjun Liu,
- Xingjun Liu
Affiliations
- Cuiping Wang
- College of Materials and Fujian Provincial Key Laboratory of Materials Genome, Xiamen University, Xiamen, China
- Cuiping Wang
- Xiamen Key Laboratory of High Performance Metals and Materials, Xiamen University, Xiamen, China
- Xin Chen
- College of Materials and Fujian Provincial Key Laboratory of Materials Genome, Xiamen University, Xiamen, China
- Xin Chen
- Xiamen Key Laboratory of High Performance Metals and Materials, Xiamen University, Xiamen, China
- Yuechao Chen
- College of Materials and Fujian Provincial Key Laboratory of Materials Genome, Xiamen University, Xiamen, China
- Yuechao Chen
- Xiamen Key Laboratory of High Performance Metals and Materials, Xiamen University, Xiamen, China
- Jinxin Yu
- State Key Laboratory of Advanced Welding and Joining, Harbin Institute of Technology, Harbin, China
- Jinxin Yu
- Institute of Materials Genome and Big Data, Harbin Institute of Technology, Shenzhen, China
- Wensu Cai
- College of Materials and Fujian Provincial Key Laboratory of Materials Genome, Xiamen University, Xiamen, China
- Wensu Cai
- Xiamen Key Laboratory of High Performance Metals and Materials, Xiamen University, Xiamen, China
- Zhongfeng Chen
- College of Materials and Fujian Provincial Key Laboratory of Materials Genome, Xiamen University, Xiamen, China
- Zhongfeng Chen
- Xiamen Key Laboratory of High Performance Metals and Materials, Xiamen University, Xiamen, China
- Xiang Yu
- College of Materials and Fujian Provincial Key Laboratory of Materials Genome, Xiamen University, Xiamen, China
- Xiang Yu
- Xiamen Key Laboratory of High Performance Metals and Materials, Xiamen University, Xiamen, China
- Yingju Li
- Institute of Metal Research, Chinese Academy of Sciences (CAS), Shenyang, China
- Yuansheng Yang
- Institute of Metal Research, Chinese Academy of Sciences (CAS), Shenyang, China
- Xingjun Liu
- State Key Laboratory of Advanced Welding and Joining, Harbin Institute of Technology, Harbin, China
- Xingjun Liu
- Institute of Materials Genome and Big Data, Harbin Institute of Technology, Shenzhen, China
- Xingjun Liu
- College of Materials and Fujian Provincial Key Laboratory of Materials Genome, Xiamen University, Xiamen, China
- Xingjun Liu
- Xiamen Key Laboratory of High Performance Metals and Materials, Xiamen University, Xiamen, China
- DOI
- https://doi.org/10.3389/fmats.2022.882955
- Journal volume & issue
-
Vol. 9
Abstract
This study combines machine learning and a phase diagram to accelerate the design of a cobalt-based superalloy with a composition of Co-30Ni-10Al-6Ta (at%). The results show that Co-30Ni-10Al-6Ta alloy exhibits high γ′ solvus temperature (1,215 °C) and high yield strength (1,220 Mpa at 25 °C), which is comparable with commercial nickel-based polycrystalline superalloy M-Mar-247. Moreover, the wide processing window and excellent γ′ phase stability make it lucrative for further applications at high temperatures. Meanwhile, the alloy design method also provides a new idea for efficiently realizing the preparation of high-performance alloys.
Keywords
- machine learning
- new cobalt-based superalloys
- high strength
- high γ' solvus temperature
- high γ′ phase stability
