A two-stage predicting model for γ′ solvus temperature of L12-strengthened Co-base superalloys based on machine learning. (July 2019) - Yu, Jinxin; Guo, Shun; Chen, Yuechao; Han, Jiajia; Lu, Yong; Jiang, Qingshan; Wang, Cuiping; Liu, Xingjun
Abstract: As one of the candidate materials of the next generation aircraft engines, L12 -strengthened Co-base superalloys have drawn lots of attentions. However, Co-base superalloys have some disadvantages, such as γ′ precipitates in the superalloys are metastable. Moreover, improving this superalloy through traditional experimental approaches is extremely costly and inefficient. Thus, it is necessary to develop a new approach that could make rapid and accurate predictions of the properties of the L12 -strengthened Co-base superalloys. In this study, the γ′ solvus temperature, which is the basic property of L12 -strengthened Co-base superalloys, is predicted based on our two-stage approach. Firstly, the existence of the γ′ precipitates are predicted. And then, the solvus temperatures of the candidates which are predicted to have γ′ precipitates are calculated by our models. A new superalloy with high γ′ precipitates solvus temperature is designed successfully with the help of our approach. The time cost of this approach is less than that of the traditional experimental approach. This approach could also be used to discover L12 -strengthened Co-base superalloys with other desired properties. Highlights: A two-stage design based on machine learning for Co-based superalloy. Two properties of the Co–base superalloy could be optimized simultaneously. A new superalloy with high γ′ precipitates solvus temperature has been designed successfully.