Abstract
Creep rupture life is a key performance parameter of nickel-based superalloys, which directly affects the engineering service behavior of components. In this study, a machine learning method based on data fusion was proposed to predict the properties of new alloys with the properties of existing alloys, or to predict other related properties of the same alloy, so as to solve the problem of accurate prediction of creep rupture life caused by the lack of accumulated data. Using the existing nickel-based superalloy creep rupture life data accumulated in the NIMS database, a creep rupture life prediction model was established using key alloy factor screening and feature-based transfer learning strategy (FS-SVR). The performance of GH4169D alloy was successfully predicted by the properties of GH4169 alloy, and the high temperature creep rupture life was predicted by the low temperature creep rupture life of GH4169 and GH4169D alloys, and the prediction accuracy reached more than 90%. The research results not only provide a fast method for predicting the creep properties of novel nickel-based superalloys, but also provide a reference case for data fusion to assist the research and development of new materials.
NSTL
Elsevier