Interpretable Predicting Creep Rupture Life of Superalloys: Enhanced by Domain-Specific Knowledge.

ABSTRACT: Evaluating and understanding the effect of manufacturing processes on the creep performance in superalloys poses a significant challenge due to the intricate composition involved. This study presents a machine-learning strategy capable of evaluating the effect of the heat treatment process on the creep performance of superalloys and predicting creep rupture life with high accuracy. This approach integrates classification and regression models with domain-specific knowledge. The physical constraints lead to significantly enhanced prediction accuracy of the classification and regression models. Moreover, the heat treatment process is evaluated as the most important descriptor by integrating machine learning with superalloy creep theory. The heat treatment design of Waspaloy alloy is used as

SUBMITTER: Yin J 

PROVIDER: S-EPMC10953578 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature