Physics-informed machine learning framework for creep-fatigue life prediction of a Ni-based superalloy using ensemble learning

Abstract

Under extreme environments, critical components of nuclear power equipment suffer creep-fatigue damage accumulation due to the complex load, threatening the safety operation of equipment. Therefore, creep-fatigue life assessment is crucial to ensure the structural integrity and performance requirement of nuclear power equipment during service. To address the poor prediction accuracy under small data sets, this paper developed a physics-informed machine learning (PIML) framework to model the creep-fatigue interaction behavior of a Ni-based superalloy. In this work, an ensemble learning approach is considered to embed physical information into machine learning (ML) models for the creep-fatigue life prediction. The comparison of each model shows that PIML is capable of utilizing the strengths of purely data-driven models and physics-based models to provide excellent prediction accuracy and promote the generalization ability.