Can Machine Learning and Explainable Artificial Intelligence Help to Improve an Expert Model for Predicting Thermomechanical Fatigue?

Abstract

Machine learning (ML) models are increasingly used for predictive tasks, yet traditional data-based models relying on expert knowledge remain prevalent. This paper examines the enhancement of an expert model for thermomechanical fatigue (TMF) life prediction of turbine components using ML. Using explainable artificial intelligence (XAI) methods such as Permutation Feature Importance (PFI) and SHAP values, we analyzed the patterns and relationships learned by the ML models. Our findings reveal that ML models can be trained on TMF data, but integrating domain knowledge remains crucial. The study concludes with a proposal to further refine the expert model using insights gained from ML models, aiming for a synergistic improvement.