A breakthrough in creep lifetime prediction: Leveraging machine learning and service data

Abstract

Improvement of data-driven techniques, specifically machine learning (ML), in material science turned it into a powerful tool for predicting materials behavior. Accordingly, this study provides a ML prediction of empirical creep lifetimes of 9Cr-1Mo ex-service heater tubes that have been used in industry for up to 47 years. Data from over 90,000 h of stress rupture tests shows that the service parameters influence creep lifetime similar to mechanical properties. Employing six different ML algorithms, viz., K Nearest Neighbors (KNN), Support Vector Regressor (SVR), Random Forest (RF), Gradient Boosting (GB), Gaussian Process (GP), and Multi-Layer Perceptron (MLP) demonstrated that the GP and MLP methods performed significantly better in predicting the creep lifetimes rather than other algorithms. Finally, a validation set involving 12 samples was conducted, and the GP algorithm showed better agreement with experimental values than other ML and Larson-Miller Parameter approaches, illustrating the capability of this model to predict creep lifetimes.