Predicting creep life of CrMo pressure vessel steel using machine learning models with optimal feature subset selection

Abstract

The data-driven approach for creep life prediction typically integrates numerous characteristics, including material compositions, manufacturing details, and service conditions, into machine learning models. In this study, a machine learning-based creep life prediction approach with optimal feature subset selection is established for 2.25Cr1Mo pressure vessel steel. Before model training and testing, six critical features that significantly impact the creep life of 2.25Cr1Mo steel are selected, specifically the applied stress, temperature, and chemical compositions consisting of Cr, Ni, Mn, and Mo. Various machine learning algorithms, along with the traditional L–M method, are utilized for model training and performance evaluation. Additionally, the developed models undergo validation using experimental data independent of the training and testing datasets to assess their generalization abilities. The results reveal that, among all tested models, the support vector regression (SVR) model, coupled with the optimal feature subset, demonstrates superior prediction accuracy and generalization capability. Finally, the creep life prediction model exhibiting optimal performance is deployed into a software application, leveraging the Python programming language. This predictor tool facilitates rapid and precise creep life predictions for 2.25Cr1Mo pressure vessel steel, relying solely on a limited amount of input information, and provides a clear and visual presentation of the prediction results.