Creep lifetime prediction of 9% Cr martensitic heat-resistant steel based on ensemble learning method

Abstract

Creep lifetime prediction is critical for the design of high-temperature components. Due to creep lifetime being affected by many factors, its prediction with high accuracy is still challenging. The 9% Cr martensitic heat-resistant steel is currently the world's most widely used creep-resistant steel in supercritical power plant equipment. In this work, variables like material chemical compositions, heat treatment conditions and creep test conditions are considered in various machine learning (ML) models to predict creep lifetime. First, series of typical individual regression algorithms are assessed, but the prediction results are imperfect. Second, severa l ensemble learning algorithms are optimized by bagging and boosting, and a noticeable improvement in predictive performance is observed, especially for the extreme gradient boosting algorithm. Finally, a model coupled with Larson-Miller (LM) parameter is proposed based on stacking, which gives the best prediction results. R-square (R2), mean absolute error (MAE), and mean square error (MSE) of the proposed model is 0.918, 0.516, and 0.450, respectively.

Keywords

• Machine learning
• Ensemble learning
• Life prediction
• Creep
• Larson-Miller parameter