Prediction of High-Temperature Creep Life of Austenitic Heat-Resistant Steels Based on Data Fusion

• Published in: Metals (Basel ) 2023-09, Vol.13 (9), p.1630
• Main Authors: Wei, Limin, Wang, Shuo, Hao, Weixun, Huang, Jingtao, Qu, Nan, Liu, Yong, Zhu, Jingchuan
• Format: Article
• Language: English
• Subjects: Accuracy; austenitic heat-resistant steel; Austenitic stainless steels; Creep life; creep life prediction; Data integration; Datasets; Decision trees; Finite element analysis; Gaussian model; Grain size; Heat; Heat resistant steels; High temperature; input feature; Life prediction; Machine learning; Neural networks; Steel; Support vector machines; Temperature; Thermal power plants; Weather forecasting
• ISSN: 2075-4701; 2075-4701
• DOI: 10.3390/met13091630

The creep life prediction of austenitic heat-resistant steel is necessary to guarantee the safe operation of the high-temperature components in thermal power plants. This work presents a machine learning model that can be applied to predict the creep life of austenitic steels, offering a novel method and approach for such predictions. In this paper, creep life data from six typical austenitic heat-resistant steels are used to predict their creep life using various machine learning models. Moreover, the dissimilarities between the machine learning model and the conventional lifetime prediction method are compared. Finally, the influence of different input characteristics on creep life is discussed. The results demonstrate that the prediction accuracy of machine learning depends on both the model and the dataset used. The Gaussian model based on the second dataset achieves the highest level of prediction accuracy. Additionally, the accuracy and the generalization ability of the machine learning model prediction are significantly better than those of the traditional model. Lastly, the effect of the input characteristics on creep life is generally consistent with experimental observations and theoretical analyses.