# Machine Learning-Based Framework for Predicting Creep Rupture Life of Modified 9 Cr-1 Mo Steel
###### Abstract  
Efficient and accurate predictions of creep rupture life are essential for ensuring the integrity of high-temperature components. In this work, a machine learning-based framework is developed for the quick screening of crucial features and accurate prediction of the creep rupture life of modified 9 Cr-1 Mo steels. A feature screening protocol based on correlation filtering and sequential feature selection techniques is established for identifying critical features that significantly affect the prediction performance from a set of numerous descriptors. Moreover, several machine learning algorithms are employed for model training to examine their ability to map the complex nonlinear interactions between multivariate features and creep life. The results show that the test stress, test temperature, tempering time, and the contents of S and Cr are identified as the crucial features that greatly influence the life prediction performance of modified 9 Cr-1 Mo steels. Moreover, the Gaussian process regression (GPR) model with these five selected crucial features exhibits the highest prediction accuracy among various machine learning strategies. Finally, an additional dataset out of model training and testing is used to further validate the efficacy of the constructed GPR model. The validated results demonstrate that most creep data are distributed inside the two-factor band lines. Results from this work show that the developed machine learning framework can offer high accuracy and excellent adaptability in predicting the creep life of modified 9 Cr-1 Mo steels under various environmental conditions. machine learning; creep; feature screening; life prediction; 9 Cr-1 Mo steel +
Footnote †: journal: Applied Sciences  
1
Footnote 1: School of Chemical Engineering and Technology, Xi’an Jiaotong University, Xi’an 710049, China  
[PERSON]  
1 School of Chemical Engineering and Technology, Xi'an Jiaotong University, Xi'an 710049, China  
Zaoxiao Zhang  
Monkman-Grant method [8], the Theta projection method [9; 10], the Omega method [11], etc. However, the time-temperature parameter (TTP) extrapolation models highly depend on the experimental data, and the extrapolation outcomes generally exhibit significant uncertainties [12]. On the other hand, the constitutive models are capable of describing the creep behavior and predicting creep rupture life with short/intermediate-term creep data compared to time-consuming experimental measurements. However, each of these models has a specific range of applications. Moreover, the parameters for these theoretical models are typically complex and alloy composition-sensitive [13]. Therefore, the practical application of traditional creep life prediction models is limited. Recently, machine learning methods have demonstrated considerable promise for the life prediction of components [14; 15; 16; 17]. As a data-driven approach, the significant advantage of machine learning is its exceptional ability to map the intricate nonlinear relationship between high-dimensional data, including material characteristics and environmental factors. Moreover, the application of machine learning techniques does not necessitate extensive specialist knowledge. Thus, machine learning can offer an alternative to complex traditional life prediction models and overcome their limitations. To date, some efforts have been made to improve the applications of machine learning methods in the prediction of the creep rupture life of alloys [13; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28]. In the majority of these machine learning studies, the alloy composition, heat treatment parameters, and environmental factors such as testing temperature and stress are regarded as input variables, while the creep rupture life or the time-temperature parameters (e.g., Larson-Miller parameter and Manson-Haferd parameter) are employed as output variables [13; 20; 21; 22; 23; 24; 25; 26; 27; 28].