# Machine Learning-Based Framework for Predicting Creep Rupture Life of Modified 9 Cr-1 Mo Steel
## 3 Results and Discussion
### Feature Screening Results
The long-term tempering of a martensitic 9 Cr steel, according to [PERSON] [45], caused a higher minimum creep rate and a shorter creep rupture life than the conventional quenching and tempering of the same steel, which is consistent with the present results. The composition features, namely the S and Cr content, are also two essential features that greatly influence the creep property. The role of S in capturing the inclusion information has been reported in earlier studies [46, 47] which demonstrate that inclusion particles are the preferred sites for the nucleation of creep damage. This study also concludes that the inclusion is important for the accurate estimation of creep rupture life since a higher S content has a detrimental effect on creep property. The Cr content is shown to be another key element feature, and higher Cr content tends to negatively affect the creep rupture life. It is well known that Cr content is required to attain adequate oxidation resistance in high-temperature steam. However, it has been shown that a high content of Cr is not preferred for long-term stability in 9-12% Cr steels, because a large amount of M\({}_{23}\)C\({}_{6}\) carbides would coarsen during service [20,48]. The current results of feature importance analysis are consistent with earlier theoretical studies, suggesting that the dataset employed in this work is extremely trustworthy and explicable. The above findings can also provide valuable insights and guidance for future creep-oriented alloy design. It is also important to note that in addition to these five selected features, other features including alloy contents (e.g., Mo, V, Nb, and Ni) and processing parameters (e.g., normalizing temperature and tempering temperature) are insignificant for the model prediction due to their low variance in the current dataset. The variation in material form (e.g., tube, plate, and pipe) also has a negligible impact on creep rupture life according to both the results of the Pearson correlation coefficient and SHAP values. This is probably because  
Figure 5: Variations in \(R^{2}\) and \(RMSE\) as a function of the number of input features. (**a**) GPR model and (**b**) SVR model. the manufacturing process and microstructure vary from form to form, and consequently the differences in material form are characterized by alloy contents and heat treatment parameters. The use of insufficient data in this work is another possible cause. Most creep data are collected from the modified 9 Cr-1 Mo steel in the form of a tube, while limited data are obtained from the pipe. Building a larger, high-quality database is urgently required, because both the quantity and quality of data have a significant impact on the accuracy of machine learning models [49]. Although the material form and other features are demonstrated in this study to be insignificant for creep life prediction, more work still has to be done to make use of as many features as possible and investigate their effects on model performance. For example, it is possible to consider the material's grain size as a crucial input feature in future machine learning-based predictions of creep rupture life. Previous works have highlighted that during high-temperature damage, inhomogeneous deformation tends to occur at the grain boundary and triple junctions where creep voids and cracks are most likely to be initiated [2; 3; 50]. Numerous studies have demonstrated that the grain size affects the creep strain rate differently in the high and low applied stress regimes [51; 52]. Therefore, future research can take grain size into account together with alloy contents and other material characteristics for improving the model's performance.