# Machine Learning-Based Framework for Predicting Creep Rupture Life of Modified 9 Cr-1 Mo Steel
###### Abstract
Various machine learning strategies such as multilayer perceptron [13], linear regression [20], random forest regression [21], deep neural network [22], and ensemble learning methods [23; 24; 25] are developed to accurately describe the complex relationship between high-dimensional input data and output life. For instance, [PERSON] et al. [22] proposed a deep neural network for life prediction of 316 stainless steel items under creep, fatigue, and more complex creep-fatigue conditions. [PERSON] et al. [23] evaluated the performances of several ensemble learning algorithms on creep life with a prediction of 9% Cr martensitic steel, and they found the extra trees (ETs) and extreme gradient boosting (XGB) methods exhibit higher accuracy than other models. [PERSON] et al. [26] proposed a divide-and-conquer self-adaptive (DCSA) learning method to accurately predict the creep rupture life of Ni-based superalloys. Moreover, five microstructural factors such as the lattice parameter, stacking fault energy, and other characteristics were taken into consideration as input descriptors in addition to the basic input variables to reduce the prediction error. Although extensive research has shown the capability of developing machine learning-based models for creep rupture life prediction, very few studies take into account the impact of material forms (such as tube, plate, and pipe) on prediction outcomes. The modified 9 Cr-1 Mo steel has been selected for the majority of the high-temperature components, such as boiler tubes, reactor vessels, and seamless pipe, in the fossil industry. However, the findings of a recent study by [PERSON] and [PERSON] [29] suggest that despite the material (9 Cr-1 Mo-V-Nb) remaining the same for various product forms, namely tube, plate, and pipe, the parameters for the Wilshire life prediction model are different for each form. Therefore, there is a need to determine the degree to which the material form affects the machine learning-based creep life predictions. On the other hand, even though most earlier studies used an acceptable number of features, such as material characteristics and environmental factors as input variables for life prediction, many of them overlooked the feature screening procedure prior to the training and validation of machine learning models. An overly complex model may be created by using too many input variables, which will have a detrimental effect, such as the overfitting issue, on prediction performance. As a result, the machine learning model with a simple structure is required. Moreover, when predicting the creep life of new alloys supplied by various manufacturers, some input features might not be readily available. Therefore, feature screening must be carried out prior to the training of machine learning models in order to identify the crucial features that have a significant impact on the creep rupture life and to make the model as simple and applicable as possible. The work begins with constructing a dataset that contains the creep rupture data of modified 9 Cr-1 Mo steel with different material forms, and focuses on developing a machine learning-based framework for the efficient identification of crucial features selected from a collection of numerous features, and the precise prediction of creep rupture life. Several machine learning algorithms are employed to train the model and examine the impact of different algorithms on prediction performance. These algorithms include least absolute shrinkage and selection operator (Lasso) [30], ridge regression (Ridge) [31], support vector regression (SVR) [32], Gaussian process regression (GPR) [33], decision tree (DT) [34] and random forest (RF) [35]. Finally, the developed creep rupture life prediction model that possesses the highest prediction accuracy is validated using a dataset out of model training and testing.