2.25Cr-1Mo-0.25V steel is the main material of hydrogenation reactor due to its good high temperature properties and hydrogen embrittlement resistance. As we all know, there is a great performance gap between virgin material and actual product. Nevertheless, current design methods cannot evaluate the impact of manufacturing. In engineering practice, tensile testing is widely implemented on online surveillance samples or experimentally simulated samples to acquire the actual material characteristics. However, sampling from service hydrogenation reactor, as well as simulating actual manufacturing process, is time-consuming, labor-intensive, and difficult. To systematically analyze the effect of fabrication on 2.25Cr-1Mo-0.25V steel, a prediction method that can consider manufacturing residual effects needs to be proposed. Nowadays, many data-driven methods, such as back propagation neural network (BPNN), support vector machine (SVM) and random forest (RF), are employed in predicting material behavior during hot forming, whereas they are scarcely applied on 2.25Cr-1Mo-0.25V steel. In this work, the mentioned three machine learning procedures were constructed to simulate the behavior of actually manufactured 2.25Cr-1Mo-0.25V steel in uniaxial tensile test. Firstly, tensile samples were extracted from different locations of an actually manufactured hydrogenation reactor, and stretched at 454 °C and 482 °C. Subsequently, BPNN, SVM and RF models were developed on the obtained tensile curves, respectively. Their inputs were manufacturing parameter, sampling location, service temperature and tensile strain, whereas their output was tensile stress. Finally, the predictability of models was evaluated by test set data which were not involved in model training process. The results show that the predictions of BPNN, SVM and RF all agree well with experimental data. BPNN exhibits lowest prediction error and highest generalization power, SVM is somewhere in between, while RF is the worst. This work will contribute to the application of data-driven methods in material property prediction of 2.25Cr-1Mo-0.25V steel.

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