Prediction of long-term creep life of 9Cr–1Mo–V–Nb steel using artificial neural network. (January 2020) - Liang, Tuo; Liu, Xinbao; Fan, Ping; Zhu, Lin; Bi, Yao; Zhang, Yagang
Abstract: In this work, the method of artificial neural network was employed to predict the long-term creep rupture time of 9Cr–1Mo–V–Nb steel using the NIMS datasheet. In order to verify the performance of this method, the long-term creep rupture times of 23 000–41000 h were predicted using the data lower than 17 000 h. Meanwhile, the detailed analyses were carried out by comparison with the traditional time-temperature parametric (TTP) methods, such as Larson-Miller, Manson-Harferd, and Orr-Sherby-Dorn method. The results showed that by the artificial neural network method, the predicted creep rupture times above had an average relative error of 17%, which was significantly lower than those of TTP methods. It further demonstrated that the artificial neural network offers a convenient tool to predict the accurate creep rupture time of 9Cr–1Mo–V–Nb steel due to its robust ability in law learning and extrapolation generalization. Highlights: TTP methods show poor results of long-term creep life prediction. Results of neural network are better in short-term prediction. Results of neural network are accurate enough in long-term prediction.