Impact Factor3.0

DOI number:10.1016/j.ijpvp.2019.104014

Journal:International Journal of Pressure Vessels and Piping

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.

Indexed by:Journal paper

Document Type:J

Volume:179

Page Number:104014

Translation or Not:no

Date of Publication:2020-06-09

Included Journals:SCI