Abstract
The creep rupture life of 9–12% chromium ferritic steel is predicted as a function of alloy composition, creep stress, and creep temperature. A database is made up from data in previous publications. A novel abductive network is constructed to predict creep rupture life. With a four-layer architecture, the network shows a precise prediction of creep rupture life of 9–12% chromium ferritic steel. Performance is examined and compared with backpropagation algorithm (BP) neural network. Results indicate that the proposed approach is more accurate than Larson–Miller parametric method and more efficient than that of BP neural network. Automatic relevance determination reveals that the influence of Cr and W on creep rupture life of 9–12% chromium ferritic steel are the greatest amongst the alloying elements.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
