Material Fracture Life Prediction Under High Temperature Creep Conditions Using Support Vector Machines And Artificial Neural Networks Techniques
No Thumbnail Available
Identifiers
Publication date
2021
Advisors
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citations
Export
Abstract
One of the most applied materials to manufacture critical components in power plants are martensitic steels due to their high creep and oxidation resistance. In this work, the fracture life of martensitic steels that are designed based on the P92 standard is modeled in order to better understand the relation between its service life and its composition and its thermal treatment. This feature is usually studied by performing creep tests, although carrying out tests of this type are really cost and time consuming. To solve this problem, a multivariate analysis and a training-testing model methodology were performed using a dataset formed by 344 creep tests with the final goal of obtaining a model to predict the fracture life of the material based on several nonlinear techniques like support vector machines and artificial neural networks. Once the models were defined based on predicting with the better generalization capability to cover the whole scenario of the problem, those were compared to determine which one was the most accurate among them. Finally, it was concluded that the model's performance using the proposed methodology based on artificial neural networks got the most accurate results, achieving low errors of approximately 6.14% when predicting creep behavior under long service times.
Description
Publisher Copyright: © 2021 IEEE.
Keywords
artificial neural networks , nonlinear regression , support vector machines , validation methodology , Nonlinear regression , Artificial neural networks , Support vector machines , Validation methodology , Artificial Intelligence , Computational Theory and Mathematics , Computer Science Applications , Funding Info , The authors wish to thank to the Basque Government for its support through grant KK-2019-00033 METALCRO2. , The authors wish to thank to the Basque Government for its support through grant KK-2019-00033 METALCRO2.
Type
Citation
Martinez , R F , Jimbert , P , Callejo , L M & Barbero , J I 2021 , Material Fracture Life Prediction Under High Temperature Creep Conditions Using Support Vector Machines And Artificial Neural Networks Techniques . in 31st International Conference on Computer Theory and Applications, ICCTA 2021 - Proceedings . 31st International Conference on Computer Theory and Applications, ICCTA 2021 - Proceedings , Institute of Electrical and Electronics Engineers Inc. , pp. 127-132 , 31st International Conference on Computer Theory and Applications, ICCTA 2021 , Alexandria , Egypt , 11/12/21 . https://doi.org/10.1109/ICCTA54562.2021.9916603
conference
conference