 | | Mechanics of Solids
A Journal of Russian Academy of Sciences | | Founded
in January 1966
Issued 6 times a year
Print ISSN 0025-6544
Online ISSN 1934-7936 |
Archive of Issues
| Total articles in the database: | | 13427 |
| In Russian (Èçâ. ÐÀÍ. ÌÒÒ): | | 8178
|
| In English (Mech. Solids): | | 5249 |
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| << Previous article | Volume 60, Issue 6 / 2025 | Next article >> |
| Dongquan Wu, Lianpeng Lu, and Dizhi Guo, "Implementation of Creep Behavior Using Neural Network into the Finite Element Method," Mech. Solids. 60 (6), 4900-4920 (2025) |
| Year |
2025 |
Volume |
60 |
Number |
6 |
Pages |
4900-4920 |
| DOI |
10.1134/S0025654425602204 |
| Title |
Implementation of Creep Behavior Using Neural Network into the Finite Element Method |
| Author(s) |
Dongquan Wu (Sino-European Institute of Aviation Engineering, Civil Aviation University of China, Tianjin, 300300 China, dqwu@cauc.edu.cn)
Lianpeng Lu (Sino-European Institute of Aviation Engineering, Civil Aviation University of China, Tianjin, 300300 China)
Dizhi Guo (Sino-European Institute of Aviation Engineering, Civil Aviation University of China, Tianjin, 300300 China) |
| Abstract |
Machine learning is increasingly used to predict material behavior in scientific fields, out-performing traditional numerical techniques. In this study, an Artificial Neural Network model was
integrated into a finite element formulation to establish the creep strain law of metallic materials, considering creep time, stress level, temperature, and multiaxial ductility factor. First, the Neural Network’s structure and principles were presented, and its ability to infer creep-strain derivatives without
prior learning was highlighted. After selecting a 2-hidden-layer architecture, the trained model was
implemented into Abaqus via a CREEP subroutine. A similar Artificial Neural Network structure estimates multiaxial ductility factor considering relevant factors which was used for a multiaxial creep
condition. To validate the model, it was compared with the analytical Wen-Tu model for Sanicro25
alloy. The model’s predictive ability was demonstrated through uniaxial and small punch creep test
simulations. Results suggested the Artificial Neural Network can replace analytical creep strain models in finite element codes and is competitive in simulation time. |
| Keywords |
Artificial Neural Network, Creep Behavior, Multiaxial Ductility Factor, Finite Element Method, Numerical Implementation |
| Received |
01 May 2025 | Revised |
26 August 2025 | Accepted |
29 August 2025 |
| Link to Fulltext |
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