 | | 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: | | 13288 |
| In Russian (Èçâ. ÐÀÍ. ÌÒÒ): | | 8164
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| In English (Mech. Solids): | | 5124 |
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| << Previous article | Volume 60, Issue 4 / 2025 | Next article >> |
| W.Y. Liu and X.J. Chen, "Machine Learning Prediction on the Stress Intensity Factor for Multiple Edge Cracks in Coatings under Arbitrarily Varying Loads," Mech. Solids. 60 (4), 2763-2780 (2025) |
| Year |
2025 |
Volume |
60 |
Number |
4 |
Pages |
2763-2780 |
| DOI |
10.1134/S0025654425601636 |
| Title |
Machine Learning Prediction on the Stress Intensity Factor for Multiple Edge Cracks in Coatings under Arbitrarily Varying Loads |
| Author(s) |
W.Y. Liu (Department of Applied Mechanics, University of Science and Technology Beijing, Beijing, 100083 China)
X.J. Chen (Department of Applied Mechanics, University of Science and Technology Beijing, Beijing, 100083 China, chenxuejun@ustb.edu.cn) |
| Abstract |
This study utilizes machine learning (ML) methodology to estimate the stress intensity factor (SIF) for multiple edge cracks in a coating-substrate pair. The arbitrarily varying loading function is decomposed into a weighted sum of sine and cosine functions using Fourier series expansion, from which extracted are the characteristic period and harmonic number. A large data set derived from finite element calculation is used to train the ML model. By validation and comparison, it is found that the even extension method offers the highest accuracy in estimating the SIF. For three different loading functions, the predicted results show an average error of less than 1% compared to those by the finite element method. Additionally, the error of the predicted results is less than 3% in comparison with those in two thermal shock scenarios from existing literatures. The findings highlight the potential of ML-driven computational frameworks to achieve efficient and accurate evaluation of SIF for multiple cracks under realistic service conditions. |
| Keywords |
Machine learning, Coating-substrate pair, Multiple cracks, Stress intensity factor |
| Received |
09 April 2025 | Revised |
12 May 2025 | Accepted |
13 May 2025 |
| Link to Fulltext |
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