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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.11154 |
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| _version_ | 1866915110410256384 |
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| author | Yasniy, Oleh Tymoshchuk, Dmytro Didych, Iryna Zagorodna, Nataliya Malyshevska, Olha |
| author_facet | Yasniy, Oleh Tymoshchuk, Dmytro Didych, Iryna Zagorodna, Nataliya Malyshevska, Olha |
| contents | In the current study, the fatigue life of QSTE340TM steel was modelled using a machine learning method, namely, a neural network. This problem was solved by a Multi-Layer Perceptron (MLP) neural network with a 3-75-1 architecture, which allows the prediction of the crack length based on the number of load cycles N, the stress ratio R, and the overload ratio Rol. The proposed model showed high accuracy, with mean absolute percentage error (MAPE) ranging from 0.02% to 4.59% for different R and Rol. The neural network effectively reveals the nonlinear relationships between input parameters and fatigue crack growth, providing reliable predictions for different loading conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_11154 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Modelling of automotive steel fatigue lifetime by machine learning method Yasniy, Oleh Tymoshchuk, Dmytro Didych, Iryna Zagorodna, Nataliya Malyshevska, Olha Machine Learning Neural and Evolutionary Computing In the current study, the fatigue life of QSTE340TM steel was modelled using a machine learning method, namely, a neural network. This problem was solved by a Multi-Layer Perceptron (MLP) neural network with a 3-75-1 architecture, which allows the prediction of the crack length based on the number of load cycles N, the stress ratio R, and the overload ratio Rol. The proposed model showed high accuracy, with mean absolute percentage error (MAPE) ranging from 0.02% to 4.59% for different R and Rol. The neural network effectively reveals the nonlinear relationships between input parameters and fatigue crack growth, providing reliable predictions for different loading conditions. |
| title | Modelling of automotive steel fatigue lifetime by machine learning method |
| topic | Machine Learning Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2501.11154 |