Low‐Cycle and Thermomechanical Fatigue Life Prediction Method for Compacted Graphite Iron Based on Small‐Sample Physics‐Informed Neural Networks
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Wiley Open Access
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| Natura: | Artículo Open Access |
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Wiley
2025
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| _version_ | 1867008646765871104 |
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| author | Teng Ma Guoxi Jing Xiuxiu Sun Guang Chen Yafei Fu Tian Ma |
| author_facet | Teng Ma Guoxi Jing Xiuxiu Sun Guang Chen Yafei Fu Tian Ma Teng Ma Guoxi Jing Xiuxiu Sun Guang Chen Yafei Fu Tian Ma |
| collection | Wiley Open Access |
| contents | Low‐Cycle and Thermomechanical Fatigue Life Prediction Method for Compacted Graphite Iron Based on Small‐Sample Physics‐Informed Neural Networks Teng Ma Guoxi Jing Xiuxiu Sun Guang Chen Yafei Fu Tian Ma Fatigue & Fracture of Engineering Materials & Structures ABSTRACTA physics‐informed neural network (PINN) model based on deep learning has been proposed for predicting low‐cycle fatigue (LCF) and thermomechanical fatigue (TMF) life. By analyzing the LCF and TMF data of compacted graphite iron (CGI), characteristic parameters were identified that can simultaneously represent both types of fatigue, achieving a unification of the parameters for the two fatigue life models. The incorporation of fatigue life physical information as a constraint in the loss function of the deep neural network enabled accurate predictions of LCF and TMF for CGI under small‐sample conditions. Comparative analysis results indicated that the deep learning–based PINN model outperformed traditional machine learning models in terms of prediction accuracy. Additionally, comparisons with traditional LCF and TMF life prediction models showed that the deep learning–based PINN model achieves high prediction accuracy while possessing generalization and extrapolation capabilities unattainable by traditional models. These results demonstrate that the PINN model exhibits high accuracy and versatility. 10.1111/ffe.70002 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| doi_str_mv | 10.1111/ffe.70002 |
| format | Artículo Open Access |
| id | wiley_oa_10_1111_ffe_70002 |
| institution | Wiley Open Access |
| license_str_mv | http://onlinelibrary.wiley.com/termsAndConditions#vor |
| publishDate | 2025 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Low‐Cycle and Thermomechanical Fatigue Life Prediction Method for Compacted Graphite Iron Based on Small‐Sample Physics‐Informed Neural Networks Teng Ma Guoxi Jing Xiuxiu Sun Guang Chen Yafei Fu Tian Ma Fatigue & Fracture of Engineering Materials & Structures Low‐Cycle and Thermomechanical Fatigue Life Prediction Method for Compacted Graphite Iron Based on Small‐Sample Physics‐Informed Neural Networks Teng Ma Guoxi Jing Xiuxiu Sun Guang Chen Yafei Fu Tian Ma Fatigue & Fracture of Engineering Materials & Structures ABSTRACTA physics‐informed neural network (PINN) model based on deep learning has been proposed for predicting low‐cycle fatigue (LCF) and thermomechanical fatigue (TMF) life. By analyzing the LCF and TMF data of compacted graphite iron (CGI), characteristic parameters were identified that can simultaneously represent both types of fatigue, achieving a unification of the parameters for the two fatigue life models. The incorporation of fatigue life physical information as a constraint in the loss function of the deep neural network enabled accurate predictions of LCF and TMF for CGI under small‐sample conditions. Comparative analysis results indicated that the deep learning–based PINN model outperformed traditional machine learning models in terms of prediction accuracy. Additionally, comparisons with traditional LCF and TMF life prediction models showed that the deep learning–based PINN model achieves high prediction accuracy while possessing generalization and extrapolation capabilities unattainable by traditional models. These results demonstrate that the PINN model exhibits high accuracy and versatility. 10.1111/ffe.70002 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| title | Low‐Cycle and Thermomechanical Fatigue Life Prediction Method for Compacted Graphite Iron Based on Small‐Sample Physics‐Informed Neural Networks |
| topic | Fatigue & Fracture of Engineering Materials & Structures |
| url | https://onlinelibrary.wiley.com/doi/10.1111/ffe.70002 |