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- Title
Very high‐cycle fatigue life prediction of high‐strength steel based on machine learning.
- Authors
Liu, Xiaolong; Zhang, Siyuan; Cong, Tao; Zeng, Fan; Wang, Xi; Wang, Wenjing
- Abstract
Very high‐cycle fatigue life (VHCF) prediction of high‐strength steel based on machine learning (ML) was investigated in this paper. First, a total of 173 sets of experimental data on the VHCF of high‐strength steel were collected to train the ML model. The sensitivity coefficient analysis indicated that inclusion size and maximum stress were the strongest correlation parameters with fatigue life and selected as the input features for the final model training. The S–N curve predicted by the obtained ML model closely aligns with the actual S–N curve. Among the three algorithm models, namely, random forest, XG boost, and gradient boosting, the gradient boosting model exhibited superior performance and achieved the highest accuracy in predicting the VHCF life of high‐strength steel. A comparison between the Murakami model and the gradient boosting model was conducted. It is indicated that ML exhibits superior predictive performance with high efficiency and excellent accuracy. Highlights: S–N curve predicted by machine learning model closely aligns with that by experiments.Gradient boosting model exhibited superior performance in predicting the VHCF life.Machine learning model outperforms the Murakami model in the terms of accuracy.
- Subjects
STEEL fatigue; HIGH cycle fatigue; FATIGUE life; MACHINE learning; STEEL; RANDOM forest algorithms
- Publication
Fatigue & Fracture of Engineering Materials & Structures, 2024, Vol 47, Issue 3, p1024
- ISSN
8756-758X
- Publication type
Article
- DOI
10.1111/ffe.14213