Fatigue Life Prediction of Gray Cast Iron for Cylinder Head Based on Microstructure and Machine Learning
文献类型：期刊论文
| 作者 | Teng, Xiaoyuan1,2; Pang, Jianchao1; Liu, Feng2; Zou, Chenglu1; Bai, Xin1; Li, Shouxin1; Zhang, Zhefeng1 |
| 刊名 | ACTA METALLURGICA SINICA-ENGLISH LETTERS
 |
| 出版日期 | 2023-05-24 |
| 页码 | 13 |
| 关键词 | Gray cast iron Microstructure feature Machine learning High-cycle fatigue life |
| ISSN号 | 1006-7191 |
| DOI | 10.1007/s40195-023-01566-z |
| 通讯作者 | Pang, Jianchao(jcpang@imr.ac.cn) ; Zhang, Zhefeng(zhfzhang@imr.ac.cn) |
| 英文摘要 | Conventional fatigue tests on complex components are difficult to sample, time-consuming and expensive. To avoid such problems, several popular machine learning (ML) algorithms were used and compared to predict fatigue life of gray cast iron (GCI) with the complex microstructures. The feature analysis shows that the fatigue life of GCI is mainly influenced by the external environment such as the stress amplitude, and the internal microstructure parameters such as the percentage of graphite, graphite length, stress concentration factor at the graphite tip, matrix microhardness and Brinell hardness. For simplicity, collected datasets with some of the above features were used to train ML models including back-propagation neural network (BPNN), random forest (RF) and eXtreme gradient boosting (XGBoost). The comparison results suggest that the three models could predict the fatigue lives of GCI, while the implemented RF algorithm is the best performing model. Moreover, the S-N curves fitted by the Basquin relation in the predicted data have a mean relative error of 15% compared to the measured data. The results have demonstrated the advantages of ML, which provides a generic way to predict the fatigue life of GCI for reducing time and cost. |
| 资助项目 | National Natural Science Foundation of China (NSFC)[51871224] ; National Natural Science Foundation of China (NSFC)[52130002] |
| WOS研究方向 | Metallurgy & Metallurgical Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000994091200002 |
| 出版者 | CHINESE ACAD SCIENCES, INST METAL RESEARCH |
| 资助机构 | National Natural Science Foundation of China (NSFC) |
| 源URL | [http://ir.imr.ac.cn/handle/321006/177982]  |
| 专题 | 金属研究所_中国科学院金属研究所 |
| 通讯作者 | Pang, Jianchao; Zhang, Zhefeng |
| 作者单位 | 1.Chinese Acad Sci, Inst Met Res, Shi Changxu Innovat Ctr Adv Mat, Shenyang 110016, Peoples R China 2.Liaoning Petrochem Univ, Sch Mech Engn, Fushun 113001, Peoples R China |
| 推荐引用方式 GB/T 7714 | Teng, Xiaoyuan,Pang, Jianchao,Liu, Feng,et al. Fatigue Life Prediction of Gray Cast Iron for Cylinder Head Based on Microstructure and Machine Learning[J]. ACTA METALLURGICA SINICA-ENGLISH LETTERS,2023:13. |
| APA | Teng, Xiaoyuan.,Pang, Jianchao.,Liu, Feng.,Zou, Chenglu.,Bai, Xin.,...&Zhang, Zhefeng.(2023).Fatigue Life Prediction of Gray Cast Iron for Cylinder Head Based on Microstructure and Machine Learning.ACTA METALLURGICA SINICA-ENGLISH LETTERS,13. |
| MLA | Teng, Xiaoyuan,et al."Fatigue Life Prediction of Gray Cast Iron for Cylinder Head Based on Microstructure and Machine Learning".ACTA METALLURGICA SINICA-ENGLISH LETTERS (2023):13. |
入库方式： OAI收割
来源：金属研究所
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