Tensile Strength 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, Feng2,3; Zou, Chenglu1; Li, Shouxin1; Zhang, Zhefeng1 |
| 刊名 | STEEL RESEARCH INTERNATIONAL
 |
| 出版日期 | 2024 |
| 卷号 | 95期号:1页码:11 |
| 关键词 | gray cast irons machine learning microstructures ultimate tensile strength |
| ISSN号 | 1611-3683 |
| DOI | 10.1002/srin.202300205 |
| 通讯作者 | Pang, Jianchao(jcpang@imr.ac.cn) ; Zhang, Zhefeng(zhfzhang@imr.ac.cn) |
| 英文摘要 | The ultimate tensile strength (UTS) of gray cast iron (GCI) can be affected by numerous parameters due to its complex microstructures. To further understand the UTS of GCI, it is necessary to evaluate the impact of various parameters. Herein, a UTS prediction method based on microstructure features and machine learning (ML) algorithms is proposed. The six regression algorithms, namely, Bayesian Ridge, Linear Regression, Elastic Net Regression, Support Vector Regression, Gradient Boosting Regressor (GBR), and Random Forest Regressor are used to develop the prediction models. The predicted results show that the GBR has the best prediction performance for the predicted UTS and the error bands within 5%. The feature importance indicates that matrix hardness has the greatest effect on the UTS in the ML models. Several machine learning algorithms are used to evaluate the tensile strength of metals based on microstructure characteristics. These models can accurately predict the tensile properties of gray cast iron and rank the importance of the microstructural features referenced in the models, which can guide the application of machine learning algorithms in tensile prediction and alloy design of gray cast iron.image (c) 2023 WILEY-VCH GmbH |
| 资助项目 | National Natural Science Foundation of China[51871224] ; National Natural Science Foundation of China[52130002] ; National Natural Science Foundation of China (NSFC) |
| WOS研究方向 | Metallurgy & Metallurgical Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001181052200034 |
| 出版者 | WILEY-V C H VERLAG GMBH |
| 资助机构 | National Natural Science Foundation of China ; National Natural Science Foundation of China (NSFC) |
| 源URL |  |
| 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, Shi Changxu Innovat Ctr Adv Mat, Inst Met Res, Shenyang 110016, Peoples R China 2.Liaoning Petrochem Univ, Sch Mech Engn, 1 Dandong Rd, Fushun 113001, Peoples R China 3.Jihua Lab, Foshan 528200, Peoples R China |
| 推荐引用方式 GB/T 7714 | Teng, Xiaoyuan,Pang, Jianchao,Liu, Feng,et al. Tensile Strength Prediction of Gray Cast Iron for Cylinder Head Based on Microstructure and Machine Learning[J]. STEEL RESEARCH INTERNATIONAL,2024,95(1):11. |
| APA | Teng, Xiaoyuan,Pang, Jianchao,Liu, Feng,Zou, Chenglu,Li, Shouxin,&Zhang, Zhefeng.(2024).Tensile Strength Prediction of Gray Cast Iron for Cylinder Head Based on Microstructure and Machine Learning.STEEL RESEARCH INTERNATIONAL,95(1),11. |
| MLA | Teng, Xiaoyuan,et al."Tensile Strength Prediction of Gray Cast Iron for Cylinder Head Based on Microstructure and Machine Learning".STEEL RESEARCH INTERNATIONAL 95.1(2024):11. |
入库方式： OAI收割
来源：金属研究所
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