Tensile Strength Prediction of Gray Cast Iron for Cylinder Head Based on Microstructure and Machine Learning

doi:10.1002/srin.202300205

IMR OpenIR

	Tensile Strength Prediction of Gray Cast Iron for Cylinder Head Based on Microstructure and Machine Learning
	Teng, Xiaoyuan 1,2; Pang, Jianchao 1; Liu, Feng 2,3; Zou, Chenglu 1; Li, Shouxin 1; Zhang, Zhefeng 1
通讯作者	Pang, Jianchao(jcpang@imr.ac.cn) ; Zhang, Zhefeng(zhfzhang@imr.ac.cn)
	2024
发表期刊	STEEL RESEARCH INTERNATIONAL
ISSN	1611-3683
卷号	95 期号:1 页码:11
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
关键词	gray cast irons machine learning microstructures ultimate tensile strength
资助者	National Natural Science Foundation of China ; National Natural Science Foundation of China (NSFC)
DOI	10.1002/srin.202300205
收录类别	SCI
语种	英语
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
WOS类目	Metallurgy & Metallurgical Engineering
WOS记录号	WOS:001181052200034
出版者	WILEY-V C H VERLAG GMBH
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.imr.ac.cn/handle/321006/184695
专题	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.