Fatigue Life Prediction of Gray Cast Iron for Cylinder Head Based on Microstructure and Machine Learning

doi:10.1007/s40195-023-01566-z

IMR OpenIR

	Fatigue Life Prediction of Gray Cast Iron for Cylinder Head Based on Microstructure and Machine Learning
	Teng, Xiaoyuan 1,2; Pang, Jianchao 2; Liu, Feng 1; Zou, Chenglu 2; Bai, Xin 2; Li, Shouxin 2; Zhang, Zhefeng 2
通讯作者	Pang, Jianchao(jcpang@imr.ac.cn) ; Zhang, Zhefeng(zhfzhang@imr.ac.cn)
	2023-05-24
发表期刊	ACTA METALLURGICA SINICA-ENGLISH LETTERS
ISSN	1006-7191
页码	13
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.
关键词	Gray cast iron Microstructure feature Machine learning High-cycle fatigue life
资助者	National Natural Science Foundation of China (NSFC)
DOI	10.1007/s40195-023-01566-z
收录类别	SCI
语种	英语
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
WOS类目	Metallurgy & Metallurgical Engineering
WOS记录号	WOS:000994091200002
出版者	CHINESE ACAD SCIENCES, INST METAL RESEARCH
引用统计	被引频次：3[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/321006/177983
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.Liaoning Petrochem Univ, Sch Mech Engn, Fushun 113001, Peoples R China 2.Chinese Acad Sci, Inst Met Res, Shi Changxu Innovat Ctr Adv Mat, Shenyang 110016, 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.