A Comparative Investigation of Machine Learning Algorithms for Pore-Influenced Fatigue Life Prediction of Additively Manufactured Inconel 718 Based on a Small Dataset

doi:10.3390/ma16196606

IMR OpenIR

	A Comparative Investigation of Machine Learning Algorithms for Pore-Influenced Fatigue Life Prediction of Additively Manufactured Inconel 718 Based on a Small Dataset
	Hu, Bing-Li 1,2; Luo, Yan-Wen 3; Zhang, Bin 3; Zhang, Guang-Ping 1
通讯作者	Zhang, Guang-Ping(gpzhang@imr.ac.cn)
	2023-10-01
发表期刊	MATERIALS
卷号	16 期号:19 页码:23
摘要	Fatigue life prediction of Inconel 718 fabricated by laser powder bed fusion was investigated using a miniature specimen tests method and machine learning algorithms. A small dataset-based machine learning framework integrating thirteen kinds of algorithms was constructed to predict the pore-influenced fatigue life. The method of selecting random seeds was employed to evaluate the performance of the algorithms, and then the ranking of various machine learning algorithms for predicting pore-influenced fatigue life on small datasets was obtained by verifying the prediction model twenty or thirty times. The results showed that among the thirteen popular machine learning algorithms investigated, the adaptive boosting algorithm from the boosting category exhibited the best fitting accuracy for fatigue life prediction of the additively manufactured Inconel 718 using the small dataset, followed by the decision tree algorithm in the nonlinear category. The investigation also found that DT, RF, GBDT, and XGBOOST algorithms could effectively predict the fatigue life of the additively manufactured Inconel 718 within the range of 1 x 105 cycles on a small dataset compared to others. These results not only demonstrate the capability of using small dataset-based machine learning techniques to predict fatigue life but also may guide the selection of algorithms that minimize performance evaluation costs when predicting fatigue life.
关键词	small dataset machine learning algorithm fatigue life additive manufacturing
资助者	National Key R&D Program of China ; National Natural Science Foundation of China (NSFC) ; Fundamental Research Project of Shenyang National Laboratory for Materials Science
DOI	10.3390/ma16196606
收录类别	SCI
语种	英语
资助项目	National Key R&D Program of China[2022YFB4601000] ; National Natural Science Foundation of China (NSFC)[52171128] ; National Natural Science Foundation of China (NSFC)[51971060] ; Fundamental Research Project of Shenyang National Laboratory for Materials Science[L2019R18]
WOS研究方向	Chemistry ; Materials Science ; Metallurgy & Metallurgical Engineering ; Physics
WOS类目	Chemistry, Physical ; Materials Science, Multidisciplinary ; Metallurgy & Metallurgical Engineering ; Physics, Applied ; Physics, Condensed Matter
WOS记录号	WOS:001083341400001
出版者	MDPI
引用统计	被引频次：4[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/321006/179495
专题	中国科学院金属研究所
通讯作者	Zhang, Guang-Ping
作者单位	1.Chinese Acad Sci, Inst Met Res, Shenyang Natl Lab Mat Sci, 72 Wenhua Rd, Shenyang 110016, Peoples R China 2.Univ Sci & Technol China, Sch Mat Sci & Engn, Shenyang 110016, Peoples R China 3.Northeastern Univ, Sch Mat Sci & Engn, Minist Educ, Key Lab Anisotropy & Texture Mat, 3-11 Wenhua Rd, Shenyang 110819, Peoples R China
推荐引用方式 GB/T 7714	Hu, Bing-Li,Luo, Yan-Wen,Zhang, Bin,et al. A Comparative Investigation of Machine Learning Algorithms for Pore-Influenced Fatigue Life Prediction of Additively Manufactured Inconel 718 Based on a Small Dataset[J]. MATERIALS,2023,16(19):23.
APA	Hu, Bing-Li,Luo, Yan-Wen,Zhang, Bin,&Zhang, Guang-Ping.(2023).A Comparative Investigation of Machine Learning Algorithms for Pore-Influenced Fatigue Life Prediction of Additively Manufactured Inconel 718 Based on a Small Dataset.MATERIALS,16(19),23.
MLA	Hu, Bing-Li,et al."A Comparative Investigation of Machine Learning Algorithms for Pore-Influenced Fatigue Life Prediction of Additively Manufactured Inconel 718 Based on a Small Dataset".MATERIALS 16.19(2023):23.