Modeling of Crack Susceptibility of Ni-Based Superalloy for Additive Manufacturing via Thermodynamic Calculation and Machine Learning

doi:10.11900/0412.1961.2023.00050

IMR OpenIR

	Modeling of Crack Susceptibility of Ni-Based Superalloy for Additive Manufacturing via Thermodynamic Calculation and Machine Learning
	Mu Yahang 1,2; Zhang Xue 1,2; Chen Ziming 3; Sun Xiaofeng 1; Liang Jingjing 1; Li Jinguo 1; Zhou Yizhou 1
通讯作者	Sun Xiaofeng(xfsun@imr.ac.cn) ; Liang Jingjing(jjliang@imr.ac.cn)
	2023-08-11
发表期刊	ACTA METALLURGICA SINICA
ISSN	0412-1961
卷号	59 期号:8 页码:1075-1086
摘要	The rapid development of aeroengines has led to high demand heat resistant blades. As a result, fabricating techniques and designing materials have taken center stage in producing aeroengines. Additive manufacturing (AM), which integrates design and manufacturing, has advantages in preparing blades with complex cavity structures. However, commercial Ni-based superalloys have poor additive manufacturability and are prone to defects such as cracks, severely hindering the development of the AM of superalloy blades. Therefore, finding a high-performance superalloy with excellent additive manufacturability is necessary. To alleviate this problem, many crack susceptibility criteria and test methods have recently been proposed to evaluate the crack susceptibility of alloys from a compositional and/or process point of view. However, the rapid prediction of the crack susceptibility of superalloys remains a challenge, hindering the widespread screening and designing of superalloys for AM. Nevertheless, using machine learning (ML) in conjunction with thermodynamic calculation may effectively predict the properties of alloys, and this combination is anticipated to grow as an important tool for designing alloys with low crack susceptibility for AM. Based on the aforementioned context, this study reports the development of an ML prediction model after combining experimental data and thermodynamic calculations to establish a Ni-based alloy crack susceptibility database. This ML model has an excellent prediction effect (R-2 = 0.96 on the training set and R-2 = 0.81 on the validation set) and enables accurate prediction of the crack susceptibility of the experimental alloys and published alloys. It is verified that a hot crack is the most typical type of crack in Ni-based superalloys during AM. The influence of elements on crack susceptibility is also analyzed using the SHapley Additive exPlanation method. Precipitation-strengthening (Al and Ti) and trace (C and B) elements greatly influence crack susceptibility. A small amount of Re can inhibit cracks, but excessive amounts produce a topologically close-packed phase, deteriorating the crack susceptibility and mechanical properties. The influence of other alloying elements on crack susceptibility is roughly ranked as follows: Re, W, Cr, Mo, Ta, and Co, which can provide a screening method for the composition design of subsequent AMed superalloys.
关键词	Ni-based superalloy crack susceptibility additive manufacturing machine learning thermodynamic calculation
资助者	National Science and Technology Major Project
DOI	10.11900/0412.1961.2023.00050
收录类别	SCI
语种	英语
资助项目	National Science and Technology Major Project[Y2019-VII-0011-0151] ; National Science and Technology Major Project[P2022-C-IV-002-001]
WOS研究方向	Metallurgy & Metallurgical Engineering
WOS类目	Metallurgy & Metallurgical Engineering
WOS记录号	WOS:001035775200010
出版者	SCIENCE PRESS
引用统计	被引频次：6[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/321006/178802
专题	中国科学院金属研究所
通讯作者	Sun Xiaofeng; Liang Jingjing
作者单位	1.Chinese Acad Sci, Inst Met Res, Shi Changxu Innovat Ctr Adv Mat, Shenyang 110016, Peoples R China 2.Univ Sci & Technol China, Sch Mat Sci & Engn, Shenyang 110016, Peoples R China 3.Univ Sci & Technol Beijing, Sch Intelligence Sci & Technol, Beijing 100083, Peoples R China
推荐引用方式 GB/T 7714	Mu Yahang,Zhang Xue,Chen Ziming,et al. Modeling of Crack Susceptibility of Ni-Based Superalloy for Additive Manufacturing via Thermodynamic Calculation and Machine Learning[J]. ACTA METALLURGICA SINICA,2023,59(8):1075-1086.
APA	Mu Yahang.,Zhang Xue.,Chen Ziming.,Sun Xiaofeng.,Liang Jingjing.,...&Zhou Yizhou.(2023).Modeling of Crack Susceptibility of Ni-Based Superalloy for Additive Manufacturing via Thermodynamic Calculation and Machine Learning.ACTA METALLURGICA SINICA,59(8),1075-1086.
MLA	Mu Yahang,et al."Modeling of Crack Susceptibility of Ni-Based Superalloy for Additive Manufacturing via Thermodynamic Calculation and Machine Learning".ACTA METALLURGICA SINICA 59.8(2023):1075-1086.