Pore-affected fatigue life scattering and prediction of additively manufactured Inconel 718: An investigation based on miniature specimen testing and machine learning approach

被引：0

作者：

Luo, Y.W. ^{[1
]}

Zhang, B. ^{[1
]}

Feng, X. ^{[1
]}

Song, Z.M. ^{[2
]}

Qi, X.B. ^{[3
]}

Li, C.P. ^{[3
]}

Chen, G.F. ^{[3
]}

Zhang, G.P. ^{[2
]}

机构：

[1] Key Laboratory for Anisotropy and Texture of Materials, Ministry of Education, School of Materials Science and Engineering, Northeastern University, 3-11 Wenhua Road, Shenyang,110819, China

[2] Shenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences, 72 Wenhua Road, Shenyang,110016, China

[3] Materials & Manufacturing Qualification Group, Corporate Technology Siemens Ltd China, Beijing,100102, China

来源：

Materials Science and Engineering: A | 2021年 / 802卷

基金：

中国国家自然科学基金;

关键词：

Additives - Fatigue of materials - Forecasting - Learning algorithms - Machine learning - Melting - Selective laser melting;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

Fatigue life scattering and prediction of Inconel 718 fabricated by selective laser melting were investigated using miniature specimen tests combined with statistical method and machine learning algorithms. The relationship between pore features and fatigue life of the selective laser melting-fabricated specimens was analyzed statistically. The results show that the increase in the size and/or the number of the pores in the specimens, and/or the decrease in the distance from a pore center to the specimen surface degraded the fatigue life. The machine learning and statistical analysis results reveal that the fatigue life are most closely related to the location of the pores compared with the size and the number of pores in the specimens. The finding may provide a potential way to get high-throughput statistical data helping in evaluating defect-dominated scattering and prediction of fatigue life of additive manufactured metallic parts using miniature specimen testing assisted by the machine learning approach. © 2020 Elsevier B.V.

引用

共 22 条

[21] A novel approach based on the elastoplastic fatigue damage and machine learning models for life prediction of aerospace alloy parts fabricated by additive manufacturing
Zhan, Zhixin
Li, Hua
INTERNATIONAL JOURNAL OF FATIGUE, 2021, 145
[22] Pore-induced fatigue failure: A prior progressive fatigue life prediction framework of laser-directed energy deposition Ti-6Al-4V based on machine learning
Dang, Linwei
He, Xiaofan
Tang, Dingcheng
Xin, Hao
Zhan, Zhixin
Wang, Xiangming
Wu, Bin
THEORETICAL AND APPLIED FRACTURE MECHANICS, 2024, 130

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