A fatigue life prediction approach for laser-directed energy deposition titanium alloys by using support vector regression based on pore-induced failures

被引：56

作者：

Dang, Linwei ^{[1
]}

He, Xiaofan ^{[1
]}

Tang, Dingcheng ^{[1
]}

Li, Yuhai ^{[1
]}

Wang, Tianshuai ^{[1
]}

机构：

[1] Beihang Univ, Sch Aeronaut Sci & Engn, Beijing 100191, Peoples R China

来源：

INTERNATIONAL JOURNAL OF FATIGUE | 2022年 / 159卷

基金：

中国国家自然科学基金;

关键词：

Fatigue life prediction; Machine learning; Pore; Microstructure; Additive manufacturing; POROSITY DEFECTS; PERFORMANCE; TI-6AL-4V; BEHAVIOR;

D O I：

10.1016/j.ijfatigue.2022.106748

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

A support vector regression (SVR) algorithm was chosen in this study to develop a fatigue life prediction model by post-mortem fractography analysis. Models based on the SVR algorithm with different input variables were compared to identify optimized input variables according to errs and correlation coefficients. Variations were verified in a stress intensity factor range obtained by Murakami's approach and for pores types determined by the relationship between pore size and microstructure size. The results confirm the importance of considering the fatigue behavior of pores and microstructures during crack initiation in the fatigue life prediction for additive manufacturing (AM) metals.

引用

页数：10

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