Fracture mechanism of Ti60 alloy at high temperature in very high cycle fatigue regime and fatigue life prediction

被引:0
|
作者
Zhou, Qikai [1 ,2 ]
Huang, Zhiyong [1 ]
Qian, Hongjiang [1 ]
Wang, Jian [1 ]
Shen, Zeshuai [1 ]
Pan, Kai [3 ]
Chen, Yonghui [4 ]
机构
[1] Sichuan Univ, Sch Aeronaut & Astronaut, Chengdu 610000, Peoples R China
[2] Facil Design & Instrumentat Inst, China Aerodynam Res & Dev Ctr, Mianyang, Peoples R China
[3] Chinese Aeronaut Estab, Beijing, Peoples R China
[4] Aircraft Strength Res Inst China, Natl Key Lab Strength & Struct Integr, Xian, Peoples R China
基金
中国国家自然科学基金;
关键词
fatigue life prediction; fracture mechanism; high temperature; Ti60; very high cycle fatigue; TITANIUM-ALLOY; CRACK INITIATION; STRESS RATIO; TI-6AL-4V ALLOY; BEHAVIOR; TENSILE; GROWTH; MICROSTRUCTURE; TEXTURE; EVOLUTION;
D O I
10.1111/ffe.14359
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
This study aimed to investigate the failure mechanism of Ti60 titanium alloy in the high cycle fatigue (HCF) and very high cycle fatigue (VHCF) at 500 degrees C. Ti60 specimens were characterized before and after the 500 degrees C VHCF test, and the fracture surfaces were observed. The results show that there are three different fatigue failure mechanisms at 500 degrees C: (i) equiaxed primary alpha phase (alpha p) cleavage, forming small unsmooth facets and rough fracture area (RA), and fusion of non-adjacent facets to grow into the main crack. (ii) alpha p grains gather into large grain with triple junction, and large grain slip to form large fusion facet. (iii) Oxide shedding and then cracks form. A dynamic recurrent neural network model is used to predict the fatigue life of Ti60 alloy, 91% of the overall predictions were within the scatter band of 3.0, and 100% were within the scatter band of 5.0. Characterization yields three fatigue fracture mechanisms of Ti60 alloy at 500 degrees C. Haigh diagrams evaluate the empirical formulas and constant life curves. Characterization reveals multiple triple junctions within the large alpha p grains after fatigue test. A dynamic recurrent neural network model predicts the fatigue life.
引用
收藏
页码:3285 / 3299
页数:15
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