A hardness-based constitutive model for numerical simulation of blind rivet with gradient hardness distribution

被引:1
|
作者
Tao, Liang [1 ,2 ]
Feng, Zhiguo [1 ,3 ]
Jiang, Yulian [1 ]
Mo, Ningning [1 ]
Liu, Yong [1 ]
机构
[1] Guizhou Univ, Sch Mech Engn, Guizhou, Peoples R China
[2] Guizhou Inst Technol, Sch Mech Engn, Guiyang, Peoples R China
[3] Guizhou Univ, Key Lab Special Equipment & Mfg Technol, Guiyang 550025, Peoples R China
来源
MATERIALS TODAY COMMUNICATIONS | 2024年 / 39卷
基金
中国国家自然科学基金;
关键词
Constitutive model; Microstructural evolution; A286; Superalloy; Blind rivet; Numerical simulation; JOHNSON-COOK; PARAMETERS; BEHAVIOR; PREDICT;
D O I
10.1016/j.mtcomm.2024.109331
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
A286 superalloy blind rivets find extensive applications in the structural connections of aerospace equipment owing to their exceptional strength and heat resistance. However, the gradient hardness distribution within the rivet sleeves complicates achieving precise numerical simulation results with traditional constitutive models. This study aims to establish a constitutive model suitable for materials exhibiting gradient hardness to support the development of A286 superalloy blind rivets. Initially, quasi-static compression experiments were conducted on A286 superalloy samples at different hardness levels (160-324 HV), strain rates (0.01-10 s-1), and temperatures (25-600 degrees C). Subsequently, the influence of material microstructural evolution on flow stress during compression was investigated using electron backscatter diffraction technique. Furthermore, a hardness-based Johnson-Cook (J-C) constitutive model was developed by introducing hardness and offset compensation onto the original J-C model and compared with its counterpart. Finally, the hardness-based model was employed to simulate the bulge compression forming of A286 blind rivets and validated through experimental trials. The findings reveal that dislocation strengthening predominantly contributes to the increased material flow stress with escalating strain rates and sample hardness. Compared to the original J-C model, the stress prediction error of the hardness-based model reduced from 26.8 % to 2.9 %, effectively depicting the variation in material flow stress under different hardness values. The simulated values of bulge morphology, dimensions, and load closely align with experimental data, affirming the efficacy of the hardness-based model in numerically simulating blind rivets with gradient hardness distribution.
引用
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页数:13
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