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金属学报  2016, Vol. 52 Issue (3): 289-297    DOI: 10.11900/0412.1961.2015.00322
  论文 本期目录 | 过刊浏览 |
微结构相关的高循环疲劳分散性计算方法研究*
韩世伟1,石多奇1,2,杨晓光1,2,苗国磊1
1 北京航空航天大学能源与动力工程学院, 北京 100191
2 先进航空发动机协同创新中心, 北京 100191
COMPUTATIONAL STUDY ON MICROSTRUCTURE-SENSITIVE HIGH CYCLE FATIGUE DISPERSIVITY
Shiwei HAN1,Duoqi SHI1,2,Xiaoguang YANG1,2,Guolei MIAO1
1) School of Energy and Power Engineering, Beihang University, Beijing 100191, China
2) the Collaborative Innovation Center for Advanced Aero-Engine (CICAAE), Beijing 100191, China;
引用本文:

韩世伟, 石多奇, 杨晓光, 苗国磊. 微结构相关的高循环疲劳分散性计算方法研究*[J]. 金属学报, 2016, 52(3): 289-297.
Shiwei HAN, Duoqi SHI, Xiaoguang YANG, Guolei MIAO. COMPUTATIONAL STUDY ON MICROSTRUCTURE-SENSITIVE HIGH CYCLE FATIGUE DISPERSIVITY[J]. Acta Metall Sin, 2016, 52(3): 289-297.

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摘要: 

为了研究微结构对高循环疲劳分散性的影响, 发展了考虑多晶材料微结构特征的极值概率分析方法. 首先, 通过Voronoi算法构造了近似多晶合金微结构的随机多晶胞元模型. 其次, 应用基于内变量的晶体塑性本构理论, 模拟了不同应变幅下处于结构表面和内部多晶微结构胞元的循环应力应变响应. 通过计算有限数量的随机多晶微结构, 采用疲劳指示参数表征剪应变主导的裂纹萌生驱动力, 从而得到不同应变及边界约束情形下的疲劳指示参数分布. 最后, 应用极值概率理论分析了多晶胞元中疲劳指示参数的极值分布规律. 以TC4合金为例, 计算结果表明: 高循环疲劳分散性随应变幅降低而上升, 且在弹性极限附近变化显著; 此外, 相比于构件内部晶粒, 处于表面的晶粒具有更高的裂纹萌生驱动力.

关键词 高循环疲劳疲劳分散性极值概率多晶微结构晶体塑性    
Abstract

Empirical approaches to characterize the variability of high cycle fatigue have been widely used. However, little is understood about the intrinsic relationship of randomness of microstructure attributes on the overall variability in high cycle fatigue. The ability of quantifying the dispersivity of high cycle fatigue with physics based computational methods has great potential in design of minimum life and can aid in the improvement of fatigue resistance. To investigate the effects between microstructure attributes and high cycle fatigue dispersivity, the microstructure-sensitive extreme value probabilistic framework is introduced. First, the Voronoi algorithm is used to construct random polycrystalline microstructure representative volume elements. Different kinds of periodic boundary conditions are proposed to simulate the interior and surface constraints in polycrystalline microstructure representative volume elements. Then mechanical responses of both interior and surface microstructure representative volume elements under different strain amplitudes are simulated by internal state variable based crystal plasticity. The fatigue indicator parameter is introduced to characterize the driving force for fatigue crack formation dominated by maximum shear plastic strain amplitude. By computing a limited number of random polycrystalline microstructure representative volume elements, the distributions of fatigue indicator parameter under different strain amplitudes are obtained and analyzed with extreme value probability theory. The study with a kind of titanium alloy with material grade TC4 supports that the high cycle fatigue dispersivity increases with the decrease of the strain amplitude, especially under elastic limit. The extreme value of fatigue indicator parameter from random polycrystalline microstructure representative volume elements correlates well with the Gumbel extreme value distribution. Besides, the lower the average stress under different strain amplitudes, the fewer grains in polycrystalline microstructure representative volume element yield. Moreover, the grains on surface tend to have higher probability to initiate fatigue cracks and lower dispersivity in fatigue crack formation.

Key wordshigh cycle fatigue    fatigue dispersivity    extreme value probability    polycrystalline microstructure    crystal plasticity
收稿日期: 2015-06-23     
基金资助:*国家重点基础研究发展计划资助项目2015CB057400
图1  微结构相关的极值概率计算框架
图2  几何建模
图3  表面和内部对应的周期边界条件
图4  晶粒体积概率密度分布
图5  微结构胞元中晶粒体积的标准差频数分布
Case number Strain amplitude Δε Δε p (under monotonic tension) Spatial constraint
1 0.45% 0 Interior
2 0.50% 0 Interior
3 0.60% 0.0145% Interior
4 0.70% 0.0489% Interior
5 0.70% 0.0489% Surface
表1  微结构胞元计算参数
图6  TC4合金的单调拉伸曲线及塑性应变增量
图7  不同应变幅下初生α相晶粒FIP概率密度分布
图8  不同应变幅下胞元中FIP均值和标准差分布
图9  0.70%应变幅内部和表面多晶胞元中FIP 均值和标准差分布
图10  不同应变幅下微结构胞元中FIP 极值概率分布
图11  0.45%应变幅下微结构胞元中FIP 极值概率分布
Case number un bn bn/un R2
1 5.6×10-7 3.410×10-6 6.0842 0.7934
2 1.6×10-4 2.090×10-4 1.3025 0.9064
3 3.9×10-3 3.862×10-4 0.0976 0.9675
4 7.3×10-3 6.541×10-4 0.0896 0.9808
5 8.2×10-3 7.046×10-4 0.0859 0.9910
表2  Gumbel分布拟合参数
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