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用于跨多种加载工况预测疲劳寿命的微观结构敏感临界塑性应变能密度准则。

Microstructure-sensitive critical plastic strain energy density criterion for fatigue life prediction across various loading regimes.

作者信息

Bandyopadhyay Ritwik, Prithivirajan Veerappan, Peralta Alonso D, Sangid Michael D

机构信息

School of Aeronautics and Astronautics, Purdue University, West Lafayette, IN, USA.

Honeywell Aerospace, Phoenix, AZ, USA.

出版信息

Proc Math Phys Eng Sci. 2020 Apr;476(2236):20190766. doi: 10.1098/rspa.2019.0766. Epub 2020 Apr 1.

DOI:10.1098/rspa.2019.0766
PMID:32398935
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7209147/
Abstract

In the present work, we postulate that a critical value of the stored plastic strain energy density (SPSED) is associated with fatigue failure in metals and is independent of the applied load. Unlike the classical approach of estimating the (homogenized) SPSED as the cumulative area enclosed within the macroscopic stress-strain hysteresis loops, we use crystal plasticity finite element simulations to compute the (local) SPSED at each material point within polycrystalline aggregates of a nickel-based superalloy. A Bayesian inference method is used to calibrate the critical SPSED, which is subsequently used to predict fatigue lives at nine different strain ranges, including strain ratios of 0.05 and -1, using nine statistically equivalent microstructures. For each strain range, the predicted lives from all simulated microstructures follow a lognormal distribution. Moreover, for a given strain ratio, the predicted scatter is seen to be increasing with decreasing strain amplitude; this is indicative of the scatter observed in the fatigue experiments. Finally, the lognormal mean lives at each strain range are in good agreement with the experimental evidence. Since the critical SPSED captures the experimental data with reasonable accuracy across various loading regimes, it is hypothesized to be a material property and sufficient to predict the fatigue life.

摘要

在本研究中,我们假设储存的塑性应变能密度(SPSED)的临界值与金属中的疲劳失效相关,且与施加的载荷无关。与将(均匀化的)SPSED估计为宏观应力 - 应变滞后回线内包围的累积面积的经典方法不同,我们使用晶体塑性有限元模拟来计算镍基高温合金多晶聚集体内每个材料点处的(局部)SPSED。采用贝叶斯推理方法来校准临界SPSED,随后使用九个统计等效的微观结构来预测九个不同应变范围(包括应变比为0.05和 -1)下的疲劳寿命。对于每个应变范围,所有模拟微观结构预测的寿命都遵循对数正态分布。此外,对于给定的应变比,预测的离散度随应变幅值减小而增大;这与疲劳实验中观察到的离散度一致。最后,每个应变范围的对数正态平均寿命与实验结果吻合良好。由于临界SPSED在各种加载条件下都能以合理的精度捕捉实验数据,因此推测它是一种材料属性,足以预测疲劳寿命。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd60/7209147/3c31ad1a8020/rspa20190766-g10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd60/7209147/56c833967db8/rspa20190766-g1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd60/7209147/6f3ecdb1a513/rspa20190766-g2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd60/7209147/8c3a7d316254/rspa20190766-g3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd60/7209147/e75066e8e4be/rspa20190766-g4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd60/7209147/f625d90156e6/rspa20190766-g5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd60/7209147/177896772888/rspa20190766-g6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd60/7209147/b15c83395abe/rspa20190766-g7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd60/7209147/dd440a225b68/rspa20190766-g8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd60/7209147/b8f66b71300f/rspa20190766-g9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd60/7209147/3c31ad1a8020/rspa20190766-g10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd60/7209147/56c833967db8/rspa20190766-g1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd60/7209147/6f3ecdb1a513/rspa20190766-g2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd60/7209147/8c3a7d316254/rspa20190766-g3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd60/7209147/e75066e8e4be/rspa20190766-g4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd60/7209147/f625d90156e6/rspa20190766-g5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd60/7209147/177896772888/rspa20190766-g6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd60/7209147/b15c83395abe/rspa20190766-g7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd60/7209147/dd440a225b68/rspa20190766-g8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd60/7209147/b8f66b71300f/rspa20190766-g9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd60/7209147/3c31ad1a8020/rspa20190766-g10.jpg

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