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应用阿姆斯特朗-弗雷德里克运动硬化模型对AISI316L钢的低周疲劳行为进行建模。

Modeling of LCF Behaviour on AISI316L Steel Applying the Armstrong-Frederick Kinematic Hardening Model.

作者信息

Pate Sushant Bhalchandra, Dundulis Gintautas, Griskevicius Paulius

机构信息

Faculty of Mechanical Engineering and Design, Kaunas University of Technology, K. Donelaičio g. 73, 44249 Kaunas, Lithuania.

出版信息

Materials (Basel). 2024 Jul 9;17(14):3395. doi: 10.3390/ma17143395.

DOI:10.3390/ma17143395
PMID:39063685
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11278255/
Abstract

The combination of kinematic and isotropic hardening models makes it possible to model the behaviour of cyclic elastic-plastic steel material, though the estimation of the hardening parameters and catching the influence of those parameters on the material response is a challenging task. In the current work, an approach for the numerical simulation of the low-cycle fatigue of AISI316L steel is presented using a finite element method to study the fatigue behaviour of the steel at different strain amplitudes and operating temperatures. Fully reversed uniaxial LCF tests are performed at different strain amplitudes and operating temperatures. Based on the LCF test experimental results, the non-linear isotropic and kinematic hardening parameters are estimated for numerical simulation. On comparing, the numerical simulation results were in very good agreement with those of the experimental ones. This presented method for the numerical simulation of the low-cycle fatigue on AISI316 stainless steel can be used for the approximate prediction of the fatigue life of the components under different cyclic loading amplitudes.

摘要

运动硬化模型和各向同性硬化模型的结合使得对循环弹塑性钢材的行为进行建模成为可能，尽管硬化参数的估计以及掌握这些参数对材料响应的影响是一项具有挑战性的任务。在当前工作中，提出了一种使用有限元方法对AISI316L钢的低周疲劳进行数值模拟的方法，以研究该钢在不同应变幅值和工作温度下的疲劳行为。在不同应变幅值和工作温度下进行了完全反向单轴低周疲劳试验。基于低周疲劳试验的实验结果，估计了用于数值模拟的非线性各向同性和运动硬化参数。经比较，数值模拟结果与实验结果非常吻合。这种提出的对AISI316不锈钢低周疲劳进行数值模拟的方法可用于近似预测不同循环载荷幅值下部件的疲劳寿命。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a0c/11278255/79e930e7f3bf/materials-17-03395-g010.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a0c/11278255/5642c35965bd/materials-17-03395-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a0c/11278255/1f743eabd2c8/materials-17-03395-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a0c/11278255/79e930e7f3bf/materials-17-03395-g010.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a0c/11278255/e852a9931b59/materials-17-03395-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a0c/11278255/0daa408afbdd/materials-17-03395-g007.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a0c/11278255/79e930e7f3bf/materials-17-03395-g010.jpg

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本文引用的文献

1
Prediction of 316 stainless steel low-cycle fatigue life based on machine learning.基于机器学习的 316 不锈钢低周疲劳寿命预测。
Sci Rep. 2023 Apr 25;13(1):6753. doi: 10.1038/s41598-023-33354-1.