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高周疲劳中轴向和弯曲载荷作用下疲劳强度的概率估计

Probabilistic Estimation of Fatigue Strength for Axial and Bending Loading in High-Cycle Fatigue.

作者信息

Tomaszewski Tomasz, Strzelecki Przemysław, Mazurkiewicz Adam, Musiał Janusz

机构信息

Faculty of Mechanical Engineering, University of Science and Technology, al. Prof. S. Kaliskiego 7, 85-796 Bydgoszcz, Poland.

出版信息

Materials (Basel). 2020 Mar 5;13(5):1148. doi: 10.3390/ma13051148.

DOI:10.3390/ma13051148
PMID:32150873
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7085001/
Abstract

In this paper, the sensitivity to the type of loads (axial and bending loading) of selected construction materials (AW6063 T6 aluminum alloy, S355J2+C structural steel, and 1.4301 acid-resistant steel) in high-cycle fatigue was verified. The obtained fatigue characteristics were described by a probabilistic model of the 3-parameters Weibull cumulative distribution function. The main area of research concerned the correct implementation of the weakest link theory model. The theory is based on a highly-stressed surface area and a highly-stressed volume in the region of the highest stresses. For this purpose, an analytical model and a numerical model based on the finite element method were used. The model that gives the lowest error implemented in specific test conditions was determined on the basis of high-cycle fatigue analysis. For the analyzed materials, it was a highly-stressed volume model based on the weakest link theory.

摘要

本文验证了选定建筑材料(AW6063 T6铝合金、S355J2+C结构钢和1.4301耐酸钢)在高周疲劳中对载荷类型(轴向和弯曲载荷)的敏感性。所获得的疲劳特性由三参数威布尔累积分布函数的概率模型描述。主要研究领域涉及最弱链理论模型的正确实施。该理论基于高应力表面积和最高应力区域中的高应力体积。为此,使用了基于有限元法的解析模型和数值模型。基于高周疲劳分析确定了在特定测试条件下误差最低的模型。对于所分析的材料,它是基于最弱链理论的高应力体积模型。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b2e/7085001/b14678420da5/materials-13-01148-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b2e/7085001/b9f3f0a9095a/materials-13-01148-g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b2e/7085001/b8f1419ef540/materials-13-01148-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b2e/7085001/0709f5242d0e/materials-13-01148-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b2e/7085001/26dda7510b7f/materials-13-01148-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b2e/7085001/7211b58038e8/materials-13-01148-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b2e/7085001/7d6345ddd876/materials-13-01148-g007a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b2e/7085001/b14678420da5/materials-13-01148-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b2e/7085001/b9f3f0a9095a/materials-13-01148-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b2e/7085001/3140144ca149/materials-13-01148-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b2e/7085001/b8f1419ef540/materials-13-01148-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b2e/7085001/0709f5242d0e/materials-13-01148-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b2e/7085001/26dda7510b7f/materials-13-01148-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b2e/7085001/7211b58038e8/materials-13-01148-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b2e/7085001/7d6345ddd876/materials-13-01148-g007a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b2e/7085001/b14678420da5/materials-13-01148-g008.jpg

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