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对威布尔参数进行修改，以在疲劳应用中获得更精确的概率分布函数。

A modification in Weibull parameters to achieve a more accurate probability distribution function in fatigue applications.

作者信息

Fakoor H, Alizadeh Kaklar J

机构信息

Urmia University, Urmia, Iran.

出版信息

Sci Rep. 2023 Oct 16;13(1):17537. doi: 10.1038/s41598-023-44907-9.

DOI:10.1038/s41598-023-44907-9
PMID:37845362
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10579224/
Abstract

Risk evaluation for fatigue failure of the engineering components is an important aspect of the engineering design. Weibull distributions are often used in preference to the log-normal distribution to analyze probability aspects of fatigue results. This study presents a probabilistic model for calculating Weibull distribution parameters to reduce the effect of percentage discretization error of experimental fatigue life and R-S-N curves for three reliability levels. By considering any result of standard fatigue test as an equivalent Weibull distribution, artificial data are generated and the accuracy of common Weibull distribution model can be improved. The results show error reduction in the Kolmogorov-Smirnov test and R-square values. Also, the Basquin model is used for different reliability levels with the same error order for risk evaluation of fatigue failure. The coefficient of variation for fatigue life increases at higher stress levels and has a linear relation with stress level for a high-cycle fatigue regime.

摘要

工程部件疲劳失效的风险评估是工程设计的一个重要方面。在分析疲劳结果的概率方面，威布尔分布通常比对数正态分布更受青睐。本研究提出了一种用于计算威布尔分布参数的概率模型，以减少实验疲劳寿命和三个可靠性水平的R-S-N曲线的百分比离散化误差的影响。通过将标准疲劳试验的任何结果视为等效威布尔分布，生成人工数据，并提高常见威布尔分布模型的准确性。结果表明，柯尔莫哥洛夫-斯米尔诺夫检验和R平方值的误差减小。此外，巴斯昆模型用于不同可靠性水平，疲劳失效风险评估的误差顺序相同。在高应力水平下，疲劳寿命的变异系数增加，并且在高周疲劳状态下与应力水平呈线性关系。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/40aa/10579224/89fbffb314b4/41598_2023_44907_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/40aa/10579224/78e6a4b7882a/41598_2023_44907_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/40aa/10579224/2f22721f804b/41598_2023_44907_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/40aa/10579224/f402b196baed/41598_2023_44907_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/40aa/10579224/ed4851c3927c/41598_2023_44907_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/40aa/10579224/ac86230755d7/41598_2023_44907_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/40aa/10579224/89fbffb314b4/41598_2023_44907_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/40aa/10579224/78e6a4b7882a/41598_2023_44907_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/40aa/10579224/2f22721f804b/41598_2023_44907_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/40aa/10579224/f402b196baed/41598_2023_44907_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/40aa/10579224/ed4851c3927c/41598_2023_44907_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/40aa/10579224/ac86230755d7/41598_2023_44907_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/40aa/10579224/89fbffb314b4/41598_2023_44907_Fig6_HTML.jpg

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