Abstract
Purpose
The study aims to provide a basis for the effective use of safety-related information data and a quantitative assessment way for the occurrence probability of the safety risk such as the fatigue fracture of the key components.
Design/methodology/approach
The fatigue crack growth rate is of dispersion, which is often used to accurately describe with probability density. In view of the external dispersion caused by the load, a simple and applicable probability expression of fatigue crack growth rate is adopted based on the fatigue growth theory. Considering the isolation among the pairs of crack length a and crack formation time t (a∼t data) obtained from same kind of structural parts, a statistical analysis approach of t distribution is proposed, which divides the crack length in several segments. Furthermore, according to the compatibility criterion of crack growth, that is, there is statistical development correspondence among a∼t data, the probability model of crack growth rate is established.
Findings
The results show that the crack growth rate in the stable growth stage can be approximately expressed by the crack growth control curve da/dt = Q•a, and the probability density of the crack growth parameter Q represents the external dispersion; t follows two-parameter Weibull distribution in certain a values.
Originality/value
The probability density f(Q) can be estimated by using the probability model of crack growth rate, and a calculation example shows that the estimation method is effective and practical.
Keywords
Citation
Xi, N. and Xu, H. (2023), "Probability estimation method for fatigue crack growth rate based on
Publisher
:Emerald Publishing Limited
Copyright © 2023, Niansheng Xi and Hongmin Xu
License
Published in Railway Sciences. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
1. Introduction
Metal components are widely used in various mechanical-related industries, and fatigue fracture of key components is one of the most serious safety risks. As a key parameter, crack growth rate can well describe the evolution law of the fatigue fracture of key components. Therefore, as a prerequisite for safe use, quantitative estimation of crack growth rate becomes an important content to predict and evaluate the damage tolerance of metal components. However, the process of fatigue crack growth is usually dispersive, which means that the distribution of crack growth rate may not be properly described by the mean crack growth rate.
The dispersion of crack growth rate can be divided into two types according to different sources: one is inherent dispersion coming from metal components, which is mainly due to the material metallurgy and manufacturing processes; and the other is external dispersion caused by external load and environment (Zhong, Jin, Hong, & Tao, 2000; James, 2001). With the development of material metallurgy and manufacturing technology, inherent dispersion is effectively controlled. Especially when the samples are used to simulate the actual components in the laboratory, the effect of the dispersion of the samples on the random crack growth can be ignored so that the effect of external dispersion on crack growth rate can be well investigated. For the external dispersion, the loading process is generally regarded as a steady-state stochastic process (Yang & Yao, 1995). For example, rails, wheels, bogies and other components while running a railway rolling stock are subjected to steady, random external loads.
In practical engineering, the inherent dispersion and the external dispersion must exist at the same time, which will both affect the crack propagation. Obviously, when the inherent dispersion is small, the load dispersion is the main influence factor of the random crack growth. Though the distribution law of crack growth rate can be obtained by testing a large number of specimens, the cost is too high to bear especially for large-scale metal components. As a result, it is much difficult to evaluate the fatigue crack growth rate with probability.
Yang and Yao (1995) reviewed the random crack growth models considering the two kinds of dispersions, respectively, and suggest that the initiation and propagation of crack because of no exact physical description may be regarded as a “black box” for phenomenological study. The average growth rate model based on the Paris formula was used by Dui, Liu, Wang, and Dong (2020) to describe the average crack growth under the random load spectrum, which was treated as the “equivalent constant amplitude spectrum” so as to reflect all the complicated load sequence effects and other effects, and high-precision prediction of crack growth rate under the random load spectrum has been achieved. Many scholars pay more attention to the durability problem caused by the inherent dispersion and focus on the methods of determining the relatively small crack growth rate (Mannig & Yang, 1984; He, 1994; Zuo, He, & Li, 2021; Chen, Bao, Zhang, & Fei, 2004; Barter, Athiniotis & Clark, 1997). In other literature (Zhang, Chen, & Huang, 2003; Jin, Zhong, Hong, & Xiong, 2000; Wang, Fang, Li, & Liu, 2021), it is emphasized that the distribution of crack growth life is deduced by using the existing data or the incomplete data of fatigue crack growth tests, so that the reliability analysis of crack growth can be carried out. Although much research has been done on fatigue crack growth, the proposed models or methods are mainly based on the analysis and modeling of correlation data.
In the industrial field, many times the same type of metal components are used in large quantities. For example, the 840D wheel was once massively used in the railway freight car, and its material metallurgy, the processing manufacture and so on were identical. In practice, the common mode failure often occurs, such as the fatigue crack propagation of the same type of metal components with one mechanism at the same positions. Recently, with the development and wide application of big data information systems, it has become more and more practical to collect a large amount of
In this paper, based on the isolated
2. Statistical method for analyzing t distribution with data
2.1 Distribution of time to crack formation
A large number of studies have shown that the time to form a given length of fatigue crack obeys a three-parameter Weibull distribution. For the fatigue of metal components, it is entirely possible that there is a certain length of crack in the initial period of service (
In the formula,
—crack length; —given crack length; —cumulative distribution function of under given ; —time to crack formation; —Weibull shape parameter under given , closely related to the mechanism of crack propagation; —Weibull scale parameter under given , mainly related to fatigue load spectrum.
For a large number of
In the formula,
—Weibull shape parameter under arbitrary crack length.
2.2 data collation
For the fatigue crack under service conditions, we may get many isolated
In the formula,
—the group amount of total data; , — data of No. ; —crack length in the data; —time to crack formation in the data.
In order to get the
The volume of the crack length segments should not be too small, so as to avoid large error in fitting the probability density of the crack growth parameter. Generally, it should be greater than or equal to 5.
In order to well fit the probability density of
, the sample number of each crack segment should be as much as possible.The range of each crack length segment should not be too large. In this way, the average crack length can be used to represent the given crack length.
Let
In the formula,
—probability density function of under given crack length ; —cumulative distribution function of under given crack length ; —average crack length of No. segment; —the volume of the crack length segments, 5.
At this point, Formula (2) should be rewritten accordingly as follows:
2.3 Weibull parameters fitting under different crack lengths
First, based on all the
Then, for each crack length segment, using statistical analysis software, set the Weibull shape parameter as the obtained
In order to guarantee the validity of the parameter fitting, it is necessary to check the correlation coefficient given by the fitting. In general, a correlation coefficient greater than 0.9 can meet the engineering needs.
3. Fatigue crack growth model based on load dispersion
3.1 Function relationship between a and t
At the stable stage of crack propagation, the crack growth rate
In the formula,
—crack growth rate; —stress range; 、 —constants relating to the material; —shape factor.
When the external load exists dispersion, the fatigue stress range is not a constant value, Formula (6) can be rewritten as:
In the formula,
—crack growth parameter, relating to the load spectrum; —constant relating to the material.
Comparing Formula (6) and Formula (7) shows that the dispersion of external load is entirely reflected by the dispersion of
In the formula,
Formula (7) and Formula (8) are consistent with the formulas for crack growth in structural durability and probabilistic damage tolerance analysis (Yang & Yao, 1995; Dui et al., 2020; Mannig & Yang, 1984; He, 1994; Zuo et al., 2021; Chen et al., 2004). The crack growth parameter
3.2 Probability model of crack growth rate
It is obvious that for the
As shown in Figure 1, the crack propagation is controlled by the crack growth curve
In the formula,
—reliability for , often set to ; —time to crack formation under given Rk、 ; —number of set values of reliability ; —cumulative distribution of under given Rk、 ; —cumulative distribution of under given 、 .
3.3 Estimation of crack growth parameter
Under each given
In the formula,
—crack growth parameter under given 、 ; —average crack growth parameter under given .
Up to this point,
Many load spectrums are regarded as steady-state stochastic processes in engineering, for example, the load spectrum while trains are running on railway lines. Therefore, it is reasonable to take the crack growth parameter as a normal distribution, that is,
—mathematical expectation parameter of normal distribution; —deviation parameter of Normal distribution.
As a result, probability estimation of crack growth parameter can be performed after determining normal distribution parameters by fitting a series of
4. Case demonstration and verification
4.1 The data from service
A kind of freight car wheel has been widely used on railway, and a large number of wheels have cracked due to fatigue at the spoke hole location. It is shown that when the crack length
A total of 3,949 pairs of
4.2 Determination of probability density of t under various a
According to the total
4.3 Distribution function of Q
Given
The normal distribution parameters are estimated based on the 19
5. Conclusions
The crack growth rate can be approximately expressed by
, and the probability density of crack growth parameter represents the external dispersion for the fatigue cracking mode of the same kind of components.A large amount of
data from service are isolated each other, but there is a corresponding relationship of statistical development, which can be reflected by dividing the crack length range into several segments and statistically analyzing the distribution of time to crack formation.According to the compatibility criterion of crack propagation, the probability model of crack growth under different reliability is established, and the probability density of
can be estimated based on the data from service.A case study is carried out to demonstrate the method application, and it verifies that the estimation method is effective and practical.
Figures
Weibull distributions of t under different crack lengths
j | Range of segment/mm | Parameters of Weibull distribution | ||||
---|---|---|---|---|---|---|
R2 | Quantity of data points | |||||
1 | (0, 3) | 1.88 | 3.285 | 14.28 | 0.9934 | 75 |
2 | [3, 6) | 4.40 | 14.17 | 0.9973 | 776 | |
3 | [6, 9) | 7.03 | 14.83 | 0.9970 | 740 | |
4 | [9, 12) | 9.99 | 14.60 | 0.9964 | 802 | |
5 | [12, 15) | 12.63 | 15.34 | 0.9903 | 401 | |
6 | [15, 18) | 15.36 | 15.09 | 0.9950 | 501 | |
7 | [18, 21) | 19.42 | 15.11 | 0.9968 | 332 | |
8 | [21, 24) | 21.97 | 15.98 | 0.9584 | 93 | |
9 | [24, 27) | 24.97 | 16.46 | 0.9917 | 119 | |
10 | [27, 30) | 27.72 | 15.70 | 0.9401 | 29 | |
11 | [30, 33) | 30.46 | 15.73 | 0.9713 | 54 | |
12 | [33, 35] | 34.56 | 16.40 | 0.9398 | 27 |
Source(s): Authors own work
Crack growth parameter values
k | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
---|---|---|---|---|---|---|---|---|---|---|
0.05 | 0.10 | 0.15 | 0.20 | 0.25 | 0.30 | 0.35 | 0.40 | 0.45 | 0.50 | |
0.7095 | 0.7687 | 0.8153 | 0.8572 | 0.897 | 0.9364 | 0.9763 | 1.0175 | 1.0611 | 1.1078 | |
k | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | |
0.55 | 0.60 | 0.65 | 0.70 | 0.75 | 0.80 | 0.85 | 0.90 | 0.95 | ||
1.1588 | 1.2156 | 1.2804 | 1.3561 | 1.4478 | 1.5642 | 1.7227 | 1.9657 | 2.4472 |
Source(s): Authors own work
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Acknowledgements
This research was supported by the China National Railway Group Co., Ltd. Research and Development Project (N2022T008).