An Optimization of Master <i>S</i>-<i>N</i> Curve Fitting Method Based on Improved Neighborhood Rough Set

Zou, Li; Ren, Siyuan; Li, Hongxin; Yang, Xinhua

doi:10.1109/access.2021.3049403

Cited by 8 publications

(4 citation statements)

References 35 publications

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“…To verify the performance of NRSBSG algorithm, we compared NRSBSG with FARNeMF (Hu 2008), NRSBCE (Zou et al 2021) and IFSANRSR (Zou et al2019a) on the selected 10 data sets. The parameter a is set to be 0.5, and the intra class radius I δ in NRSBSG is 0.1 which is the same as that in the other three algorithms.…”

Section: Reduction Results With Same Parametersmentioning

confidence: 99%

Attribute reduction algorithm of neighborhood rough set based on supervised granulation and its application

et al. 2022

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In neighborhood rough set theory, attribute reduction based on measure of information has important application significance.The influence of different decision classes was not considered for calculation of traditional conditional neighborhood entropy, and the improvement of algorithm based on conditional neighborhood entropy mainly includes of introducing multi granularity and different levels, while the mutual influence between samples with different labels is less considered. To solve this problem, this paper uses the supervised strategy to improve the conditional neighborhood entropy of three-layer granulation. By using two different neighborhood radii to adjust the mutual influence degree of different label samples, and by considering the mutual influence between conditional attributes through the feature complementary relationship, a neighborhood rough set attribute reduction algorithm based on supervised granulation is proposed. Experiment results on UCI data sets show that the proposed algorithm is superior to the traditional conditional neighborhood entropy algorithm in both aspects of reduction rate and reduction accuracy. Finally, the proposed algorithm is applied to the evaluation of fatigue life influencing factors of titanium alloy welded joints. The results of coupling relationship analysis show that the effect of joint type should be most seriously considered in the calculation of stress concentration factor. The results of influencing factors analysis show that the stress range has the highest weight among all the fatigue life influencing factors of titanium alloy welded joint.

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Section: Reduction Results With Same Parametersmentioning

confidence: 99%

Attribute reduction algorithm of neighborhood rough set based on supervised granulation and its application

et al. 2022

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“…where N P is the fatigue life of material when the survival probability is equal to P. In other words, N p is the probability percentile of random variable N . According to equation (20), N P can be rewritten as the following form…”

Section: General Model For Determining Probabilistic S-n Curve Of Mat...mentioning

confidence: 99%

“…Wang et al 19 considered the influences of failure mode and stress ratio, and a unified probabilistic S-N curve fitting model was presented based on the heterogeneous mixture distribution and Bayes clustering method. To reduce the dispersion of fatigue life data of material, Zou et al 20 developed an optimization method for fitting the S-N curve based on the conditional entropy and improved neighborhood rough set. Barbosa et al 21 reviewed the models used for constructing the probabilistic S-N curves of metallic and composite materials, and the fitting accuracy of these models was compared and analyzed.…”

Section: Introductionmentioning

confidence: 99%

A general method for determining probabilistic S-N curve based on static strength data of material

Gao

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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The conventional group test method for obtaining probabilistic S- N curve is a time-consuming and expensive test program. This study aims to present an alternative method for determining the probabilistic S- N curve of material based on its static strength data. Firstly, the probabilistic mapping relationship between the static strength and fatigue life of material is elaborated. Subsequently, the Weibull distribution is utilized to model the static strength and fatigue life data of material, and the correlation between the distribution parameters of them is investigated. Finally, an alternative method for determining the probabilistic S- N curve of material is proposed, and the experimental data of carbon steels and composite laminates are applied to verify its validity. The results show that the proposed method is capable of determining the probabilistic S- N curve of material based on its static strength data, which can significantly save test time and reduce test cost. In engineering applications, when the parameters of static strength distribution and the median S- N curve are known, the probabilistic S- N curve with any given survival probability can be determined through a unified analytical expression.

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“…Compared with other methods, for the calculation results and the accuracy of fatigue life, the nodal force based structural stress method can be used to analyze more precisely. We have studied the fatigue life prediction method for aluminum alloy welded joints in the early stage and achieved preliminary research results [15,16]. How to further reduce the scatter of fatigue data and improve the accuracy for fatigue life is a significant issue that needs to be solved, which is very important for the application of the method in engineering.…”

Section: Introductionmentioning

confidence: 99%

Optimization of S-N curve fitting based on neighborhood rough set reduction with improved firefly algorithm

Zou

Zhu

2023

Frattura Ed Integrità Strutturale

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S-N curve fatigue samples of titanium alloy welded joints have such a comparatively significant scatter, that results in the issue that the fatigue life prediction accuracy is not optimal. In this work, the titanium alloy welded joints' fatigue data is used as analysis data, and the neighborhood rough set reduction with improved firefly algorithm efficient method of fitting stress-life curves is set forth. The welded joint's fatigue decision system is built with fatigue data. The continuous iteration of the firefly algorithm is used as the search strategy, the neighborhood rough set is adopted to decrease attributes, and the major deciding elements of welded joints' fatigue life is identified. The fatigue characteristic domains are divided based on the neighborhood rough set reduction with improved firefly algorithm's key factor set, and the S-N curves can then be fitted to each domain individually. According to the goodness-of-fit analysis, the proposed approach can improve fatigue life accuracy and reduce sample scattering from fatigue.

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An Optimization of Master S-N Curve Fitting Method Based on Improved Neighborhood Rough Set

Cited by 8 publications

References 35 publications

Attribute reduction algorithm of neighborhood rough set based on supervised granulation and its application

Attribute reduction algorithm of neighborhood rough set based on supervised granulation and its application

A general method for determining probabilistic S-N curve based on static strength data of material

Optimization of S-N curve fitting based on neighborhood rough set reduction with improved firefly algorithm

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