A new approach to fitting the three-parameter Weibull distribution: An application to glass ceramics

Garrido, Arturo; Carretero, Raquel Caro; Jiménez-Octavio, Jesús R.; Carnicero, Alberto; Such, M.

doi:10.1080/03610926.2019.1702698

Cited by 8 publications

(2 citation statements)

References 35 publications

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“…A higher m value suggested a narrower range of fracture stresses and a higher dependability/reliability because the m value represented the degree of variation in the strength of the tested samples [ [272] , [273] , [274] ]. Table 4 lists the typical values of the m for a few materials, such as bulk metallic glasses based on magnesium which were compiled.…”

Section: Overview Of Weibull Analysis (Wa)mentioning

confidence: 99%

Weibull analysis of ceramics and related materials: A review

Osuchukwu,

Salihi,

Ibrahim

et al. 2024

Heliyon

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Section: Overview Of Weibull Analysis (Wa)mentioning

confidence: 99%

Weibull analysis of ceramics and related materials: A review

Osuchukwu,

Salihi,

Ibrahim

et al. 2024

Heliyon

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“…The Weibull distribution has wide applicability in practical engineering, as it can describe and predict data and results by changing its associated parameters (Khalil et al ., 2021; Garrido et al ., 2021; Yang et al ., 2022). Strzelecki (2021) proposed a Weibull distribution model based on the stochastic fatigue limit and fatigue life for specimens with low failure probabilities at different stress levels.…”

Section: Introductionmentioning

confidence: 99%

Research on fatigue life evaluation method of shaft parts based on small sample

Liu

Zhang

Zhu

et al. 2023

IJSI

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PurposeDue to the limitation of experimental conditions and budget, fatigue data of mechanical components are often scarce in practical engineering, which leads to low reliability of fatigue data and reduces the accuracy of fatigue life prediction. Therefore, this study aims to expand the available fatigue data and verify its reliability, enabling the achievement of life prediction analysis at different stress levels.Design/methodology/approachFirst, the principle of fatigue life probability percentiles consistency and the perturbation optimization technique is used to realize the equivalent conversion of small samples fatigue life test data at different stress levels. Meanwhile, checking failure model by fitting the goodness of fit test and proposing a Monte Carlo method based on the data distribution characteristics and a numerical simulation strategy of directional sampling is used to extend equivalent data. Furthermore, the relationship between effective stress and characteristic life is analyzed using a combination of the Weibull distribution and the Stromeyer equation. An iterative sequence is established to obtain predicted life.FindingsThe TC4–DT titanium alloy is selected to assess the accuracy and reliability of the proposed method and the results show that predicted life obtained with the proposed method is within the double dispersion band, indicating high accuracy.Originality/valueThe purpose of this study is to provide a reference for the expansion of small sample fatigue test data, verification of data reliability and prediction of fatigue life data. In addition, the proposed method provides a theoretical basis for engineering applications.

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