Theoretical framework for estimating a product's reliability using a variable‐amplitude loading spectrum and a stress‐based approach

Portalés, R.M.; Sania, M. del Mar Bochons; Klemenc, Jernej

doi:10.1111/ffe.12804

Cited by 4 publications

(4 citation statements)

References 19 publications

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“…The two conditions in Eqs. 5and (6) limit the transition gradient between the small and large values of the shape parameter β relative to the trend-curve slope ratio (b+d)/b. The knee-point stress S KP is calculated as a cross-section of the two asymptotes of Eq.…”

Section: Variable-scatter Model For the S-n Curvementioning

confidence: 99%

“…For this reason, a S-N fatigue-life curve is normally used to predict the fatigue life of such structures. In the past it was presumed that a fatigue-life limit S a;FL exists at a certain number of load-cycles-to-failure N FL , e.g., for structural steels the fatigue-life limit should occur between 2×10 6 [8] and 10 8 [9] loadcycles-to-failure, see Fig. 1.…”

Section: Introductionmentioning

confidence: 99%

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An Improved Model for Predicting the Scattered S-N Curves

Klemenc¹,

Podgornik²

2019

SV-JME

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In this article an improved neural network model is presented that allows us to predict the scattered S-N curves. The model is capable of predicting the S-N curve in its high-cycle and very-high-cycle fatigue domains by considering also the increased scatter of the fatigue-life data below the knee point of the S-N curve. The scatter of the fatigue-life data for an arbitrary amplitude-stress level is modelled with a two-parametric Weibull's probability density function, the parameters of which are varied as a function of the amplitude-stress level. The parameters of the S-N curve trend and its scatter distribution are not fixed, but depend on the parameters of the production process via a serial-hybrid neural network. The article presents the theoretical background and the application in the case of real experimental fatigue data for 51CrV4 spring steel manufactured with two different manufacturing technologies and two different heat treatments.

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Section: Variable-scatter Model For the S-n Curvementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Improved Model for Predicting the Scattered S-N Curves

Klemenc¹,

Podgornik²

2019

SV-JME

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“…The fatigue measure data (Li and Lu, 2007;Wormsen et al, 2015) of AISI 8630 M (5) steel and LY12CZ aluminum alloy sheets are analyzed by the error ellipse model; it can reduce sampling errors and obtain reasonable S-N curves. Scholars have studied the estimation of fatigue reliability (Liu et al, 2020;Portalés et al, 2018;Wang et al, 2018;Wang et al, 2019). Luca and Alain (2017) provided a new advanced probability method based on the linear cumulative damage rule, which can be used to reliably estimate the S-N curve of fatigue characteristics of welded joints.…”

Section: Introductionmentioning

confidence: 99%

An improved bootstrap method introducing error ellipse for numerical analysis of fatigue life parameters

Liu

Fang

et al. 2020

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Purpose The purpose of this paper is to introduce error ellipse into the bootstrap method to improve the reliability of small samples and the credibility of the S-N curve. Design/methodology/approach Based on the bootstrap method and the reliability of the original samples, two error ellipse models are proposed. The error ellipse model reasonably predicts that the discrete law of expanded virtual samples obeys two-dimensional normal distribution. Findings By comparing parameters obtained by the bootstrap method, improved bootstrap method (normal distribution) and error ellipse methods, it is found that the error ellipse method achieves the expansion of sampling range and shortens the confidence interval, which improves the accuracy of the estimation of parameters with small samples. Through case analysis, it is proved that the tangent error ellipse method is feasible, and the series of S-N curves is reasonable by the tangent error ellipse method. Originality/value The error ellipse methods can lay a technical foundation for life prediction of products and have a progressive significance for the quality evaluation of products.

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“…Therefore, the scattered fatigue-life curve that is used to estimate the specimen's fatigue-life curve and its scatter is modelled with three parameters: a 0 , a 1 and β. This approach was already successfully applied previously to model the fatigue-life properties of different metallic materials and structures [25][26][27][28][29][30][31][32][33].…”

mentioning

confidence: 99%

Statistical Modelling of the Fatigue Bending Strength of Norway Spruce Wood

Klemenc

Fajdiga

2022

Materials

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When wood is used as a structural material, the fact that it is a highly inhomogeneous material, which significantly affects its static and fatigue properties, presents a major challenge to engineers. In this paper, a novel approach to modelling the fatigue-life properties of wood is presented. In the model, the common inverse-power-law relationship between the structural amplitude loads and the corresponding number of load cycles to failure is augmented with the influence of the wood’s mass density, the loading direction and the processing lot. The model is based on the two-parametric conditional Weibull’s probability density function with a constant shape parameter and a scale parameter that is a function of the previously mentioned parameters. The proposed approach was validated using the example of experimental static and fatigue-strength data from spruce beams. It turned out that the newly presented model is capable of adequately replicating the spruce’s S-N curves with a scatter, despite the relatively scarce amount of experimental data, which came from different production lots that were loaded in different directions and had a significant variation in density. Based on the experimental data, the statistical model predicts that the lower density wood has better fatigue strength.

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Theoretical framework for estimating a product's reliability using a variable‐amplitude loading spectrum and a stress‐based approach

Cited by 4 publications

References 19 publications

An Improved Model for Predicting the Scattered S-N Curves

An Improved Model for Predicting the Scattered S-N Curves

An improved bootstrap method introducing error ellipse for numerical analysis of fatigue life parameters

Statistical Modelling of the Fatigue Bending Strength of Norway Spruce Wood

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