Probabilistic modeling of fatigue crack growth in Ti–6Al–4V

Shen, W.; Soboyejo, Alfred; Soboyejo, Winston O.

doi:10.1016/s0142-1123(01)00045-7

Cited by 18 publications

(22 citation statements)

References 18 publications

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“…In a more recent paper [33], the authors also consider the parameters C and m of Paris' law (2.2) as random variables. From the analysis of experimental data they decide to model either C by a log-normal random variable or m by a normal random variable but they are not random together.…”

Section: Stochastic Models Derived From Deterministic Lawsmentioning

confidence: 99%

Rupture Detection in Fatigue Crack Propagation

Azaïs¹,

Gégout-Petit²,

Greciet³

2018

Statistical Inference for Piecewise‐deterministic Markov Processes

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Section: Stochastic Models Derived From Deterministic Lawsmentioning

confidence: 99%

Rupture Detection in Fatigue Crack Propagation

Azaïs¹,

Gégout-Petit²,

Greciet³

2018

Statistical Inference for Piecewise‐deterministic Markov Processes

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“…30 The distribution of C is taken as lognormal and that of m as normal by most of the researchers. 8,14,31 Prior distribution of ln(C) and m is taken as normal in this paper. Statistical estimation of Paris' constants as reported by Kotulski 32 is used in this work.…”

Section: Statistics Of Paris' Constantsmentioning

confidence: 99%

Fatigue crack growth prediction in nuclear piping using Markov chain Monte Carlo simulation

Rastogi

Ghosh

et al. 2016

Fatigue Fract Eng Mat Struct

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In this paper, we present and demonstrate a methodology to improve probabilistic fatigue crack growth (FCG) predictions by using the concept of Bayesian updating using Markov chain Monte Carlo simulations. The methodology is demonstrated on a cracked pipe undergoing fatigue loading. Initial estimates of the FCG rate are made using the Paris law. The prior probability distributions of the Paris law parameters are taken from the tests on specimen made of the same material as that of pipe. Measured data on crack depth over number of loading cycles are used to update the prior distribution using the Markov chain Monte Carlo. The confidence interval on the predicted FCG rate is also estimated. In actual piping placed in a plant, the measured data can be considered equivalent to the data received from in‐service inspection. It is shown that the proposed methodology improves the fatigue life prediction. The number of observations used for updating is found to leave a significant effect on the accuracy of the updated prediction.

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“…[5][6][7] Several papers deal with statistical and stochastic models for crack propagation. [8][9][10] Nevertheless, due to the complexity of the phenomenon, most models [11][12][13][14][15][16] are only based on the Paris law so that it is difficult to make a comprehensive evaluation of scatter characteristics of sigmoidal crack growth curves. In more recent works, 1,5,7,17,18 a nonlinear fitting is applied to experimental data in the attempt to model the whole sigmoidal shape.…”

Section: Introductionmentioning

confidence: 99%

Sigmoidal crack growth rate curve: statistical modelling and applications

Paolino

Cavatorta

2012

Fatigue Fract Eng Mat Struct

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The present paper proposes a statistical model for describing sigmoidal crack growth rate curves. Major novelties are: a) exploitation of the maximum likelihood principle for obtaining material estimates by pooling together experimental data belonging to the different crack propagation regions; b) a general formulation which allows to adopt different sigmoidal models and any kind of statistical distribution for the model variables; c) fatigue life predictions through numerical integration of analytical functions with no need of Monte Carlo simulations. Experimental data taken from NASGRO database are used to check the validity of the statistical model in estimating material parameters included in the crack growth NASGRO algorithm. Illustrative plots of number of cycles to failure and crack length after a given number of cycles are presented, showing good agreement between the proposed statistical model and NASGRO results.

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Probabilistic modeling of fatigue crack growth in Ti–6Al–4V

Cited by 18 publications

References 18 publications

Rupture Detection in Fatigue Crack Propagation

Rupture Detection in Fatigue Crack Propagation

Fatigue crack growth prediction in nuclear piping using Markov chain Monte Carlo simulation

Sigmoidal crack growth rate curve: statistical modelling and applications

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