Statistical Approaches Applied to Fatigue Test Data Analysis

Engler-Pinto, Carlos; Lasecki, John; Frisch, Robert J.; DeJack, Michael A.; Allison, J.

doi:10.4271/2005-01-0802

Cited by 22 publications

(16 citation statements)

References 9 publications

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“…In Engler-Pinto, Jr. et al, [16,35] it was reported that the higher the yield strength, the greater the influence of the water on crack propagation for some cast Al-Si alloys. This is possibly because the higher-yield strength is obtained from peak-aged (T6) heat treatment, and the peak-aged aluminum alloy was found to be more sensitive to environmental effect than the overaged alloy.…”

Section: ½19mentioning

confidence: 99%

Effect of Frequency, Environment, and Temperature on Fatigue Behavior of E319 Cast-Aluminum Alloy: Small-Crack Propagation

Zhu

Jones

Allison

2008

Metall Mater Trans A

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The influence of test frequency on fatigue-crack propagation behavior of small cracks in E319 cast-aluminum alloy was studied using ultrasonic and conventional test techniques. It was observed that fatigue cracks grow faster at 30 Hz than at 20 kHz in air at both 20°C and 250°C. The effect of frequency on the fatigue-crack growth rates was attributed to an environmental effect. For E319 cast-aluminum alloy, fatigue-crack growth rate increases with increasing water exposure (characterized by the ratio of water partial pressure over test frequency, P/f), and this behavior can be estimated using a modified superposition model. The effect of temperature on fatigue-crack growth behavior was primarily attributed to the effect of temperature on Young's modulus and yield strength. The environmental contribution to fatigue-crack growth rates modestly decreases with increasing temperature.

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Section: ½19mentioning

confidence: 99%

Effect of Frequency, Environment, and Temperature on Fatigue Behavior of E319 Cast-Aluminum Alloy: Small-Crack Propagation

Zhu

Jones

Allison

2008

Metall Mater Trans A

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“…Figures 7 and 8 show that neither method has a clear advantage in terms of the relative standard errors of the parameter estimates. This is rather surprising because method B might be thought to use more information, as Engler-Pinto et al 9 suggested. It appears that the patterns in the mean relative errors (Figures 2-4) can largely be explained in terms of differences in bias rather than differences in the amount of variation, and we therefore refer to the relative accuracy of the two methods rather than their relative efficiency.…”

Section: Resultsmentioning

confidence: 84%

“…We illustrate the method using two sets of data. We address the speculation of Engler-Pinto et al 9 and investigate, using simulation, whether analysis in the life domain is more efficient than analysis in the strength domain. This study focuses on ordinary staircase testing and does not compare alternative life regression models, such as the RFL model.…”

Section: Introductionmentioning

confidence: 99%

“…They found difficulties in fitting the RFL model, possibly because the nature of staircase testing implies that the data set typically covers a rather narrow range of applied stress around the median fatigue strength of the specimens tested. Engler-Pinto et al 9 suggested extending the conventional staircase test with a series of 10-15 tests at increasing stress amplitudes, allowing them to fit the RFL model in the life domain and hence estimate the fatigue strength distribution. They speculated that life regression may be more effective than direct analysis in the strength domain because it takes additional information into account, namely the lives of the failed specimens.…”

Section: Introductionmentioning

confidence: 99%

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A comparison of two methods of analysing staircase fatigue test data

Grove

Campean

2007

Quality & Reliability Eng

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When the fatigue strength of components or material is tested by the staircase or up-and-down method, the data can be analysed in either the fatigue strength domain, which is the most popular approach, or the life domain, based on life regression models. In this paper we exploit a simple relationship between families of models in the two domains to obtain an estimate of the distribution of fatigue strength directly from a fitted life regression model. We use simulation to compare the two approaches, concluding that for estimating important features of the fatigue strength distribution the method of maximum likelihood is more effective if it is applied in the life domain. In particular, it is more robust to the choice of x h , the step height of the staircase, provided x h is in the range r X -2r X , where r X is the standard deviation of the log-strengths. Most of our results are insensitive to the value of the initial stress amplitude.

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“…Together with testing strategy, data analysis is crucial for deriving this information with confidence. In the following sections, a new model is presented and compared with 3 existing ones: -Extended Stair-Case model (ESC) [1], in which all data (broken and run-outs) are treated as censored (left and right, respectively) and the chosen strength distribution is fitted using maximum likelihood. Normal, Log-normal and 2 and 3 parameter Weibull distributions were considered; -Castillo Canteli model [2] defines an equivalent variable v = f (S, N ) which follows a 3-parameter Weibull distribution; -Random Fatigue Limit model (RFL) [3] is a pretty sophisticated model in which both the distribution in strength and endurance can be log-normal or 2-p Weibull, and the distribution in endurance is heteroschedastic.…”

Section: Introductionmentioning

confidence: 99%

Bi-conditional probabilistic fatigue stress-based curve definition and comparison with other models

Cova

Nanni

Tovo

2014

MATEC Web of Conferences

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Abstract. The definition of the relationship between probability, fatigue stress and cycles to failure is of great importance, especially in applications which requires very low probability of failure (e.g. Pf=0.1%). In this paper a new formulation is presented, which allows to separately consider the probability of the endurance and the probability of existence of the initiating defect. This approach is then compared to a number of known models. For this purpose, the proprietary results of 8 fatigue test sets, each with at least 24 data points, have been analyzed. A ranking of goodness-of-fit based on the Relative Likelihood can be used to choose the best distribution within each model, but unfortunately cannot be used to compare different models. The conclusions were: 1-the better description of the HCF strength, in terms of performance and robustness, were obtained by the 2-p Weibull distribution; 2-the choice of the model has a great influence in the estimate of the low probability quantiles, but it still subjective and a definitive answer cannot be given after this benchmark; 3-the strength of the proposed model is its flexibility.

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Statistical Approaches Applied to Fatigue Test Data Analysis

Cited by 22 publications

References 9 publications

Effect of Frequency, Environment, and Temperature on Fatigue Behavior of E319 Cast-Aluminum Alloy: Small-Crack Propagation

Effect of Frequency, Environment, and Temperature on Fatigue Behavior of E319 Cast-Aluminum Alloy: Small-Crack Propagation

A comparison of two methods of analysing staircase fatigue test data

Bi-conditional probabilistic fatigue stress-based curve definition and comparison with other models

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