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A unified statistical methodology for modeling fatigue by Enrique Castillo

By Enrique Castillo

The booklet offers a unified probabilistic method of assessment of fatigue harm, together with all steps to be undefined, beginning with fatigue checking out making plans, fabric characterization via lab experiments, version choice, parameter estimation and harm review and existence prediction linked to a given tension or pressure background. It additionally treats laptop courses to do all of the above.

In addition, a serious review of current types in keeping with the hot proposed replacement version is among the major goals of the booklet, attempting to switch the minds of engineers desirous about layout jobs.

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4 Model for constant stress range and level . . 1 Derivation of the model . . . . . . . 2 Parameter estimation . . . . . . . . 3 Alternative methods for dealing with run-outs . 5 Model for varying range and given stress level . 1 Derivation of the model . . . . . . . 2 Some weaknesses of the proposed model . . . 3 Parameter estimation . . . . . . . . 4 Use of the model in practice . . . . . . 5 Example of application . . . . . . . 6 Model for varying stress range and level .

1 Introduction In the evaluation and prediction of the fatigue lifetime of machines and structures the role of mathematical and statistical models is crucial, due to the high complexity of the fatigue problem, in which the consideration of the stress range, stress level and the size effect, together with an efficient estimation of the corresponding parameters represents one of the most difficult and attracting challenges, which have not yet been satisfactorily solved. As a consequence, reliable failure prediction, engineering design and risk analysis in fatigue are not possible without the help of statistical models.

2(b) are obtained, from which similar conclusions can be drawn, that is, the fatigue lifetime depends on the stress level selected (in this case σm ). Again, the percentile lines appear with similar trends as the previous ones, but with a different inclination. This suggests that the same parametric family of percentile curves could be used to represent all these cases, but with different parameter values, which obviously will depend on the constant values of σM or σm , respectively. 2: (a) Set of experiments run for two constant values of σM , and (b) set of experiments run for two constant values of σm .

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