Abstract
A three-parameters model for the interpolation of fatigue crack propagation data is proposed. It has been validated by a Literature data set obtained by testing 180 M(T) specimens under three different loading levels. In details, it is highlighted that the results of the analysis carried out by means of the proposed model are more smooth and clear than those obtainable using other methods or models. Also, the parameters of the model have been computed and some peculiarities have been picked out.
Highlights
The assessment of the fatigue damage by means of phenomenological models notoriously is closely linked to the analysis of experimental results obtained from standard specimens of a given material tested with suitably chosen loading programs
The crack growth rate can be directly evaluated in the range [N0-Nf] by means of the previous expression without amplify the irregularities and/or anomalies of the raw data and without losing information in the initial and final part of the aforementioned range, as it occurs with other procedures as those suggested by ASTM Standards
A n analytical model to interpolate the experimental crack growth curves, that is alternative to the methods proposed by the ASTM 647-95a Standard, has been derived analysing the data of 180 fatigue crack propagation tests carried out by Ghonem and Dore
Summary
The assessment of the fatigue damage by means of phenomenological models notoriously is closely linked to the analysis of experimental results obtained from standard specimens of a given material tested with suitably chosen loading programs. It is well known that crack growth data have stochastic nature, as the phenomenon which generates them, due to, essentially, the random evolution of the local combinations, at each point of the crack front, of the induced stress state and material properties For this reason, in order to identify a deterministic law describing the phenomenon, that is able to represent the common data trend independently of the global scatter or the position of the single data point, data have to be elaborated by means of a best-fit method after having selected an analytical model. The most used methods for crack growth testing and data analysis are those suggested by ASTM Standard [2], which, concerning the data analysis, proposes two different approaches: the Secant Method and the Incremental Polynomial Method Since both methods are based on local interpolation of experimental data, irregularities and/or anomalies in the data distribution, for the first method, and the number of data points chosen for the best-fit parabola, for the second one, affect significantly the results. Our interest in facing this problem, in order to contribute, if possible, to improve on the quality of raw crack propagation data analysis by formulating an interpretative model for the whole lives field of the acquired data
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