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M. Grasso et alii, Frattura ed Integrità Strutturale, 26 (2013) 69-79; DOI: 10.3221/IGF-ESIS.26.08 69 A four-parameters model for fatigue crack growth data analysis M. Grasso, F. Penta, P. Pinto, G.P. Pucillo Università di Napoli Federico II, Dipartimento di Ingegneria Industriale, P.le V. Tecchio 80 – 80125 Napoli, Italy marzio.grasso@unina.it A BSTRACT . A four-parameters model for interpolation of fatigue crack growth data is presented. It has been validated by means of both data produced by the Authors and data collected from Literature. The proposed model is an enhanced version of a three-parameters model already discussed in a previous work that has been suitably modified in order to overcome some drawbacks raised when applied to a quite wider experimental data set. Results of validation study have also revealed that the new model, besides interpolating accurately crack growth data, allows to identify the presence of anomalies in the data sets. For this reason, by a suitable filter to be chosen depending on the size and number of anomalies, it can be used to remove them and obtain sigmoidal crack propagation curves smoother than those obtained when the current analysis techniques are used. In the end, possible model parameters correlations are analysed. K EYWORDS . Fatigue crack propagation; Crack growth data analysis; Correlation model; Fatigue damage; Fatigue. I NTRODUCTION t is well known that the assessment of fatigue damage by means of phenomenological models is closely linked to the procedure and accuracy with which experimental raw crack growth data are analysed. Very often they are discontinuous and naturally irregular and their analysis has to be made on different levels to reach the model formulation or, at least, the canonical graphical correlation between the crack growth rates and the stress intensity factor range, which is the parameter controlling the phenomenon. Actually, when experimental results are analysed, in addition to the normal scatter due to both the phenomenon random character and measurement uncertainty, some further anomalies are usually observed in the homogeneous sequences of data points of the crack growth curves: significant deviations, apparently inconsistent and absolutely uncontrollable, of some data from the trend followed by all the remaining experimental points of the same curve. They are probably due to the random evolution, in the infinite points of the crack front, of the local combinations of stress states and material strengths. Thus, pursuing an improvement in the analysis of crack growth data would mean to formulate a correlation model able to reproduce more accurately the aforementioned trend and not only simply and solely improve the data points interpolation by new averaging techniques and alternative fitting methods. The Standard procedures currently in use to carry out both fatigue crack propagation tests and data analysis do not guarantee that the anomalies in acquired data are absent and do not provide a mean nor a criterion to filter them [1]. The main analytical models found in Literature [2-8], based on polynomial or exponential interpolation formula, do not seem having solved completely the problem. Consequently, to improve the quality of raw crack propagation data analysis we propose an interpretative model whose validity has been checked with a very wide set of crack growth data. A similar attempt, reported in [9] and concerning a three parameters model, has been successfully tested using only experimental data produced by Ghonem and Dore [10]. However, this model revealed some limitations when used to interpolate both I