A four-parameters model for fatigue crack growth data analysis | Zendy

Marzio Grasso | Zendy; F. Penta | Zendy; P. Pinto | Zendy; Giovanni Pio Pucillo | Zendy

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A four-parameters model for fatigue crack growth data analysis

Author(s) -

Marzio Grasso,

F. Penta,

P. Pinto,

Giovanni Pio Pucillo

Publication year - 2013

Publication title -

frattura ed integrità strutturale

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.368

H-Index - 19

ISSN - 1971-8993

DOI - 10.3221/igf-esis.26.08

Subject(s) - interpolation (computer graphics) , data set , sigmoid function , set (abstract data type) , paris' law , experimental data , computer science , filter (signal processing) , growth model , fracture mechanics , algorithm , mathematics , structural engineering , statistics , engineering , crack closure , artificial intelligence , motion (physics) , artificial neural network , computer vision , programming language , mathematical economics

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

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