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THE RANDOMNESS OF FATIGUE CRACK GROWTH UNDER CONSTANT‐AMPLITUDE LOADS
Author(s) -
Lapetra C.,
Mayo J.,
Dominguez J.
Publication year - 1996
Publication title -
fatigue and fracture of engineering materials and structures
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.887
H-Index - 84
eISSN - 1460-2695
pISSN - 8756-758X
DOI - 10.1111/j.1460-2695.1996.tb00995.x
Subject(s) - randomness , paris' law , amplitude , constant (computer programming) , aluminium , materials science , dispersion (optics) , growth rate , stochastic process , mathematics , structural engineering , mechanics , fracture mechanics , statistics , engineering , crack closure , composite material , physics , computer science , geometry , quantum mechanics , optics , programming language
— An analysis of fatigue crack growth from an statistical point of view has been carried out. Eighteen pre‐cracked specimens obtained from the same sheet of aluminium alloy were subjected to identical load and environmental conditions. Tests were conducted under constant‐amplitude loads in order to isolate the effect of material randomness. The experimental results exhibited two different types of dispersion: one showed as a change in the mean growth rate between tests and the other as nonuniform growth in the same specimen. At a subsequent stage, the statistical distribution of the experimental results was studied and a theoretical model was developed to account for the growth pattern observed. The proposed model uses a growth law comprising random parameters to account for the low‐frequency component (slow changes). The comparison of the performance of two different laws, viz. the Paris‐Erdogan law and the cubic law, is presented. Both were tested on the assumption of randomness in two of their fitting parameters. In addition to the above‐mentioned law, the model uses a stochastic log‐normal process to model the high‐frequency component (rapid changes). The parameters for this process were determined by time series analysis of fatigue crack growth rate data.