journal article·2015-01Open access

Predicting fatigue crack initiation in fibre metal laminates based on metal fatigue test data

S.W.F. Spronk, I. Şen, R.C. Alderliesten

Abstract

Abstract A methodology is presented to predict the cycles to crack initiation in a notched fibre metal laminate subjected to cyclic loading. The methodology contains four steps. First, the far-field metal layer stress cycle is obtained using classical laminate theory. Second, the peak stress cycle is estimated from a combination of a handbook solution for the stress concentration factor in a finite isotropic plate, and analytical solutions for the stress concentration for equal situations in infinitely large plates. The third step is to adapt the amplitude of the peak stress cycle to the characteristics of S–N data for monolithic material from the literature to allow for the cycles to initiation to be read from the S–N curve for each metal layer. In contrast to what can be found hitherto in the literature about predicting the cycles to fatigue crack initiation in fibre metal laminates, the authors of this paper leave no obscurities but rather attempt to bring understanding of the complete path from situation to prediction. Test results from the literature for Glare 4B-3/2-0.3 have been replicated using the aforementioned methodology. It is shown that it can accurately predict the number of cycles to crack initiation, although the S–N data that is used for the predictions dictates the obtained accuracy. The closer the stress cycle value of the S–N data is to the value of the case analysed, the higher the accuracy obtained. Such a trend was not observed for the stress concentration factor of the S–N curves used, although a choice for S–N data with a different stress concentration factor can cause a significant change in precision. The method is also shown to work for several other fibre metal laminates.
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Research context

OpenAlex topics: Mechanical Behavior of Composites · Fatigue and fracture mechanics · Electronic Packaging and Soldering Technologies

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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