چکیده
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This study aims to survey the effects of crack
length on the natural frequencies and mode shapes of
adhesively bonded double-strap joints (DSJs) in the models
with different adherend thicknesses and different adhesive
ductility. Hence, the results of these analyses are used as a
method of crack detection inside the adhesive layer. For
this purpose, a rich database of results for DSJ models with
cracks in length of 0 B lC B 10 mm were used for the aim
of training the artificial neural network (ANN) algorithms.
Subsequently, the results obtained from ANN models can
be used to estimate the existence of crack and its length.
The results show that the natural frequencies of the models
with different adherend thicknesses and different adhesive
ductility follow a logical trend, by which detection of
cracks is possible for a wide range of geometries and
material properties, using ANN analysis. By contrast, the
results show that mode shapes are not affected by cracks.
Therefore, the mode shapes are not useful characteristics
for judgments.
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