Aluminum alloy materials is an important component material in the safe
flight of aircraft. It is very important and necessary to predict the
fatigue crack growth between holes of aviation aluminum alloy materials.
At present, the investigation on the prediction of the cracks between
two holes and multi-holes is a key problem to be solved. Due to the
fatigue crack growth test of aluminum alloy plate with two or three
holes was carried out by MTS fatigue testing machine, the crack length
growth data under different test conditions were obtained. In this
paper, support vector regression (SVR) was used to fit the crack data,
and the parameters of SVR are optimized by grid search algorithm at the
same time. And then the model of SVR to predict the crack length was
established. Discussion on the results show that the prediction model is
effective. Furthermore, the crack growth between three holes were
predicted accurately through the model of the crack law between two
holes under the same load form.