Stress intensity factor estimation for unbalanced rotating cracked shafts by artificial neural networks

The knowledge of the stress intensity factor (SIF) values along a crack front is essential to calculate the crack growth rate and the remaining life of a mechanical component. In the case of a rotating shaft, usually it presents disalignments, which modify the SIF data with regard to a balanced one. This paper presents the use of an artificial neural network (ANN) for estimating the SIF at the crack front in an unbalanced shaft under rotating bending, previously, a quasi-static numerical (finite element) model, which simulates a rotating shaft, has been developed to create the training cases for the ANN. The obtained results allow to study the influence of the unbalance of rotating shafts in the crack breathing mechanism and will allow to predict the influence of this behaviour on the values of the SIF and in the propagation of cracks.

of-the-art; surface cracks; round bars; breathing mechanism; bending vibrations; transient-response; jeffcott rotor; fatigue-crack; model; growth

Stress intensity factor estimation for unbalanced rotating cracked shafts by artificial neural networks Articles