Deep learning method for analysis and segmentation of fatigue damage in X-ray computed tomography data for fiber-reinforced polymers

Abstract

Fiber-reinforced polymers (FRP) are becoming increasingly widespread in safety-critical components, e.g., in rotor blades of wind turbines, due to their excellent lightweight potential. In such applications mechanical, cyclic loads occur resulting in fatigue, which is characterized by an accumulation of damage. X-ray micro computed tomography scans are used to investigate different damage states in FRP. The detection rate and robustness against human bias are improved over the frequently used threshold algorithm by deep learning. A convolutional neuronal network (CNN) segmented the damage mechanisms. Thereby, the damage evolution of interfiber failure, matrix cracks, and delamination in various levels of stiffness reduction is observed in more detail. The CNN is used to generate quantitative mechanism differentiated damage analysis, with which the layer-dependent damage evolution is observed. That includes a high dependency of the stacking sequence for the intralaminar and interlaminar development of the mentioned damage mechanisms.