An automated high-resolution digital image correlation system for capturing fatigue crack growth mechanisms

The virtual certification of aeroplane structures is becoming increasingly important and requires accurate models to predict service life and damage tolerance. Therefore, fatigue crack growth models that consider the interaction between local mechanical loads and microstructural features of the materials are needed. The crack tip field provides a lot of information, such as the plastic zone, mixed-mode loading conditions, crack closure mechanisms or the stress state. This work presents a fully-automatic robotic test stand to acquire high-resolution digital image correlation data. Intelligent algorithms evaluate a large amount of data, determine the crack tip position and compute the crack tip loadings. The enhanced da/dN-∆KDIC evaluation for 2-mm-thick aluminium AA2024-T3 sheet material explains the ~56 % higher crack propagation rate for the T-L in contrast to the L-T crack growth direction