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Background Digital image correlation (DIC) with microscopes has become an important experimental tool in fracture mechanics to study local effects such as the plastic zone, crack closure, crack deflection or crack branching. High-resolution light microscopes provide 2D images but the field of view is limited to a small area and very sensitive to its alignment. A flexible positioning system is therefore needed to collect such DIC data during the entire fatigue crack growth process. Objective We present in our paper a new experimental setup for local high-resolution 2D DIC measurements at any location and at any time during fatigue crack growth experiments with a non-fixed DIC microscopy system. Methods We use a robot to move the 2D DIC microscope to any location on the surface of the specimen. Optical and tactile methods automatically adjust the system and ensure highest image quality as well as accurate alignment. In addition, an advanced repositioning method reduces out-of-plane motion effects. Results The robot is able to achieve a repositioning accuracy of less than 0.06 mm in vector space, resulting in very low Von Mises strain scattering of 0.07 to 0.09% in the DIC evaluation. The system minimizes systematic errors caused by translation and rotational deviations. Effects such as crack deflection, crack branching or the plastic zone of a fatigue crack can be investigated with a field of view of 10.2 x 6.4 mm2. Conclusions The robot supported DIC system generates up to 8000 high-quality DIC images in an experiment that enables the application of digital evaluation algorithms. Redundant information create confidence in the results as all revealed effects are comprehensible. This increases the information content of a single fatigue crack growth test and accelerates knowledge generation.

Today’s societal challenges require rapid response and smart materials solutions in almost all technical areas. Driven by these needs, data-driven research has emerged as an enabler for faster innovation cycles. In fields such as chemistry, materials science and life sciences, autonomous data generation and processing is already accelerating knowledge discovery. In contrast, in experimental mechanics, complex investigations like studying fatigue crack growth in structural materials have traditionally adhered to standardized procedures with limited adoption of the digital transformation. In this work, we present a novel infrastructure for data-centric experimental mechanics. The setup is demonstrated using a complex fatigue crack growth experiment for aerospace materials. Our methodology incorporates an open-source Python library that complements a multi-scale digital image correlation and robot-assisted test rig. Our novel approach significantly increases the information-to-cost ratio of fatigue crack growth experiments in aerospace materials compared to traditional experiments. Thus, serves as a catalyst for discovering new scientific knowledge and contributes to the data-driven acceleration of the deployment of new applications in the field of structural materials and structures.