Towards self-driving labs for experimental mechanics: a data-centric approach for fatigue crack analysis in aerospace materials

Abstract 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.