The aerospace industry’s drive to improve fuel efficiency and reduce emissions has led to the widespread adoption of lightweight, thin-walled structures in aircraft assemblies. However, these flexible structures are susceptible to deformation under gravitational and external forces, causing assembly deviations and potential misalignment. To counteract the misalignment caused by part deformation in joining processes, shims are specifically designed to fill the gaps between components. Yet, despite advances in metrology, the process of shimming remains largely manual, since each assembly’s unique gap profile requires physical measurement, fitting, and adjustment, which hinders full automation. A promising solution is the use of a digital twin to simulate and predict part deformation during the joining process. The predicted deformation resulting from the digitalization of the compliant structures to be joined can be used for designing shims in a virtual assembly environment or as input for adaptive tooling systems specifically designed to compensate for the deformation. This work introduces a concept for a test cell equipped with a large-scale metrology system to capture the data needed for constructing a digital twin of manufactured large, compliant, thin-walled structures. Corresponding data pipelines, designed to meet FAIR data principles, combine a time-series database for continuous measurement data with a non-relational database for storing measurement metadata. Preliminary results demonstrate the test cell’s ability to provide the data required for applying inverse form finding methods to approximate the LCTS gravity-free state shape and for training data-driven physics informed models for the prediction of the deformation behavior. These findings lay the groundwork for future digital twin development and offer insights to improve alignment precision in the assembly of large, compliant, thin-walled structures.