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.

The increasing use of lightweight, large compliant thin-walled structures (LCTS) made of carbon-fiber-reinforced polymer (CFRP) in aircraft fuselage assemblies contributes significantly to fuel efficiency and emission reduction. However, the low bending stiffness and large dimensions of these components make them highly susceptible to elastic deformation under gravity, fixture contact forces, and handling loads, so the geometry observed during manufacturing, inspection, and assembly does not represent the intrinsic, stress-free shape of the component. Because this gravity-free shape cannot be measured directly, it must instead be approximated from measurements taken under known loading conditions, combined with a numerical model of the component's structural response -- a separation that is required for reliable geometric inspection, digital assembly simulation, variation analysis, and fixture design. Minimal-contact supports commonly used for this purpose cause excessive deflection in structures of this size, while conventional multi-point supports leave the force distribution at each support point unknown, introducing ambiguity into the gravity compensation. This work addresses both limitations with a smart fixture system whose instrumented actuators render the imposed displacements and support states fully known, enabling a controlled, over-constrained support condition without loss of traceability of the applied loads. Surface measurements acquired on this fixture are combined with finite element simulations of the same loading configurations to approximate the gravity-free shape of large compliant CFRP structures, isolating manufacturing deviations from gravity-induced elastic deformation and yielding a gravity-free description of the component suitable for downstream geometric evaluation.