Experiment-based calibration of a digital twin for composite panel testing using standard signals

Abstract

Accurate assessment of composite structures depends on how experimental measurements are used to calibrate and validate numerical models. While nominal finite element models generally assume idealized conditions, actual manufactured components and test setups introduce notable uncertainties and require a tailored numerical representation. This paper presents a measurement-centric calibration framework that transforms a nominal finite element model into a specimen-specific Digital Twin using only standard test signals. The scalable methodology leverages nonlinear least squares optimization which is first demonstrated and verified on an analytical beam example, allowing for detailed investigation of convergence behavior as well as the improvements by parameter transformation and by Jacobian recalculation. Emphasis is placed on the linearization of model parameters prior to optimization and the role of measurement availability. This process significantly enhances calibration stability and accuracy. The method is then applied to a physical compression test of a stiffened CFRP panel at a subcomponent scale. Numerical studies are conducted to evaluate the sensitivity of the calibration process to parameter selection and validation metrics. Finally, a calibrated Digital Twin is created using experimental strain data and validated through the DIC-measured deflection field, demonstrating the model’s ability to more accurately replicate the mechanical response of an individual composite panel.