Thermal Deformation Measurement of Aerospace Honeycomb Panel Based on Fusion of 3D-Digital Image Correlation and Finite Element Method

Abstract

Aiming to solve the problem of the high-precision deformation measurement of large-scale satellite structures in manufacturing and testing environments, this paper proposes a measurement method based on the idea of fusing actual measurements with finite element analysis. The digital image correlation (DIC) method is used to obtain the high-precision deformation of the honeycomb panel, and the finite element method (FEM) model is introduced to remove the limitations of existing pure measurement methods. Data fusion based on a machine learning neural network is proposed to fuse high-precision deformation and physical information such as temperature to conduct multi-level training on FEM parameters. Through an interpolation of the analysis and calculation results after training, not only can the accuracy of the finite element be improved, but difference and extrapolation of the digital image correlation measurement results can be performed. In the experiments, the satellite on-orbit temperature data are substituted into the modified finite element model. The testing results shows that the prediction accuracy of the model under different temperature loads can be controlled within 10 μm under an 840 mm × 640 mm scale. A high predictive accuracy can be achieved for the revised model for the complete deformation of large structural sections.