As the key load-bearing component of spacecraft, the strength evaluation of stiffened plate structures faces two challenges. On the one hand, the simulation results are sometimes inaccurate, due to the simplification of the true loading conditions and modeling details. On the other hand, data from the sensors cannot provide the full-field strength information of the structure, which may result in the misjudgment of the structural state. To this end, a digital twin modeling method of multi-source data fusion based on transfer learning is proposed in this paper. In transfer learning, simulation data and sensor data are utilized as the source dataset and the target dataset, respectively. First, a pre-trained deep neural network (DNN) model is established based on the source dataset. Then, the pre-trained DNN model is fine-tuned based on the target dataset using a lower learning rate and fewer training epochs. Finally, a digital twin model can be built, which is capable of visualizing the full-field strength information of the stiffened plate structure. To verify the effectiveness of the proposed method, an experimental study on a hierarchical stiffened plate is carried out. Compared with the traditional data fusion method, the results verify the high prediction accuracy and efficiency of the proposed method, demonstrating its potential for the strength health monitoring of spacecraft in orbit.
In the composite structure of spacecraft, the honeycomb sandwich structure is the basic bearing component used to bear and transmit loads. To explore the influencing factors on the bearing capacity of honeycomb sandwich structures, this study combines local tests and speckle measurement systems to conduct tensile tests on 10 test specimens with different parameters. Firstly, a comprehensive assessment was conducted on the accuracy of the loading and measurement system, the rationality of the testing method, and the mechanical properties of the test piece. It was found that the maximum measurement error of the speckle measurement system did not exceed 0.01 mm, and the differences between the yield load and failure load measured using different inner diameters of the compression ring were 0.15% and 3.84%, respectively. This indicates that the measurement system is accurate and that the influence of the inner diameter of the compression ring can be ignored. Moreover, it was found that considering the accuracy retention ability of the structure under load, the allowable load of the embedded parts is about 90% of the yield load. Finally, the data of specimens with different parameters were compared and it was found that the strength of the honeycomb sandwich structure is directly proportional to the thickness of the skin, the density of the honeycomb core cells, and the size of the embedded parts.
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